{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T17:02:54Z","timestamp":1793120574680,"version":"4.5.0"},"reference-count":318,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Medicinal Chemistry"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.ejmech.2024.117164","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T07:34:24Z","timestamp":1734075264000},"page":"117164","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":45,"special_numbering":"C","title":["Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine"],"prefix":"10.1016","volume":"284","author":[{"given":"Subiya","family":"Ambreen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Umar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaisha","family":"Noor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Himangini","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4889-3453","authenticated-orcid":false,"given":"Ruhi","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejmech.2024.117164_bib1","article-title":"Advances and perspectives in applying deep learning for drug design and discovery","volume":"AI 6","author":"Lipinski","year":"2019","journal-title":"Front. Robot."},{"key":"10.1016\/j.ejmech.2024.117164_bib2","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"Hamet","year":"2017","journal-title":"Metabolism"},{"key":"10.1016\/j.ejmech.2024.117164_bib3","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.addr.2019.05.001","article-title":"The significance of artificial intelligence in drug delivery system design","volume":"151\u2013152","author":"Hassanzadeh","year":"2019","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib4","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.2174\/138161207780765954","article-title":"Artificial intelligence approaches for rational drug design and discovery","volume":"13","author":"Duch","year":"2007","journal-title":"Curr. Pharmaceut. Des."},{"key":"10.1016\/j.ejmech.2024.117164_bib5","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/s41598-017-18564-8","article-title":"An application of machine learning to haematological diagnosis","volume":"8","author":"Gun\u010dar","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib6","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1021\/acsmedchemlett.8b00500","article-title":"Artificial intelligence in drug design\u2014the storm before the calm?","volume":"9","author":"Jordan","year":"2018","journal-title":"ACS Med. Chem. Lett."},{"key":"10.1016\/j.ejmech.2024.117164_bib7","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1007\/s11427-018-9342-2","article-title":"Artificial intelligence in drug design","volume":"61","author":"Zhong","year":"2018","journal-title":"Sci. China Life Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib8","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial intelligence to deep learning: machine intelligence approach for drug discovery","volume":"25","author":"Gupta","year":"2021","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2024.117164_bib9","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.drudis.2018.11.014","article-title":"Artificial intelligence in drug development: present status and future prospects","volume":"24","author":"Mak","year":"2019","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib10","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.3390\/ijms20112783","article-title":"A structure-based drug discovery paradigm","volume":"20","author":"Batool","year":"2019","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejmech.2019.111579","article-title":"The Mediterranean Diet as source of bioactive compounds with multi-targeting anti-cancer profile","volume":"181","author":"Maruca","year":"2019","journal-title":"Eur. J. Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib12","article-title":"The rise and fall of machine learning methods in biomedical research","volume":"6","author":"Koohy","year":"2012","journal-title":"F1000Research"},{"key":"10.1016\/j.ejmech.2024.117164_bib13","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1186\/s12859-017-1798-2","article-title":"Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma","volume":"18","author":"Young","year":"2017","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib14","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.2174\/09298673113209990001","article-title":"Virtual screening strategies in drug discovery: a critical review","volume":"20","author":"Lavecchia","year":"2013","journal-title":"Curr. Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib15","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.drudis.2015.12.007","article-title":"In silico methods to address polypharmacology: current status, applications and future perspectives","volume":"21","author":"Lavecchia","year":"2016","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib16","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1001\/jamainternmed.2018.3931","article-title":"Estimated costs of pivotal trials for novel therapeutic agents approved by the US food and drug administration, 2015-2016","volume":"178","author":"Moore","year":"2018","journal-title":"JAMA Intern. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib17","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.drudis.2020.10.010","article-title":"Artificial intelligence in drug discovery and development","volume":"26","author":"Paul","year":"2021","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib18","article-title":"Artificial intelligence: a powerful paradigm for scientific research","volume":"2","author":"Xu","year":"2021","journal-title":"Innov"},{"key":"10.1016\/j.ejmech.2024.117164_bib19","doi-asserted-by":"crossref","first-page":"7772","DOI":"10.3390\/app11177772","article-title":"Deep learning for drug discovery: a study of identifying high efficacy drug compounds using a cascade transfer learning approach","volume":"11","author":"Zhuang","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib20","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40360-018-0282-6","article-title":"eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates","volume":"20","author":"Pu","year":"2019","journal-title":"BMC Pharmacol. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib21","author":"Rees"},{"key":"10.1016\/j.ejmech.2024.117164_bib22","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib23","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.dsx.2020.06.068","article-title":"Application of artificial intelligence in COVID-19 drug repurposing","volume":"14","author":"Mohanty","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2023.102771","article-title":"AI for targeted polypharmacology: the next frontier in drug discovery","volume":"84","author":"Cicho\u0144ska","year":"2024","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib25","first-page":"109","article-title":"Artificial intelligence and collusion","volume":"50","author":"Beneke","year":"2019","journal-title":"IIC - Int. Rev. Intellect. Prop. Compet. Law"},{"key":"10.1016\/j.ejmech.2024.117164_bib26","author":"Bielecki"},{"key":"10.1016\/j.ejmech.2024.117164_bib27","series-title":"Futur. Pharm. Prod. Dev. Res.","first-page":"73","article-title":"Artificial intelligence in the pharmaceutical sector: current scene and future prospect","author":"Kalyane","year":"2020"},{"key":"10.1016\/j.ejmech.2024.117164_bib28","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib29","author":"Sen"},{"key":"10.1016\/j.ejmech.2024.117164_bib30","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav. Ther."},{"key":"10.1016\/j.ejmech.2024.117164_bib31","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1111\/insr.12448","article-title":"Supervised machine learning techniques: an overview with applications to banking","volume":"89","author":"Hu","year":"2021","journal-title":"Int. Stat. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib32","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/math9080830","article-title":"K-nearest neighbor learning with graph neural networks","volume":"9","author":"Kang","year":"2021","journal-title":"Mathematics"},{"key":"10.1016\/j.ejmech.2024.117164_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2019.101764","article-title":"Deep supervised learning with mixture of neural networks","volume":"102","author":"Hu","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib34","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.3390\/w11112210","article-title":"Efficient water quality prediction using supervised machine learning","volume":"11","author":"Ahmed","year":"2019","journal-title":"Water"},{"key":"10.1016\/j.ejmech.2024.117164_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-art in artificial neural network applications: a survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"10.1016\/j.ejmech.2024.117164_bib36","doi-asserted-by":"crossref","first-page":"65579","DOI":"10.1109\/ACCESS.2019.2916648","article-title":"Unsupervised machine learning for networking: techniques, applications and research challenges","volume":"7","author":"Usama","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.ejmech.2024.117164_bib37","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","article-title":"K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data","volume":"622","author":"Ikotun","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib38","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neucom.2018.11.007","article-title":"Possibilistic fuzzy clustering with high-density viewpoint","volume":"329","author":"Tang","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.ejmech.2024.117164_bib39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2019.06.064","article-title":"Combining hierarchical clustering approaches using the PCA method","volume":"137","author":"Jafarzadegan","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ejmech.2024.117164_bib40","article-title":"A review of principal component analysis algorithm for dimensionality reduction","volume":"2","author":"Salih Hasan","year":"2021","journal-title":"J. Soft Comput. Data Min."},{"key":"10.1016\/j.ejmech.2024.117164_bib41","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1016\/j.drudis.2018.05.010","article-title":"Machine learning in chemoinformatics and drug discovery","volume":"23","author":"Lo","year":"2018","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114170","article-title":"Deep belief network based intrusion detection techniques: a survey","volume":"167","author":"Sohn","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.ejmech.2024.117164_bib43","first-page":"1140","article-title":"An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices","volume":"25","author":"Idrissi","year":"2022","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib44","series-title":"Mach. Learn.","first-page":"193","article-title":"Autoencoders","author":"Lopez Pinaya","year":"2020"},{"key":"10.1016\/j.ejmech.2024.117164_bib45","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"van Engelen","year":"2020","journal-title":"Mach. Learn."