{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T23:29:39Z","timestamp":1791847779667,"version":"4.3.4"},"reference-count":135,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"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":[[2025,3]]},"DOI":"10.1016\/j.ejmech.2025.117269","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T20:21:36Z","timestamp":1736454096000},"page":"117269","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"special_numbering":"C","title":["Progress of machine learning in the application of small molecule druggability prediction"],"prefix":"10.1016","volume":"285","author":[{"given":"Junyao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianmei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taoyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weicheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejmech.2025.117269_bib1","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1016\/j.chembiol.2014.07.015","article-title":"Screening technologies for small molecule discovery: the state of the art","volume":"21","author":"Janzen","year":"2014","journal-title":"Chem. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib2","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. Inf."},{"key":"10.1016\/j.ejmech.2025.117269_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.ailsci.2022.100056","article-title":"Machine learning for small molecule drug discovery in academia and industry: ML for small molecules drug discovery","volume":"3","author":"Volkamer","year":"2023","journal-title":"Artificial Intelligence in the Life Sciences"},{"key":"10.1016\/j.ejmech.2025.117269_bib4","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baab017","article-title":"Drugmonizome and drugmonizome-ml: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning","author":"Kropiwnicki","year":"2021","journal-title":"Database"},{"key":"10.1016\/j.ejmech.2025.117269_bib5","doi-asserted-by":"crossref","first-page":"9442","DOI":"10.1021\/acs.jmedchem.8b00675","article-title":"Where do recent small molecule clinical development candidates come from?","volume":"61","author":"Brown","year":"2018","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.ejmech.2025.117269_bib6","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.drudis.2021.01.008","article-title":"Integration of AI and traditional medicine in drug discovery Drug","volume":"26","author":"Khan","year":"2021","journal-title":"Discov. Today"},{"key":"10.1016\/j.ejmech.2025.117269_bib7","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1039\/D1SC04471K","article-title":"Natural product drug discovery in the artificial intelligence era","volume":"13","author":"Sald\u00edvar-Gonz\u00e1lez","year":"2022","journal-title":"Chem. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib8","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2022.826044","article-title":"The use of artificial intelligence in complementary and alternative medicine: a systematic scoping review","volume":"13","author":"Chu","year":"2022","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2025.117269_bib9","first-page":"247","article-title":"Drug repositioning of herbal compounds via a machine-learning approach","volume":"20","author":"Kim","year":"2019","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2025.117269_bib10","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1021\/acs.accounts.8b00292","article-title":"Cellular uptake of nanoparticles versus small molecules: a matter of size","volume":"51","author":"Mosquera","year":"2018","journal-title":"Acc. Chem. Res."},{"key":"10.1016\/j.ejmech.2025.117269_bib11","first-page":"2018","article-title":"Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era","volume":"20","author":"Jing","year":"2018","journal-title":"AAPS J."},{"key":"10.1016\/j.ejmech.2025.117269_bib12","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1007\/s10462-021-10058-4","article-title":"Machine learning in drug discovery: a review","volume":"55","author":"Dara","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.ejmech.2025.117269_bib13","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.cell.2023.01.035","article-title":"From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment","volume":"186","author":"Swanson","year":"2023","journal-title":"Cell"},{"key":"10.1016\/j.ejmech.2025.117269_bib14","article-title":"Ligandability and druggability assessment via machine learning","volume":"13","author":"Di Palma","year":"2023","journal-title":"Wiles. Comput. Mol. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib15","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.2025.117269_bib16","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.cbpa.2019.10.003","article-title":"Machine learning for target discovery in drug development","volume":"56","author":"Rodrigues","year":"2020","journal-title":"Curr. Opin. Chem. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib17","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.3390\/ijms21103585","article-title":"Similarity-based methods and machine learning approaches for target prediction in early drug discovery: performance and scope","volume":"21","author":"Mathai","year":"2022","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib18","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1021\/acs.molpharmaceut.9b01248","article-title":"MEDICASCY: a machine learning approach for predicting small-molecule drug side effects, indications, efficacy, and modes of action","volume":"17","author":"Zhou","year":"2020","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2025.117269_bib19","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.1038\/s41598-022-08787-9","article-title":"Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning","volume":"12","author":"Thafar","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2025.117269_bib20","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1038\/s41467-019-12928-6","article-title":"A bayesian machine learning approach for drug target identification using diverse data types","volume":"10","author":"Madhukar","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejmech.2025.117269_bib21","doi-asserted-by":"crossref","first-page":"bbab159","DOI":"10.1093\/bib\/bbab159","article-title":"Utilizing graph machine learning within drug discovery and development","volume":"22","author":"Gaudelet","year":"2021","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2025.117269_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2022.103366","article-title":"Trends in small molecule drug properties: a developability molecule assessment perspective","volume":"27","author":"Agarwal","year":"2022","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2025.117269_bib23","first-page":"381","article-title":"Machine learning algorithms - a review","volume":"9","author":"Mahesh","year":"2020","journal-title":"Int. J. Sci. Res."},{"key":"10.1016\/j.ejmech.2025.117269_bib24","doi-asserted-by":"crossref","first-page":"e166","DOI":"10.1371\/journal.pcbi.0030116","article-title":"Machine learning and its applications to biology","volume":"3","author":"Tarca","year":"2007","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib25","series-title":"Data Analytics in Bioinformatics: a Machine Learning Perspective","first-page":"35","article-title":"Introduction to unsupervised learning in bioinformatics","author":"Satpathy","year":"2021"},{"key":"10.1016\/j.ejmech.2025.117269_bib26","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1002\/jcp.24662","article-title":"Big data bioinformatics","volume":"229","author":"Greene","year":"2014","journal-title":"J. Cell. Physiol."},{"key":"10.1016\/j.ejmech.2025.117269_bib27","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2025.117269_bib28","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1093\/bib\/bbaa043","article-title":"Application of deep learning methods in biological networks","volume":"22","author":"Jin","year":"2021","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2025.117269_bib29","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1021\/acs.molpharmaceut.5b00982","article-title":"Applications of deep learning in biomedicine","volume":"13","author":"Mamoshina","year":"2016","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2025.117269_bib30","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1038\/nbt.4233","article-title":"Deep learning in biomedicine","volume":"36","author":"Wainberg","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.ejmech.2025.117269_bib31","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"issue":"2023","key":"10.1016\/j.ejmech.2025.117269_bib32","doi-asserted-by":"crossref","first-page":"9062","DOI":"10.1021\/acs.iecr.3c00722","article-title":"Transfer learning approach to develop natural molecules with specific flavor requirements","volume":"62","author":"Queiroz","year":"2023","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.ejmech.2025.117269_bib33","doi-asserted-by":"crossref","first-page":"4462","DOI":"10.1021\/acs.molpharmaceut.7b00578","article-title":"Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets","volume":"14","author":"Korotcov","year":"2017","journal-title":"Mol. pharmaceutics."},{"key":"10.1016\/j.ejmech.2025.117269_bib34","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nrg3920","article-title":"Machine learning applications in genetics and genomics","volume":"16","author":"Libbrecht","year":"2015","journal-title":"Nat. Rev. Genet."},{"key":"10.1016\/j.ejmech.2025.117269_bib35","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1111\/cbdd.14057","article-title":"Machine learning approaches and their applications in drug discovery and design","volume":"100","author":"Priya","year":"2022","journal-title":"Chem. Biol. Drug Des."},{"key":"10.1016\/j.ejmech.2025.117269_bib36","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1053\/j.semdp.2023.02.002","article-title":"Artificial intelligence and machine learning overview in pathology & laboratory medicine: a general review of data preprocessing and basic supervised concepts","volume":"40","author":"Albahra","year":"2023","journal-title":"Semin. Diagn. Pathol."},{"key":"10.1016\/j.ejmech.2025.117269_bib37","doi-asserted-by":"crossref","first-page":"80698","DOI":"10.1109\/ACCESS.2024.3406748","article-title":"Chronic diseases prediction using machine learning with data preprocessing handling: a critical review","volume":"12","author":"Ramadhan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.ejmech.2025.117269_bib38","doi-asserted-by":"crossref","first-page":"37177","DOI":"10.1007\/s11042-023-15087-5","article-title":"Data preprocessing techniques: emergence and selection towards machine learning models-a practical review using HPA dataset","volume":"82","author":"Mallikharjuna Rao","year":"2023","journal-title":"Multimed. Tool. Appl."