},{"key":"10.1016\/j.ejmech.2024.117164_bib46","author":"Gao"},{"key":"10.1016\/j.ejmech.2024.117164_bib47","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1561\/2200000071","article-title":"An introduction to deep reinforcement learning","volume":"11","author":"Fran\u00e7ois-Lavet","year":"2018","journal-title":"Found. Trends\u00ae Mach. Learn."},{"key":"10.1016\/j.ejmech.2024.117164_bib48","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","article-title":"A decade survey of transfer learning (2010\u20132020)","volume":"1","author":"Niu","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.ejmech.2024.117164_bib49","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1093\/nsr\/nwx105","article-title":"An overview of multi-task learning","volume":"5","author":"Zhang","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.inffus.2020.10.002","article-title":"Bridging deep and multiple kernel learning: a review","volume":"67","author":"Wang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.ejmech.2024.117164_bib51","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","article-title":"A survey on ensemble learning","volume":"14","author":"Dong","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib52","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","article-title":"Compound\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences","volume":"35","author":"Tsubaki","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib53","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/med.21764","article-title":"Artificial intelligence and machine learning\u2010aided drug discovery in central nervous system diseases: state\u2010of\u2010the\u2010arts and future directions","volume":"41","author":"Vatansever","year":"2021","journal-title":"Med. Res. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib54","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.3390\/ijms24032026","article-title":"Artificial intelligence and machine learning technology driven modern drug discovery and development","volume":"24","author":"Sarkar","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib55","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1038\/nrd.2017.111","article-title":"Opportunities and challenges in phenotypic drug discovery: an industry perspective","volume":"16","author":"Moffat","year":"2017","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib56","doi-asserted-by":"crossref","first-page":"35796","DOI":"10.1109\/ACCESS.2024.3373195","article-title":"Explainable artificial intelligence for drug discovery and development: a comprehensive survey","volume":"12","author":"Alizadehsani","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ejmech.2024.117164_bib57","doi-asserted-by":"crossref","first-page":"271","DOI":"10.3390\/ijerph18010271","article-title":"Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges","volume":"18","author":"Lee","year":"2021","journal-title":"Int. J. Environ. Res. Publ. Health"},{"key":"10.1016\/j.ejmech.2024.117164_bib58","doi-asserted-by":"crossref","first-page":"390","DOI":"10.5530\/jyp.2023.15.54","article-title":"Artificial intelligence the futuristic technology in the drug discovery process: a review","volume":"15","author":"Sayam","year":"2023","journal-title":"J. Young Pharm."},{"key":"10.1016\/j.ejmech.2024.117164_bib59","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13073-014-0057-7","article-title":"A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening","volume":"6","author":"Jeon","year":"2014","journal-title":"Genome Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib60","article-title":"Applications of support vector machine (SVM) learning in cancer genomics","volume":"15","year":"2018","journal-title":"Cancer Genomics Proteomics"},{"key":"10.1016\/j.ejmech.2024.117164_bib61","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2018.00242","article-title":"Machine learning on human muscle transcriptomic data for biomarker discovery and tissue-specific drug target identification","volume":"9","author":"Mamoshina","year":"2018","journal-title":"Front. Genet."},{"key":"10.1016\/j.ejmech.2024.117164_bib62","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1002\/prot.25834","article-title":"Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)","volume":"87","author":"Senior","year":"2019","journal-title":"Proteins: Struct., Funct., Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib63","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved protein structure prediction using potentials from deep learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.ejmech.2024.117164_bib64","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1002\/prot.25823","article-title":"Critical assessment of methods of protein structure prediction (CASP)\u2014round XIII","volume":"87","author":"Kryshtafovych","year":"2019","journal-title":"Proteins: Struct., Funct., Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib65","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/d41586-020-03348-4","article-title":"\u2018It will change everything\u2019: DeepMind's AI makes gigantic leap in solving protein structures","volume":"588","author":"Callaway","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.ejmech.2024.117164_bib66","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.csbj.2019.12.011","article-title":"Deep learning methods in protein structure prediction","volume":"18","author":"Torrisi","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.ejmech.2024.117164_bib67","doi-asserted-by":"crossref","first-page":"5634","DOI":"10.1038\/s41596-021-00628-9","article-title":"The trRosetta server for fast and accurate protein structure prediction","volume":"16","author":"Du","year":"2021","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.ejmech.2024.117164_bib68","doi-asserted-by":"crossref","first-page":"i268","DOI":"10.1093\/bioinformatics\/btaa457","article-title":"Geometric potentials from deep learning improve prediction of CDR H3 loop structures","volume":"36","author":"Ruffolo","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib69","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1002\/prot.26186","article-title":"Improving protein tertiary structure prediction by deep learning and distance prediction in <scp>CASP14<\/scp&gt","volume":"90","author":"Liu","year":"2022","journal-title":"Proteins: Struct., Funct., Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib70","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s12859-021-03960-9","article-title":"DeepDist: real-value inter-residue distance prediction with deep residual convolutional network","volume":"22","author":"Wu","year":"2021","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib71","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1002\/prot.25792","article-title":"Deep\u2010learning contact\u2010map guided protein structure prediction in CASP13","volume":"87","author":"Zheng","year":"2019","journal-title":"Proteins: Struct., Funct., Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib72","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1093\/bioinformatics\/btz863","article-title":"DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins","volume":"36","author":"Zhang","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib73","series-title":"ICLR 2019 Conf","article-title":"Learning protein structure with a differentiable simulator","author":"John","year":"2018"},{"key":"10.1016\/j.ejmech.2024.117164_bib74","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.cels.2019.03.006","article-title":"End-to-End differentiable learning of protein structure","volume":"8","author":"AlQuraishi","year":"2019","journal-title":"Cell Syst"},{"key":"10.1016\/j.ejmech.2024.117164_bib75","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2022.102495","article-title":"Protein structure prediction in the deep learning era","volume":"77","author":"Peng","year":"2022","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib76","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s12038-019-9909-z","article-title":"Machine-learning techniques for the prediction of protein\u2013protein interactions","volume":"44","author":"Sarkar","year":"2019","journal-title":"J. Biosci."},{"key":"10.1016\/j.ejmech.2024.117164_bib77","doi-asserted-by":"crossref","first-page":"5316","DOI":"10.1016\/j.csbj.2022.08.070","article-title":"Protein\u2013protein interaction prediction with deep learning: a comprehensive review","volume":"20","author":"Soleymani","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.ejmech.2024.117164_bib78","series-title":"Adv. Neural Inf. Process. Syst.","article-title":"Protein interface prediction using graph convolutional networks","author":"Alex","year":"2017"},{"key":"10.1016\/j.ejmech.2024.117164_bib79","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1016\/j.jmb.2015.07.016","article-title":"Updates to the integrated protein\u2013protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2","volume":"427","author":"Vreven","year":"2015","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib80","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1093\/bioinformatics\/btx585","article-title":"IntPred: a structure-based predictor of protein\u2013protein interaction sites","volume":"34","author":"Northey","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib81","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.jmb.2007.05.022","article-title":"Inference of macromolecular assemblies from crystalline state","volume":"372","author":"Krissinel","year":"2007","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib82","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/1471-2105-7-27","article-title":"NOXclass: prediction of protein-protein interaction types","volume":"7","author":"Zhu","year":"2006","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib83","series-title":"Adv. Neural Inf. Process. Syst.","article-title":"Generative models for graph-based protein design","author":"Ingraham","year":"2019"},{"key":"10.1016\/j.ejmech.2024.117164_bib84","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/S0969-2126(97)00260-8","article-title":"Cath \u2013 a hierarchic classification of protein domain structures","volume":"5","author":"Orengo","year":"1997","journal-title":"Structure"},{"key":"10.1016\/j.ejmech.2024.117164_bib85","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-75467-x","article-title":"Amalgamation of 3D structure and sequence information for protein\u2013protein interaction prediction","volume":"10","author":"Jha","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib86","doi-asserted-by":"crossref","first-page":"4992","DOI":"10.1021\/pr100618t","article-title":"Large-scale prediction of human Protein\u2212Protein interactions from amino acid sequence based on latent topic features","volume":"9","author":"Pan","year":"2010","journal-title":"J. Proteome Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib87","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1186\/s12859-022-04910-9","article-title":"Struct2Graph: a graph attention network for structure based predictions of protein\u2013protein interactions","volume":"23","author":"Baranwal","year":"2022","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib88","doi-asserted-by":"crossref","first-page":"D358","DOI":"10.1093\/nar\/gkt1115","article-title":"The MIntAct project\u2014IntAct as a common curation platform for 11 molecular interaction databases","volume":"42","author":"Orchard","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib89","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib90","doi-asserted-by":"crossref","first-page":"D506","DOI":"10.1093\/nar\/gky1049","article-title":"The UniProt Consortium, UniProt: a worldwide hub of protein knowledge","volume":"47","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib91","doi-asserted-by":"crossref","first-page":"i802","DOI":"10.1093\/bioinformatics\/bty573","article-title":"Predicting protein\u2013protein interactions through sequence-based deep learning","volume":"34","author":"Hashemifar","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib92","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0031826","article-title":"HIPPIE: integrating protein interaction networks with experiment based quality scores","volume":"7","author":"Schaefer","year":"2012","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2024.117164_bib93","doi-asserted-by":"crossref","first-page":"449D","DOI":"10.1093\/nar\/gkh086","article-title":"The database of interacting proteins: 2004 update","volume":"32","author":"Salwinski","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib94","doi-asserted-by":"crossref","first-page":"i305","DOI":"10.1093\/bioinformatics\/btz328","article-title":"Multifaceted protein\u2013protein interaction prediction based on Siamese residual RCNN","volume":"35","author":"Chen","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib95","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1093\/nar\/gkn159","article-title":"Using support vector machine combined with auto covariance to predict protein\u2013protein interactions from protein sequences","volume":"36","author":"Guo","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib96","doi-asserted-by":"crossref","first-page":"D362","DOI":"10.1093\/nar\/gkw937","article-title":"The STRING database in 2017: quality-controlled protein\u2013protein association networks, made broadly accessible","volume":"45","author":"Szklarczyk","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib97","doi-asserted-by":"crossref","first-page":"2600","DOI":"10.1093\/bioinformatics\/bts489","article-title":"SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models","volume":"28","author":"Moal","year":"2012","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib98","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1186\/s12859-020-03646-8","article-title":"Graph-based prediction of Protein-protein interactions with attributed signed graph embedding","volume":"21","author":"Yang","year":"2020","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib99","first-page":"1","article-title":"ACT-SVM: prediction of protein-protein interactions based on support vector basis model","volume":"2020","author":"Ma","year":"2020","journal-title":"Sci. Program."},{"key":"10.1016\/j.ejmech.2024.117164_bib100","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1093\/bioinformatics\/btab737","article-title":"DeepTrio: a ternary prediction system for protein\u2013protein interaction using mask multiple parallel convolutional neural networks","volume":"38","author":"Hu","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib101","doi-asserted-by":"crossref","first-page":"D529","DOI":"10.1093\/nar\/gky1079","article-title":"The BioGRID interaction database: 2019 update","volume":"47","author":"Oughtred","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib102","author":"Ma"},{"key":"10.1016\/j.ejmech.2024.117164_bib103","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2020.00018","article-title":"FCTP-WSRC: protein\u2013protein interactions prediction via weighted sparse representation based classification","volume":"11","author":"Kong","year":"2020","journal-title":"Front. Genet."},{"key":"10.1016\/j.ejmech.2024.117164_bib104","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1186\/s12859-021-04369-0","article-title":"Protein\u2013protein interaction prediction based on ordinal regression and recurrent convolutional neural networks","volume":"22","author":"Xu","year":"2021","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib105","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/med.21764","article-title":"Artificial intelligence and machine learning\u2010aided drug discovery in central nervous system diseases: state\u2010of\u2010the\u2010arts and future directions","volume":"41","author":"Vatansever","year":"2021","journal-title":"Med. Res. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib106","doi-asserted-by":"crossref","first-page":"8461","DOI":"10.1021\/jm101020z","article-title":"Quo vadis, virtual screening? A comprehensive survey of prospective applications","volume":"53","author":"Ripphausen","year":"2010","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib107","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1517\/17460441.3.8.841","article-title":"What has virtual screening ever done for drug discovery?","volume":"3","author":"Clark","year":"2008","journal-title":"Expet Opin. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib108","doi-asserted-by":"crossref","first-page":"2641","DOI":"10.4155\/fmc-2018-0076","article-title":"A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data","volume":"10","author":"Banegas-Luna","year":"2018","journal-title":"Future Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib109","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1038\/nrd941","article-title":"Integration of virtual and high-throughput screening","volume":"1","author":"Bajorath","year":"2002","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib110","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1146\/annurev-pharmtox-051921-023255","article-title":"Artificial intelligence and machine learning for lead-to-candidate decision-making and beyond","volume":"63","author":"McNair","year":"2023","journal-title":"Annu. Rev. Pharmacol. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib111","doi-asserted-by":"crossref","first-page":"535","DOI":"10.3390\/molecules21040535","article-title":"Special issue: chemoinformatics","volume":"21","author":"Willett","year":"2016","journal-title":"Molecules"},{"key":"10.1016\/j.ejmech.2024.117164_bib112","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1517\/17460441.2013.761204","article-title":"How far can virtual screening take us in drug discovery?","volume":"8","author":"Kar","year":"2013","journal-title":"Expet Opin. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib113","series-title":"Comput. Drug Discov.","first-page":"275","article-title":"Using Artificial Intelligence for de novo Drug Design and Retrosynthesis","author":"Arora","year":"2024"},{"key":"10.1016\/j.ejmech.2024.117164_bib114","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1021\/acs.jcim.8b00673","article-title":"Improved method of structure-based virtual screening via interaction-energy-based learning","volume":"59","author":"Yasuo","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib115","doi-asserted-by":"crossref","first-page":"10150","DOI":"10.3390\/molecules190710150","article-title":"Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design","volume":"19","author":"Grinter","year":"2014","journal-title":"Molecules"},{"key":"10.1016\/j.ejmech.2024.117164_bib117","doi-asserted-by":"crossref","first-page":"5166","DOI":"10.1039\/B608269F","article-title":"Molecular mechanics methods for predicting protein\u2013ligand binding","volume":"8","author":"Huang","year":"2006","journal-title":"Phys. Chem. Chem. Phys."},{"key":"10.1016\/j.ejmech.2024.117164_bib118","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1006\/jmbi.1999.3371","article-title":"Knowledge-based scoring function to predict protein-ligand interactions","volume":"295","author":"Gohlke","year":"2000","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib119","doi-asserted-by":"crossref","DOI":"10.1142\/S021972001850004X","article-title":"Boosted neural networks scoring functions for accurate ligand docking and ranking","volume":"16","author":"Ashtawy","year":"2018","journal-title":"J. Bioinf. Comput. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib120","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1021\/ci100369f","article-title":"A machine learning-based method to improve docking scoring functions and its application to drug repurposing","volume":"51","author":"Kinnings","year":"2011","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib121","doi-asserted-by":"crossref","DOI":"10.1038\/srep46710","article-title":"Performance of machine-learning scoring functions in structure-based virtual screening","volume":"7","author":"W\u00f3jcikowski","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib122","doi-asserted-by":"crossref","DOI":"10.2174\/1568026617666170224121313","article-title":"Structure-based virtual screening approaches in kinase-directed drug discovery","volume":"17","author":"Bajusz","year":"2017","journal-title":"Curr. Top. Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib123","article-title":"Constructing and validating high-performance MIEC-SVM models in virtual screening for kinases: a better way for actives discovery","volume":"6","author":"Sun","year":"2016","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib124","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1002\/cmdc.201600636","article-title":"Structure\u2010based target\u2010specific screening leads to small\u2010molecule CaMKII inhibitors","volume":"12","author":"Xu","year":"2017","journal-title":"ChemMedChem"},{"key":"10.1016\/j.ejmech.2024.117164_bib125","article-title":"Improving the virtual screening ability of target-specific scoring functions using deep learning methods","volume":"10","author":"Wang","year":"2019","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2024.117164_bib126","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/6565241","article-title":"Prediction of GPCR-ligand binding using machine learning algorithms","volume":"2018","author":"Seo","year":"2018","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib127","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1021\/acs.jcim.7b00017","article-title":"Protein\u2013ligand empirical interaction components for virtual screening","volume":"57","author":"Yan","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib128","article-title":"Optimization of molecules via deep reinforcement learning","volume":"9","author":"Zhou","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib129","series-title":"Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity","author":"Gomes","year":"2017"},{"key":"10.1016\/j.ejmech.2024.117164_bib130","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1021\/acs.jcim.7b00650","article-title":"K deep : protein\u2013ligand absolute binding affinity prediction via 3D-convolutional neural networks","volume":"58","author":"Jim\u00e9nez","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib131","doi-asserted-by":"crossref","first-page":"10520","DOI":"10.1021\/acs.chemrev.8b00728","article-title":"Concepts of artificial intelligence for computer-assisted drug discovery","volume":"119","author":"Yang","year":"2019","journal-title":"Chem. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib132","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab476","article-title":"Artificial intelligence in the prediction of protein\u2013ligand interactions: recent advances and future directions","volume":"23","author":"Dhakal","year":"2022","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib133","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1016\/j.jmgm.2009.01.003","article-title":"A comparison of ligand based virtual screening methods and application to corticotropin releasing factor 1 receptor","volume":"27","author":"Tresadern","year":"2009","journal-title":"J. Mol. Graph. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib134","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1007\/s11030-021-10326-z","article-title":"Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries","volume":"26","author":"Selvaraj","year":"2022","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2024.117164_bib135","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial intelligence to deep learning: machine intelligence approach for drug discovery","volume":"25","author":"Gupta","year":"2021","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2024.117164_bib136","doi-asserted-by":"crossref","DOI":"10.1038\/srep46710","article-title":"Performance of machine-learning scoring functions in structure-based virtual screening","volume":"7","author":"W\u00f3jcikowski","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib137","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.4155\/fmc-2018-0314","article-title":"Deep learning and virtual drug screening","volume":"10","author":"Carpenter","year":"2018","journal-title":"Future Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib138","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A deep learning approach to antibiotic discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"key":"10.1016\/j.ejmech.2024.117164_bib139","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib140","doi-asserted-by":"crossref","first-page":"8835","DOI":"10.1021\/acs.jmedchem.9b02187","article-title":"Improvement in ADMET prediction with multitask deep featurization","volume":"63","author":"Feinberg","year":"2020","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib141","doi-asserted-by":"crossref","first-page":"225","DOI":"10.22270\/jddt.v10i4.4218","article-title":"Virtual screening, molecular docking and QSAR studies in drug discovery and development programme","volume":"10","author":"Rudrapal","year":"2020","journal-title":"J. Drug Deliv. Therapeut."},{"key":"10.1016\/j.ejmech.2024.117164_bib142","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.3390\/ijms10041670","article-title":"QSAR analysis of 2-amino or 2-methyl-1-substituted benzimidazoles against Pseudomonas aeruginosa","volume":"10","author":"Podunavac-Kuzmanovi\u0107","year":"2009","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib143","doi-asserted-by":"crossref","first-page":"7146","DOI":"10.1016\/j.bmc.2006.06.065","article-title":"Synthesis and QSAR studies of novel triazole compounds containing thioamide as potential antifungal agents","volume":"14","author":"Wei","year":"2006","journal-title":"Bioorg. Med. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib144","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/978-1-0716-1787-8_10","article-title":"Deep neural networks for QSAR","author":"Xu","year":"2022","journal-title":"Methods Mol. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib145","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1186\/s12859-019-3135-4","article-title":"Comprehensive ensemble in QSAR prediction for drug discovery","volume":"20","author":"Kwon","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib146","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1021\/acs.chemrestox.0c00347","article-title":"Quantitative structure-activity relationship (QSAR) model for the severity prediction of drug-induced rhabdomyolysis by using random forest","volume":"34","author":"Zhou","year":"2021","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib147","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1111\/cbdd.13690","article-title":"The efficiency of ligand\u2013receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network","volume":"96","author":"Mozafari","year":"2020","journal-title":"Chem. Biol. Drug Des."},{"key":"10.1016\/j.ejmech.2024.117164_bib148","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2019.106656","article-title":"A novel hybrid Harris hawks optimization and support vector machines for drug design and discovery","volume":"133","author":"Houssein","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.ejmech.2024.117164_bib149","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.matpr.2019.05.262","article-title":"Drug likeness prediction using structure based molecular descriptors and support vector machines","volume":"18","author":"Vinotha","year":"2019","journal-title":"Mater. Today Proc."},{"key":"10.1016\/j.ejmech.2024.117164_bib150","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1021\/acs.chemrestox.6b00227","article-title":"Predicting rat and human pregnane X receptor activators using bayesian classification models","volume":"29","author":"AbdulHameed","year":"2016","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib151","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1021\/tx500389q","article-title":"Absorption, distribution, metabolism, excretion, and toxicity evaluation in drug discovery. 14. Prediction of human pregnane X receptor activators by using naive bayesian classification technique","volume":"28","author":"Shi","year":"2015","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib152","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1016\/j.drudis.2018.06.016","article-title":"Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks","volume":"23","author":"Ghasemi","year":"2018","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib153","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/978-3-030-32622-7_10","article-title":"Artificial neural networks in computer-aided drug design: an overview of recent advances","author":"Cheirdaris","year":"2020","journal-title":"Adv. Exp. Med. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib154","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1021\/acs.jcim.7b00166","article-title":"Profile-QSAR 2.0: kinase virtual screening accuracy comparable to four-concentration IC 50 s for realistically novel compounds","volume":"57","author":"Martin","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib155","doi-asserted-by":"crossref","first-page":"555","DOI":"10.2174\/1386207322666191016110232","article-title":"A random forest model to predict the activity of a large set of soluble epoxide hydrolase inhibitors solely based on a set of simple fragmental descriptors","volume":"22","author":"Shamsara","year":"2019","journal-title":"Comb. Chem. High Throughput Screen."},{"key":"10.1016\/j.ejmech.2024.117164_bib156","doi-asserted-by":"crossref","first-page":"4393","DOI":"10.3390\/molecules24234393","article-title":"Construction of quantitative structure activity relationship (QSAR) models to predict potency of structurally diversed Janus kinase 2 inhibitors","volume":"24","author":"Simeon","year":"2019","journal-title":"Molecules"},{"key":"10.1016\/j.ejmech.2024.117164_bib157","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1021\/acs.jcim.6b00753","article-title":"Comparison of the predictive performance and interpretability of random forest and linear models on benchmark data sets","volume":"57","author":"Marchese Robinson","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib158","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2019.103826","article-title":"Chemometrics approach for the prediction of chemical compounds' toxicity degree based on quantum inspired optimization with applications in drug discovery","volume":"193","author":"Darwish","year":"2019","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"10.1016\/j.ejmech.2024.117164_bib159","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1021\/acs.jcim.7b00087","article-title":"Demystifying multitask deep neural networks for quantitative structure\u2013activity relationships","volume":"57","author":"Xu","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib160","doi-asserted-by":"crossref","first-page":"4613","DOI":"10.1021\/acs.jcim.9b00526","article-title":"Novel consensus architecture to improve performance of large-scale multitask deep learning QSAR