},{"key":"10.1016\/j.ejmech.2025.117269_bib39","article-title":"Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening","volume":"14","author":"Chen","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2025.117269_bib40","doi-asserted-by":"crossref","first-page":"8860","DOI":"10.3390\/app14198860","article-title":"Bias in machine learning: a literature review","volume":"14","author":"Mavrogiorgos","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib41","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1038\/s41589-024-01679-1","article-title":"Machine learning in preclinical drug discovery","volume":"20","author":"Catacutan","year":"2024","journal-title":"Nat. Chem. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib42","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s11030-022-10590-7","article-title":"Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches","volume":"28","author":"Das","year":"2024","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2025.117269_bib43","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.cjche.2022.04.004","article-title":"Hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library","volume":"52","author":"Zhang","year":"2022","journal-title":"Chin. J. Chem. Eng."},{"key":"10.1016\/j.ejmech.2025.117269_bib44","doi-asserted-by":"crossref","first-page":"6395","DOI":"10.1038\/s41467-023-41948-6","article-title":"A systematic study of key elements underlying molecular property prediction","volume":"14","author":"Deng","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejmech.2025.117269_bib45","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1039\/D2CC05440J","article-title":"Photoaffinity labelling-based chemoproteomic strategy identifies PEBP1 as the target of ethyl gallate against macrophage activation","volume":"59","author":"Yu","year":"2022","journal-title":"Chem. Commun."},{"key":"10.1016\/j.ejmech.2025.117269_bib46","doi-asserted-by":"crossref","first-page":"3787","DOI":"10.1002\/anie.201411511","article-title":"(\u2212)-Englerin A is a potent and selective activator of TRPC4 and TRPC5 calcium channels","volume":"54","author":"Akbulut","year":"2015","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.ejmech.2025.117269_bib47","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/s41392-023-01381-z","article-title":"AlphaFold2 and its applications in the fields of biology and medicine","volume":"8","author":"Yang","year":"2023","journal-title":"Signal Transduct. Targeted Ther."},{"key":"10.1016\/j.ejmech.2025.117269_bib48","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"10.1016\/j.ejmech.2025.117269_bib49","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/bioinformatics\/btx731","article-title":"DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches","volume":"34","author":"Olayan","year":"2018","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2025.117269_bib50","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.3390\/molecules23092208","article-title":"Machine learning for drug-target interaction prediction","volume":"23","author":"Chen","year":"2018","journal-title":"Molecules"},{"key":"10.1016\/j.ejmech.2025.117269_bib51","article-title":"Application of machine learning for drug-target interaction prediction","volume":"12","author":"Xu","year":"2021","journal-title":"Front. Genet."},{"key":"10.1016\/j.ejmech.2025.117269_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.sbi.2021.102327","article-title":"Graph neural network approaches for drug-target interactions","volume":"73","author":"Zhang","year":"2022","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib53","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ymeth.2021.10.007","article-title":"Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions","volume":"198","author":"Jung","year":"2022","journal-title":"Methods"},{"key":"10.1016\/j.ejmech.2025.117269_bib54","article-title":"A machine learning approach for drug-target interaction prediction using wrapper feature selection and class balancing","volume":"39","author":"Redkar","year":"2022","journal-title":"Mol. Inform."},{"key":"10.1016\/j.ejmech.2025.117269_bib55","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","article-title":"Deep-learning-based drug-target interaction prediction","volume":"16","author":"Wen","year":"2017","journal-title":"J. Proteome Res."},{"key":"10.1016\/j.ejmech.2025.117269_bib56","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1021\/acsomega.0c05377","article-title":"A machine learning-based biological drug-target interaction prediction method for a tripartite heterogeneous network","volume":"6","author":"Zheng","year":"2021","journal-title":"ACS Omega"},{"key":"10.1016\/j.ejmech.2025.117269_bib57","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.sbi.2017.10.010","article-title":"Protein structure-based drug design: from docking to molecular dynamics","volume":"48","author":"\u015aled\u017a","year":"2018","journal-title":"Curr. Opin. Struct. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib58","doi-asserted-by":"crossref","first-page":"782","DOI":"10.3389\/fchem.2019.00782","article-title":"Comparison study of computational prediction tools for drug-Target binding affinities","volume":"7","author":"Thafar","year":"2019","journal-title":"Front. Chem."