models","volume":"59","author":"Zakharov","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib161","doi-asserted-by":"crossref","DOI":"10.1002\/minf.201900154","article-title":"Validation study of QSAR\/DNN models using the competition datasets","volume":"39","author":"Kato","year":"2020","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2024.117164_bib162","author":"Dahl"},{"key":"10.1016\/j.ejmech.2024.117164_bib163","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure\u2013activity relationships","volume":"55","author":"Ma","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib164","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0187271","article-title":"Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies","volume":"13","author":"Hooijmans","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2024.117164_bib165","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial intelligence to deep learning: machine intelligence approach for drug discovery","volume":"25","author":"Gupta","year":"2021","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2024.117164_bib166","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41573-019-0050-3","article-title":"Rethinking drug design in the artificial intelligence era","volume":"19","author":"Schneider","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib167","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib168","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1007\/s11030-023-10771-y","article-title":"Deep learning algorithms applied to computational chemistry","volume":"28","author":"Guzman-Pando","year":"2024","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2024.117164_bib169","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1017\/S0033583515000190","article-title":"In silico ADME\/T modelling for rational drug design","volume":"48","author":"Wang","year":"2015","journal-title":"Q. Rev. Biophys."},{"key":"10.1016\/j.ejmech.2024.117164_bib170","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1024\/0300-9831.77.2.107","article-title":"A comparison of physical properties, screening procedures and a human efficacy trial for predicting the bioavailability of commercial elemental iron powders used for food fortification","volume":"77","author":"Lynch","year":"2007","journal-title":"Int. J. Vitam. Nutr. Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib171","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/S0161-5890(03)00077-4","article-title":"Impact of physical properties of formulations on bioavailability of active substance: current and novel drugs with cyclosporine","volume":"39","author":"Andrysek","year":"2003","journal-title":"Mol. Immunol."},{"key":"10.1016\/j.ejmech.2024.117164_bib172","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1021\/acs.jcim.6b00625","article-title":"In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning","volume":"57","author":"Zang","year":"2017","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib173","doi-asserted-by":"crossref","DOI":"10.2174\/1570163814666170404160911","article-title":"Prediction of human intestinal absorption of compounds using artificial intelligence techniques","volume":"14","author":"Kumar","year":"2017","journal-title":"Curr. Drug Discov. Technol."},{"key":"10.1016\/j.ejmech.2024.117164_bib174","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1021\/ci400187y","article-title":"Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules","volume":"53","author":"Lusci","year":"2013","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib175","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13321-018-0263-1","article-title":"OPERA models for predicting physicochemical properties and environmental fate endpoints","volume":"10","author":"Mansouri","year":"2018","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib176","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1093\/bioinformatics\/btz111","article-title":"DeepAffinity: interpretable deep learning of compound\u2013protein affinity through unified recurrent and convolutional neural networks","volume":"35","author":"Karimi","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib177","author":"Born"},{"key":"10.1016\/j.ejmech.2024.117164_bib178","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib179","doi-asserted-by":"crossref","DOI":"10.1002\/minf.202000212","article-title":"ChemBoost: a chemical language based approach for protein \u2013 ligand binding affinity prediction","volume":"40","author":"\u00d6z\u00e7elik","year":"2021","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2024.117164_bib180","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/s41563-019-0332-5","article-title":"Opportunities and challenges using artificial intelligence in ADME\/Tox","volume":"18","author":"Bhhatarai","year":"2019","journal-title":"Nat. Mater."},{"key":"10.1016\/j.ejmech.2024.117164_bib181","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1038\/nrd1470","article-title":"Can the pharmaceutical industry reduce attrition rates?","volume":"3","author":"Kola","year":"2004","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib182","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1021\/ci100104j","article-title":"Estimation of ADME properties with substructure pattern recognition","volume":"50","author":"Shen","year":"2010","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib183","article-title":"In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts","volume":"6","author":"Yang","year":"2018","journal-title":"Front. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib184","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.1021\/acs.jcim.0c00443","article-title":"Practical applications of deep learning to impute heterogeneous drug discovery data","volume":"60","author":"Irwin","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib185","doi-asserted-by":"crossref","DOI":"10.3389\/fenvs.2015.00085","article-title":"Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs","volume":"3","author":"Huang","year":"2016","journal-title":"Front. Environ. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib186","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1007\/s11030-021-10326-z","article-title":"Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries","volume":"26","author":"Selvaraj","year":"2022","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2024.117164_bib187","first-page":"S7","article-title":"Requirements for a lead compound to become a clinical candidate, BMC","volume":"9","author":"Hefti","year":"2008","journal-title":"Neurosci"},{"key":"10.1016\/j.ejmech.2024.117164_bib188","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1021\/tx200211v","article-title":"Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety","volume":"24","author":"Meanwell","year":"2011","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib189","doi-asserted-by":"crossref","first-page":"3935","DOI":"10.1021\/acs.molpharmaceut.7b00631","article-title":"ADMET evaluation in drug discovery. 18. Reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches","volume":"14","author":"Lei","year":"2017","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2024.117164_bib190","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13321-016-0117-7","article-title":"ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling","volume":"8","author":"Lei","year":"2016","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib191","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1002\/minf.201500055","article-title":"In silico prediction of drug induced liver toxicity using substructure pattern recognition method","volume":"35","author":"Zhang","year":"2016","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2024.117164_bib192","doi-asserted-by":"crossref","first-page":"10520","DOI":"10.1021\/acs.chemrev.8b00728","article-title":"Concepts of artificial intelligence for computer-assisted drug discovery","volume":"119","author":"Yang","year":"2019","journal-title":"Chem. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib193","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.addr.2015.03.014","article-title":"Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools","volume":"86","author":"Tao","year":"2015","journal-title":"Adv. Drug Deliv. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib194","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1038\/s41563-019-0332-5","article-title":"Opportunities and challenges using artificial intelligence in ADME\/Tox","volume":"18","author":"Bhhatarai","year":"2019","journal-title":"Nat. Mater."},{"key":"10.1016\/j.ejmech.2024.117164_bib195","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1080\/17425255.2017.1389897","article-title":"In silico ADME-Tox modeling: progress and prospects","volume":"13","author":"Alqahtani","year":"2017","journal-title":"Expet Opin. Drug Metabol. Toxicol."},{"key":"10.1016\/j.ejmech.2024.117164_bib196","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/17460441.2017.1280024","article-title":"Computational polypharmacology: a new paradigm for drug discovery","volume":"12","author":"Chaudhari","year":"2017","journal-title":"Expet Opin. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib197","doi-asserted-by":"crossref","DOI":"10.1186\/s40169-017-0181-2","article-title":"A perspective on multi\u2010target drug discovery and design for complex diseases","volume":"7","author":"Ramsay","year":"2018","journal-title":"Clin. Transl. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib198","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/17460441.2024.2376643","article-title":"Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery","volume":"19","author":"Manen-Freixa","year":"2024","journal-title":"Expet Opin. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib199","doi-asserted-by":"crossref","DOI":"10.3389\/fchem.2019.00071","article-title":"Editorial: multi-target-directed ligands (MTDL) as challenging research tools in drug discovery: from design to pharmacological evaluation","volume":"7","author":"Alcaro","year":"2019","journal-title":"Front. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib200","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1016\/j.csbj.2022.04.021","article-title":"On the road to explainable AI in drug-drug interactions prediction: a systematic review","volume":"20","author":"Vo","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.ejmech.2024.117164_bib201","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1186\/s12859-019-3013-0","article-title":"Novel deep learning model for more accurate prediction of drug-drug interaction effects","volume":"20","author":"Lee","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib202","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12916-016-0553-2","article-title":"Post-marketing withdrawal of 462 medicinal products because of adverse drug reactions: a systematic review of the world literature","volume":"14","author":"Onakpoya","year":"2016","journal-title":"BMC Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib203","series-title":"2021 1st Int. Conf. Artif. Intell. Data Anal.","first-page":"221","article-title":"An efficient pattern recognition based method for drug-drug interaction diagnosis","author":"Javed","year":"2021"},{"key":"10.1016\/j.ejmech.2024.117164_bib204","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-97193-8","article-title":"A machine learning framework for predicting drug\u2013drug interactions","volume":"11","author":"Mei","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib205","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1111\/jcpt.12786","article-title":"Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies","volume":"44","author":"Song","year":"2019","journal-title":"J. Clin. Pharm. Therapeut."},{"key":"10.1016\/j.ejmech.2024.117164_bib206","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.smhl.2018.07.007","article-title":"Drug\u2010drug interaction discovery: kernel learning from heterogeneous similarities","volume":"9\u201310","author":"Dhami","year":"2018","journal-title":"Smart Heal"},{"key":"10.1016\/j.ejmech.2024.117164_bib207","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.582470","article-title":"Integrated random negative sampling and uncertainty sampling in active learning improve clinical drug safety drug\u2013drug interaction information retrieval","volume":"11","author":"Xie","year":"2021","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2024.117164_bib208","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2020.103603","article-title":"Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data","volume":"112","author":"Zhan","year":"2020","journal-title":"J. Biomed. Inf."},{"key":"10.1016\/j.ejmech.2024.117164_bib209","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1007068","article-title":"Leveraging genetic interactions for adverse drug-drug interaction prediction","volume":"15","author":"Qian","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib210","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.3390\/cells10113092","article-title":"Machine learning-based prediction of drug-drug interactions for histamine antagonist using hybrid chemical features","volume":"10","author":"Dang","year":"2021","journal-title":"Cells"},{"key":"10.1016\/j.ejmech.2024.117164_bib211","doi-asserted-by":"crossref","DOI":"10.1002\/minf.202100264","article-title":"An AI\u2010based prediction model for drug\u2010drug interactions in osteoporosis and paget's diseases from SMILES","volume":"41","author":"Hung","year":"2022","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2024.117164_bib212","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ymeth.2019.02.021","article-title":"Exploring semi-supervised variational autoencoders for biomedical relation extraction","volume":"166","author":"Zhang","year":"2019","journal-title":"Methods"},{"key":"10.1016\/j.ejmech.2024.117164_bib213","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ymeth.2020.05.007","article-title":"Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning","volume":"179","author":"Zhang","year":"2020","journal-title":"Methods"},{"key":"10.1016\/j.ejmech.2024.117164_bib214","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.ins.2019.05.017","article-title":"SFLLN: a sparse feature learning ensemble method with linear neighborhood regularization for predicting drug\u2013drug interactions","volume":"497","author":"Zhang","year":"2019","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib215","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1049\/iet-syb.2019.0116","article-title":"Efficient prediction of drug\u2013drug interaction using deep learning models","volume":"14","author":"Kumar Shukla","year":"2020","journal-title":"IET Syst. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib216","doi-asserted-by":"crossref","first-page":"30033","DOI":"10.1073\/pnas.1907373117","article-title":"The unreasonable effectiveness of deep learning in artificial intelligence","volume":"117","author":"Sejnowski","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.ejmech.2024.117164_bib217","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0219796","article-title":"Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures","volume":"14","author":"Shtar","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2024.117164_bib218","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-50121-3","article-title":"Drug-drug interaction predicting by neural network using integrated similarity","volume":"9","author":"Rohani","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib219","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1186\/s12859-021-04298-y","article-title":"A neural network-based method for polypharmacy side effects prediction","volume":"22","author":"Masumshah","year":"2021","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib220","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1093\/bioinformatics\/btab169","article-title":"MUFFIN: multi-scale feature fusion for drug\u2013drug interaction prediction","volume":"37","author":"Chen","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib221","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2020.103432","article-title":"Drug-drug interaction extraction via hybrid neural networks on biomedical literature","volume":"106","author":"Wu","year":"2020","journal-title":"J. Biomed. Inf."},{"key":"10.1016\/j.ejmech.2024.117164_bib222","series-title":"2016 IEEE Int. Conf. Bioinforma. Biomed.","first-page":"1074","article-title":"Dependency-based convolutional neural network for drug-drug interaction extraction","author":"Liu","year":"2016"},{"key":"10.1016\/j.ejmech.2024.117164_bib223","doi-asserted-by":"crossref","first-page":"3444","DOI":"10.1093\/bioinformatics\/btw486","article-title":"Drug drug interaction extraction from biomedical literature using syntax convolutional neural network","volume":"32","author":"Zhao","year":"2016","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib224","series-title":"2018 IEEE Int. Conf. Bioinforma. Biomed.","first-page":"1662","article-title":"Deep convolution neural networks for drug-drug interaction extraction","author":"Sun","year":"2018"},{"key":"10.1016\/j.ejmech.2024.117164_bib225","series-title":"2017 IEEE Int. Conf. Bioinforma. Biomed.","first-page":"1795","article-title":"Drug-drug interaction relation extraction with deep convolutional neural networks","author":"Dewi","year":"2017"},{"key":"10.1016\/j.ejmech.2024.117164_bib226","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1186\/s12859-021-04241-1","article-title":"Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes","volume":"22","author":"Luo","year":"2021","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib227","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1093\/nsr\/nwx105","article-title":"An overview of multi-task learning","volume":"5","author":"Zhang","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"10.1016\/j.ejmech.2024.117164_bib228","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2018.03.001","article-title":"Position-aware deep multi-task learning for drug\u2013drug interaction extraction","volume":"87","author":"Zhou","year":"2018","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib229","series-title":"Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)","first-page":"554","article-title":"Drug-drug interaction extraction via recurrent neural network with multiple attention layers","author":"Yi","year":"2017"},{"key":"10.1016\/j.ejmech.2024.117164_bib230","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1186\/s12859-017-1855-x","article-title":"An attention-based effective neural model for drug-drug interactions extraction","volume":"18","author":"Zheng","year":"2017","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib231","article-title":"Drug drug interaction extraction from the literature using a recursive neural network","volume":"13","author":"Lim","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2024.117164_bib232","doi-asserted-by":"crossref","DOI":"10.1002\/wcms.1504","article-title":"Current advances in ligand\u2010based target prediction","volume":"11","author":"Yang","year":"2021","journal-title":"WIREs Comput. Mol. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib233","doi-asserted-by":"crossref","first-page":"10883","DOI":"10.18632\/oncotarget.14073","article-title":"The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology","volume":"8","author":"Kadurin","year":"2017","journal-title":"Oncotarget"},{"key":"10.1016\/j.ejmech.2024.117164_bib234","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","article-title":"druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico","volume":"14","author":"Kadurin","year":"2017","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2024.117164_bib235","doi-asserted-by":"crossref","first-page":"4398","DOI":"10.1021\/acs.molpharmaceut.8b00839","article-title":"Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery","volume":"15","author":"Polykovskiy","year":"2018","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2024.117164_bib236","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib237","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13321-018-0286-7","article-title":"Molecular generative model based on conditional variational autoencoder for de novo molecular design","volume":"10","author":"Lim","year":"2018","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib238","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","article-title":"Molecular de-novo design through deep reinforcement learning","volume":"9","author":"Olivecrona","year":"2017","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib239","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","article-title":"Generating focused molecule libraries for drug discovery with recurrent neural networks","volume":"4","author":"Segler","year":"2018","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib240","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.3390\/ijms22041676","article-title":"Advances in de novo drug design: from conventional to machine learning methods","volume":"22","author":"Mouchlis","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib241","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.drudis.2019.07.006","article-title":"Deep learning in drug discovery: opportunities, challenges and future prospects","volume":"24","author":"Lavecchia","year":"2019","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2024.117164_bib242","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","article-title":"Molecular de-novo design through deep reinforcement learning","volume":"9","author":"Olivecrona","year":"2017","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib243","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13321-019-0355-6","article-title":"An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor","volume":"11","author":"Liu","year":"2019","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib244","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aap7885","article-title":"Deep reinforcement learning for de novo drug design","volume":"4","author":"Popova","year":"2018","journal-title":"Sci. Adv."