},{"key":"10.1016\/j.ejmech.2025.117269_bib59","doi-asserted-by":"crossref","first-page":"5545","DOI":"10.1093\/bioinformatics\/btaa1005","article-title":"DeepPurpose: a deep learning library for drug-target interaction prediction","volume":"36","author":"Huang","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2025.117269_bib60","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"Venkatesh, GraphDTA: predicting drug-target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2020","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ejmech.2025.117269_bib61","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/bib\/bbz157","article-title":"Machine learning approaches and databases for prediction of drug-target interaction: a survey paper","volume":"22","author":"Bagherian","year":"2021","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2025.117269_bib62","first-page":"1","article-title":"Drug-target interaction predication via multi-channel graph neural networks","volume":"23","author":"Li","year":"2022","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2025.117269_bib63","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1080\/07391102.2021.2017350","article-title":"DFT analysis and in silico exploration of drug-likeness, toxicity prediction, bioactivity score, and chemical reactivity properties of the urolithins","volume":"41","author":"Hussein","year":"2023","journal-title":"J. Biomol. Struct. Dyn."},{"key":"10.1016\/j.ejmech.2025.117269_bib64","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-78449-1","article-title":"Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt","volume":"10","author":"El-Attar","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2025.117269_bib65","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2022.971369","article-title":"A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors","volume":"13","author":"Ai","year":"2022","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2025.117269_bib66","doi-asserted-by":"crossref","first-page":"7860","DOI":"10.1038\/s41598-022-11925-y","article-title":"QSAR study of phenolic compounds and their anti-DPPH radical activity by discriminant analysis","volume":"12","author":"Lu","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2025.117269_bib67","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1021\/acs.jcim.0c00864","article-title":"Comparison of chemical structure and cell morphology information for multitask bioactivity predictions","volume":"61","author":"Trapotsi","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib90","first-page":"1561","article-title":"Equivariant graph neural networks for toxicity prediction","volume":"36","author":"Cremer","year":"2023","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.ejmech.2025.117269_bib69","doi-asserted-by":"crossref","first-page":"3724","DOI":"10.1021\/acs.jpclett.1c00045","article-title":"Machine learning prediction of allosteric drug activity from molecular dynamics","volume":"12","author":"Marchetti","year":"2021","journal-title":"J. Phys. Chem. Lett."},{"key":"10.1016\/j.ejmech.2025.117269_bib70","doi-asserted-by":"crossref","first-page":"48139","DOI":"10.1021\/acsomega.2c06174","article-title":"Machine learning-assisted prediction of the biological activity of aromatase inhibitors and data mining to explore similar compounds","volume":"7","author":"Ishfaq","year":"2022","journal-title":"ACS Omega"},{"key":"10.1016\/j.ejmech.2025.117269_bib71","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1007\/s11030-022-10465-x","article-title":"Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset","volume":"27","author":"Deng","year":"2023","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2025.117269_bib72","doi-asserted-by":"crossref","first-page":"2948","DOI":"10.1021\/acs.jcim.3c00308","article-title":"Building machine learning small molecule melting points and solubility models using CCDC melting points dataset","volume":"63","author":"Zhu","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib73","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1186\/s13321-023-00752-6","article-title":"Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models","volume":"15","author":"Tayyebi","year":"2023","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2025.117269_bib74","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1039\/D4DD00065J","article-title":"A machine learning approach for the prediction of aqueous solubility of pharmaceuticals: a comparative model and dataset analysis","volume":"3","author":"Ghanavati","year":"2024","journal-title":"Digital. Discovery"},{"key":"10.1016\/j.ejmech.2025.117269_bib75","doi-asserted-by":"crossref","first-page":"3236","DOI":"10.1021\/acsomega.2c06702","article-title":"Attention-based graph neural network for molecular solubility prediction","volume":"8","author":"Ahmad","year":"2023","journal-title":"ACS Omega"},{"key":"10.1016\/j.ejmech.2025.117269_bib68","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010029","article-title":"Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs","volume":"18","author":"Periwal","year":"2022","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib76","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1093\/bioinformatics\/bty070","article-title":"WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest","volume":"34","author":"Wu","year":"2018","journal-title":"Bioinformatics. Bioinformatics."