},{"key":"10.1016\/j.ejmech.2024.117164_bib245","doi-asserted-by":"crossref","DOI":"10.1002\/minf.201700153","article-title":"De novo design of bioactive small molecules by artificial intelligence","volume":"37","author":"Merk","year":"2018","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2024.117164_bib246","article-title":"Generative recurrent networks for de novo drug design","volume":"37","author":"Gupta","year":"2018","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2024.117164_bib247","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101552","article-title":"Generative adversarial network in medical imaging: a review","volume":"58","author":"Yi","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.ejmech.2024.117164_bib248","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1109\/TKDE.2021.3130191","article-title":"A review on generative adversarial networks: algorithms, theory, and applications","volume":"35","author":"Gui","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.ejmech.2024.117164_bib249","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1021\/acs.jcim.7b00690","article-title":"Reinforced Adversarial Neural Computer for de Novo Molecular Design","volume":"58","author":"Putin","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib250","doi-asserted-by":"crossref","first-page":"4386","DOI":"10.1021\/acs.molpharmaceut.7b01137","article-title":"Adversarial Threshold Neural Computer for Molecular de Novo Design","volume":"15","author":"Putin","year":"2018","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2024.117164_bib251","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13321-019-0397-9","article-title":"A de novo molecular generation method using latent vector based generative adversarial network","volume":"11","author":"Prykhodko","year":"2019","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib252","author":"De Cao"},{"key":"10.1016\/j.ejmech.2024.117164_bib253","doi-asserted-by":"crossref","first-page":"13664","DOI":"10.1039\/D1SC04444C","article-title":"Structure-based: de novo drug design using 3D deep generative models","volume":"12","author":"Li","year":"2021","journal-title":"Chem. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib254","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib255","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1021\/acs.jcim.8b00706","article-title":"Shape-Based Generative Modeling for de Novo Drug Design","volume":"59","author":"Skalic","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2024.117164_bib256","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","article-title":"druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico","volume":"14","author":"Kadurin","year":"2017","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2024.117164_bib257","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/s41563-019-0338-z","article-title":"Exploiting machine learning for end-to-end drug discovery and development","volume":"18","author":"Ekins","year":"2019","journal-title":"Nat. Mater."},{"key":"10.1016\/j.ejmech.2024.117164_bib258","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1056\/NEJMms1706744","article-title":"Putting the patient back together \u2014 social medicine, network medicine, and the limits of reductionism","volume":"377","author":"Greene","year":"2017","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib259","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/534314a","article-title":"Can you teach old drugs new tricks?","volume":"534","author":"Nosengo","year":"2016","journal-title":"Nature"},{"key":"10.1016\/j.ejmech.2024.117164_bib260","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.compbiomed.2019.05.006","article-title":"DrugR+: a comprehensive relational database for drug repurposing, combination therapy, and replacement therapy","volume":"109","author":"Masoudi-Sobhanzadeh","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib261","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1186\/s12859-019-3238-y","article-title":"Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces","volume":"20","author":"Zheng","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib262","doi-asserted-by":"crossref","first-page":"2712","DOI":"10.3390\/molecules24152712","article-title":"Inferring drug-related diseases based on convolutional neural network and gated recurrent unit","volume":"24","author":"Xuan","year":"2019","journal-title":"Molecules"},{"key":"10.1016\/j.ejmech.2024.117164_bib263","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1186\/s12859-019-2644-5","article-title":"Prediction of drug-disease associations based on ensemble meta paths and singular value decomposition","volume":"20","author":"Wu","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib264","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/s12911-019-0764-5","article-title":"Relation path feature embedding based convolutional neural network method for drug discovery","volume":"19","author":"Zhao","year":"2019","journal-title":"BMC Med. Inf. Decis. Making"},{"key":"10.1016\/j.ejmech.2024.117164_bib265","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1186\/s12859-019-3283-6","article-title":"Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning","volume":"20","author":"Wang","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib266","series-title":"IFIP Adv. Inf. Commun. Technol.","first-page":"302","article-title":"Drug-target interaction prediction in drug repositioning based on deep semi-supervised learning","author":"Bahi","year":"2018"},{"key":"10.1016\/j.ejmech.2024.117164_bib267","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103492","article-title":"Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches","volume":"115","author":"deAndr\u00e9s-Galiana","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib268","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1186\/s12859-019-2858-6","article-title":"Drug repurposing with network reinforcement","volume":"20","author":"Nam","year":"2019","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib269","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1093\/bioinformatics\/btz418","article-title":"deepDR: a network-based deep learning approach to in silico drug repositioning","volume":"35","author":"Zeng","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib270","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1089\/cmb.2019.0063","article-title":"BiRWDDA: a novel drug repositioning method based on multisimilarity fusion","volume":"26","author":"Yan","year":"2019","journal-title":"J. Comput. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib271","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1186\/s12967-020-02541-3","article-title":"RepCOOL: computational drug repositioning via integrating heterogeneous biological networks","volume":"18","author":"Fahimian","year":"2020","journal-title":"J. Transl. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib272","article-title":"A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses","volume":"24","author":"Li","year":"2021","journal-title":"iScience"},{"key":"10.1016\/j.ejmech.2024.117164_bib273","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s41421-020-0153-3","article-title":"Network-based drug repurposing for novel coronavirus 2019-nCoV\/SARS-CoV-2","volume":"6","author":"Zhou","year":"2020","journal-title":"Cell Discov"},{"key":"10.1016\/j.ejmech.2024.117164_bib274","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106585","article-title":"SNF\u2013CVAE: computational method to predict drug\u2013disease interactions using similarity network fusion and collective variational autoencoder","volume":"212","author":"Jarada","year":"2021","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.ejmech.2024.117164_bib275","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1093\/bioinformatics\/btz418","article-title":"deepDR: a network-based deep learning approach to in silico drug repositioning","volume":"35","author":"Zeng","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib276","article-title":"A novel drug repurposing approach for non-small cell lung cancer using deep learning","volume":"15","author":"Li","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2024.117164_bib277","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A deep learning approach to antibiotic discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"key":"10.1016\/j.ejmech.2024.117164_bib278","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1093\/bib\/bby004","article-title":"Artificial intelligence in drug combination therapy","volume":"20","author":"Tsigelny","year":"2019","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib279","doi-asserted-by":"crossref","first-page":"38022","DOI":"10.18632\/oncotarget.16723","article-title":"Combination therapy in combating cancer","volume":"8","author":"Mokhtari","year":"2017","journal-title":"Oncotarget"},{"key":"10.1016\/j.ejmech.2024.117164_bib280","series-title":"2018 IEEE Int. Symp. High Perform. Comput. Archit.","first-page":"620","article-title":"Applied machine learning at facebook: a datacenter infrastructure perspective","author":"Hazelwood","year":"2018"},{"key":"10.1016\/j.ejmech.2024.117164_bib281","series-title":"2018 6th Int. Symp. Digit. Forensic Secur","first-page":"1","article-title":"Prediction of the autism spectrum disorder diagnosis with linear discriminant analysis classifier and K-nearest neighbor in children","author":"Altay","year":"2018"},{"key":"10.1016\/j.ejmech.2024.117164_bib282","doi-asserted-by":"crossref","first-page":"6136","DOI":"10.1038\/s41467-020-19950-z","article-title":"Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects","volume":"11","author":"Julkunen","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejmech.2024.117164_bib283","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008653","article-title":"TranSynergy: mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations","volume":"17","author":"Liu","year":"2021","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib284","doi-asserted-by":"crossref","first-page":"433","DOI":"10.2217\/nnm-2019-0366","article-title":"Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment","volume":"15","author":"Wilson","year":"2020","journal-title":"Nanomedicine"},{"key":"10.1016\/j.ejmech.2024.117164_bib285","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aan0941","article-title":"Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP)","volume":"10","author":"Rashid","year":"2018","journal-title":"Sci. Transl. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib286","doi-asserted-by":"crossref","first-page":"3332","DOI":"10.1021\/acsnano.5b00638","article-title":"Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology","volume":"9","author":"Wang","year":"2015","journal-title":"ACS Nano"},{"key":"10.1016\/j.ejmech.2024.117164_bib287","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.artmed.2017.05.008","article-title":"Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles","volume":"83","author":"Li","year":"2017","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib288","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1186\/s12859-018-2509-3","article-title":"Predicting tumor cell line response to drug pairs with deep learning","volume":"19","author":"Xia","year":"2018","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.ejmech.2024.117164_bib289","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006752","article-title":"Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer","volume":"15","author":"Malyutina","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib290","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.csbj.2020.02.006","article-title":"Deep graph embedding for prioritizing synergistic anticancer drug combinations","volume":"18","author":"Jiang","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.ejmech.2024.117164_bib291","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1096\/fj.04-2747rev","article-title":"Nanomedicine: current status and future prospects","volume":"19","author":"Moghimi","year":"2005","journal-title":"Faseb. J."},{"key":"10.1016\/j.ejmech.2024.117164_bib292","series-title":"Nanoparticles Biomed. Appl.","first-page":"385","article-title":"The role of artificial intelligence in scaling nanomedicine toward broad clinical impact","author":"Khong","year":"2020"},{"key":"10.1016\/j.ejmech.2024.117164_bib293","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1038\/nprot.2016.017","article-title":"Optimization of drug combinations using feedback system control","volume":"11","author":"Nowak-Sliwinska","year":"2016","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.ejmech.2024.117164_bib294","doi-asserted-by":"crossref","DOI":"10.1038\/srep14508","article-title":"A streamlined search technology for identification of synergistic drug combinations","volume":"5","author":"Weiss","year":"2015","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2024.117164_bib295","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1177\/2472630316682338","article-title":"Current trends in multidrug optimization: an alley of future successful treatment of complex disorders","volume":"22","author":"Weiss","year":"2017","journal-title":"SLAS Technol"},{"key":"10.1016\/j.ejmech.2024.117164_bib296","doi-asserted-by":"crossref","first-page":"87","DOI":"10.14573\/altex.2010.2.87","article-title":"Food for thought \u2026 on alternative methods for nanoparticle safety testing","author":"Hartung","year":"2010","journal-title":"ALTEX"},{"key":"10.1016\/j.ejmech.2024.117164_bib297","doi-asserted-by":"crossref","DOI":"10.1038\/d41586-018-05664-2","article-title":"Software beats animal tests at predicting toxicity of chemicals","volume":"559","author":"Van Noorden","year":"2018","journal-title":"Nature"},{"key":"10.1016\/j.ejmech.2024.117164_bib298","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2019.00100","article-title":"From petri dishes to organ on chip platform: the increasing importance of machine learning and image analysis","volume":"10","author":"Mencattini","year":"2019","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2024.117164_bib299","first-page":"23","article-title":"Promising strategies for overcoming cancer drug resistance: from nanomedicine to artificial intelligence","volume":"1","author":"Martinelli","year":"2022","journal-title":"World J. Med. Innov."},{"key":"10.1016\/j.ejmech.2024.117164_bib300","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1038\/nbt.2877","article-title":"A community effort to assess and improve drug sensitivity prediction algorithms","volume":"32","author":"Costello","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.ejmech.2024.117164_bib301","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1093\/bioinformatics\/btv529","article-title":"Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel","volume":"32","author":"Cort\u00e9s-Ciriano","year":"2016","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2024.117164_bib302","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1049\/iet-syb.2018.5094","article-title":"Ensembled machine learning framework for drug sensitivity prediction","volume":"14","author":"Sharma","year":"2020","journal-title":"IET Syst. Biol."},{"key":"10.1016\/j.ejmech.2024.117164_bib303","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1002\/jcc.24764","article-title":"Deep learning for computational chemistry","volume":"38","author":"Goh","year":"2017","journal-title":"J. Comput. Chem."},{"key":"10.1016\/j.ejmech.2024.117164_bib304","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s13321-017-0226-y","article-title":"Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data","volume":"9","author":"Koutsoukas","year":"2017","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2024.117164_bib305","author":"Oskooei"},{"key":"10.1016\/j.ejmech.2024.117164_bib306","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aac5954","article-title":"Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform","volume":"8","author":"Zarrinpar","year":"2016","journal-title":"Sci. Transl. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib307","article-title":"Modulating BET bromodomain inhibitor ZEN\u20103694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE.AI, an artificial intelligence platform","volume":"1","author":"Pantuck","year":"2018","journal-title":"Adv. Ther."},{"key":"10.1016\/j.ejmech.2024.117164_bib308","doi-asserted-by":"crossref","DOI":"10.1002\/adhm.201901862","article-title":"Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine","volume":"9","author":"Singh","year":"2020","journal-title":"Adv. Healthcare Mater."},{"key":"10.1016\/j.ejmech.2024.117164_bib309","doi-asserted-by":"crossref","DOI":"10.15761\/FNN.1000S1004","article-title":"The use of data mining and machine learning in nanomedicine: a survey","volume":"4","author":"Dimitri","year":"2018","journal-title":"Front. Nanosci. Nanotechnol."},{"key":"10.1016\/j.ejmech.2024.117164_bib310","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cmpb.2016.04.025","article-title":"A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles","volume":"132","author":"Jones","year":"2016","journal-title":"Comput. Methods Progr. Biomed."},{"key":"10.1016\/j.ejmech.2024.117164_bib311","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.ejmech.2024.117164_bib312","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1039\/D2SC05709C","article-title":"AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor","volume":"14","author":"Ren","year":"2023","journal-title":"Chem. Sci."},{"key":"10.1016\/j.ejmech.2024.117164_bib313","first-page":"188","article-title":"AI-Driven predictive analytics for drug studies","volume":"2","author":"Tummala","year":"2024","journal-title":"J. Pharma Insights Res."},{"key":"10.1016\/j.ejmech.2024.117164_bib314","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.ejmech.2024.117164_bib315","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1186\/s13195-021-00826-3","article-title":"Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease","volume":"13","author":"Tsuji","year":"2021","journal-title":"Alzheimer's Res. Ther."},{"key":"10.1016\/j.ejmech.2024.117164_bib316","series-title":"IEEE Symp. Ser. Comput. Intell.","first-page":"1542","article-title":"Improving deep learning with generic data augmentation","author":"Taylor","year":"2018"},{"key":"10.1016\/j.ejmech.2024.117164_bib317","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Barredo Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.ejmech.2024.117164_bib318","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108734","article-title":"Current strategies to address data scarcity in artificial intelligence-based drug discovery: a comprehensive review","volume":"179","author":"Gangwal","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejmech.2024.117164_bib319","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.bushor.2018.03.007","article-title":"Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making","volume":"61","author":"Jarrahi","year":"2018","journal-title":"Bus. Horiz."}],"container-title":["European Journal of Medicinal Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0223523424010468?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0223523424010468?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T16:23:46Z","timestamp":1751387026000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0223523424010468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":318,"alternative-id":["S0223523424010468"],"URL":"https:\/\/doi.org\/10.1016\/j.ejmech.2024.117164","relation":{},"ISSN":["0223-5234"],"issn-type":[{"value":"0223-5234","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine","name":"articletitle","label":"Article Title"},{"value":"European Journal of Medicinal Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejmech.2024.117164","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"117164"}}