},{"key":"10.1016\/j.ejmech.2025.117269_bib77","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1021\/acs.jcim.8b00348","article-title":"Dissecting machine-learning prediction of molecular activity: is an applicability domain needed for quantitative structure-activity Relationship models based on deep neural networks?","volume":"59","author":"Liu","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib78","doi-asserted-by":"crossref","first-page":"11086","DOI":"10.1021\/acsomega.1c01266","article-title":"Siamese recurrent neural network with a self-attention mechanism for bioactivity prediction","volume":"6","author":"Fern\u00e1ndez-Llaneza","year":"2021","journal-title":"ACS Omega"},{"key":"10.1016\/j.ejmech.2025.117269_bib79","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0204644","article-title":"Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach","volume":"13","author":"Egieyeh","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2025.117269_bib80","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1109\/TCBB.2020.2964203","article-title":"Exploring molecular descriptors and fingerprints to predict mTOR kinase inhibitors using machine learning techniques","volume":"18","author":"Kumari","year":"2021","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinf"},{"key":"10.1016\/j.ejmech.2025.117269_bib81","doi-asserted-by":"crossref","DOI":"10.1002\/minf.202200133","article-title":"MolPredictX: online biological activity predictions by machine learning models","volume":"41","author":"Tullius Scotti","year":"2022","journal-title":"Mol. Inform."},{"issue":"2021","key":"10.1016\/j.ejmech.2025.117269_bib82","article-title":"Identification of active molecules against Mycobacterium tuberculosis through machine learning","volume":"22","author":"Ye","year":"2021","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.ejmech.2025.117269_bib83","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-019-0407-y","article-title":"Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction","volume":"12","author":"Withnall","year":"2022","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2025.117269_bib84","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2022.1087273","article-title":"A machine learning-based approach to ER\u03b1 bioactivity and drug ADMET prediction","volume":"13","author":"An","year":"2023","journal-title":"Front. Genet."},{"key":"10.1016\/j.ejmech.2025.117269_bib85","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1007\/s11030-023-10649-z","article-title":"A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints","volume":"28","author":"Wu","year":"2023","journal-title":"Mol. Divers."},{"key":"10.1016\/j.ejmech.2025.117269_bib86","doi-asserted-by":"crossref","first-page":"5957","DOI":"10.1021\/acs.jcim.0c00565","article-title":"Adding stochastic negative examples into machine learning improves molecular bioactivity prediction","volume":"60","author":"C\u00e1ceres","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib87","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s42485-023-00124-6","article-title":"Transformer neural network for protein-specific drug discovery and validation using QSAR","volume":"14","author":"Amine","year":"2023","journal-title":"J. Pro. Proteom."},{"key":"10.1016\/j.ejmech.2025.117269_bib88","doi-asserted-by":"crossref","first-page":"1691","DOI":"10.1021\/acs.jcim.0c01294","article-title":"GGL-Tox: geometric graph learning for toxicity prediction","volume":"61","author":"Jiang","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib89","doi-asserted-by":"crossref","first-page":"880","DOI":"10.3389\/fphar.2017.00880","article-title":"ToxiM: a toxicity prediction tool for small molecules developed using machine learning and chemoinformatics approaches","volume":"8","author":"Sharma","year":"2017","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2025.117269_bib91","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106524","article-title":"The prediction of molecular toxicity based on BiGRU and GraphSAGE","volume":"153","author":"Liu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejmech.2025.117269_bib92","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1186\/s13321-021-00570-8","article-title":"Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network","volume":"13","author":"Chen","year":"2021","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2025.117269_bib93","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1021\/acs.jcim.9b00713","article-title":"Image based liver toxicity prediction","volume":"60","author":"Asilar","year":"2020","journal-title":"J. Chem. Inf. Model."},{"issue":"2019","key":"10.1016\/j.ejmech.2025.117269_bib94","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1021\/acsomega.8b03173","article-title":"Efficient toxicity prediction via simple features using shallow neural networks and decision trees","volume":"4","author":"Karim","year":"2019","journal-title":"ACS Omega"},{"key":"10.1016\/j.ejmech.2025.117269_bib95","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.xphs.2023.10.035","article-title":"Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning","volume":"113","author":"Gruber","year":"2024","journal-title":"J. Pharmaceut. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib96","doi-asserted-by":"crossref","first-page":"4603","DOI":"10.1021\/acs.jcim.0c00568","article-title":"Optimizing pharmacokinetic property prediction based on integrated datasets and a deep learning approach","volume":"60","author":"Wang","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib97","article-title":"Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling","volume":"15","author":"Wu","year":"2024","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.ejmech.2025.117269_bib98","series-title":"2024 7th International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China","first-page":"26","article-title":"Evaluation of machine learning models for aqueous solubility prediction in drug discovery","author":"Xue","year":"2024"},{"key":"10.1016\/j.ejmech.2025.117269_bib99","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2023.103273","article-title":"Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent","volume":"49","author":"Alanazi","year":"2023","journal-title":"Case Stud. Therm. Eng."},{"key":"10.1016\/j.ejmech.2025.117269_bib100","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ymeth.2020.06.016","article-title":"Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition","volume":"180","author":"Raschka","year":"2020","journal-title":"Methods"},{"key":"10.1016\/j.ejmech.2025.117269_bib101","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1111\/cbdd.13750","article-title":"The rise of deep learning and transformations in bioactivity prediction power of molecular modeling tools","volume":"98","author":"Bule","year":"2021","journal-title":"Chem. Biol. Drug Des."},{"key":"10.1016\/j.ejmech.2025.117269_bib102","first-page":"17","article-title":"Deep learning and its applications in biomedicine","volume":"16","author":"Cao","year":"2018","journal-title":"Dev. Reprod. Biol."},{"key":"10.1016\/j.ejmech.2025.117269_bib103","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1093\/bioinformatics\/bty070","article-title":"WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest","volume":"34","author":"Wu","year":"2018","journal-title":"Bioinformatics. Bioinformatics."},{"issue":"2022","key":"10.1016\/j.ejmech.2025.117269_bib104","article-title":"MLP-based regression prediction model cor compound bioactivity","volume":"10","author":"Qin","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"2018","key":"10.1016\/j.ejmech.2025.117269_bib105","article-title":"Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach","volume":"13","author":"Egieyeh","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.ejmech.2025.117269_bib106","doi-asserted-by":"crossref","DOI":"10.1002\/anie.202317901","article-title":"Using machine learning to predict the antibacterial activity of ruthenium complexes","volume":"63","author":"Orsi","year":"2024","journal-title":"Angew. Chem."},{"issue":"2021","key":"10.1016\/j.ejmech.2025.117269_bib107","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/acs.accounts.0c00699","article-title":"Applications of Deep learning in molecule generation and molecular property prediction","volume":"54","author":"Walters","year":"2021","journal-title":"Acc. Chem. Res."},{"issue":"2021","key":"10.1016\/j.ejmech.2025.117269_bib108","doi-asserted-by":"crossref","first-page":"3932","DOI":"10.1038\/s41467-021-24150-4","article-title":"Bioactivity descriptors for uncharacterized chemical compounds","volume":"12","author":"Bertoni","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejmech.2025.117269_bib109","doi-asserted-by":"crossref","DOI":"10.1002\/cphc.202200255","article-title":"An ensemble structure and physicochemical (SPOC) descriptor for machine-learning prediction of chemical reaction and molecular properties","volume":"23","author":"Yang","year":"2022","journal-title":"ChemPhysChem"},{"key":"10.1016\/j.ejmech.2025.117269_bib110","doi-asserted-by":"crossref","first-page":"6395","DOI":"10.1038\/s41467-023-41948-6","article-title":"A systematic study of key elements underlying molecular property prediction","volume":"14","author":"Deng","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejmech.2025.117269_bib111","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s13321-023-00708-w","article-title":"Exploring QSAR models for activity-cliff prediction","volume":"15","author":"Dablander","year":"2023","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2025.117269_bib112","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13321-022-00676-7","article-title":"Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity","volume":"15","author":"Tamura","year":"2023","journal-title":"J. Cheminf."},{"key":"10.1016\/j.ejmech.2025.117269_bib113","doi-asserted-by":"crossref","first-page":"987","DOI":"10.2174\/1568026618666180727152557","article-title":"Applications of machine learning methods in drug toxicity prediction","volume":"18","author":"Zhang","year":"2018","journal-title":"Curr. Top. Med. Chem."},{"key":"10.1016\/j.ejmech.2025.117269_bib114","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.2025.117269_bib115","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1080\/10408444.2024.2386260","article-title":"Molecular similarity in chemical informatics and predictive toxicity modeling: from quantitative read-across (q-RA) to quantitative read-across structure-activity relationship (q-RASAR) with the application of machine learning","volume":"54","author":"Banerjee","year":"2024","journal-title":"Crit. Rev. Toxicol."},{"key":"10.1016\/j.ejmech.2025.117269_bib116","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1093\/toxsci\/kfy152","article-title":"Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility","volume":"165","author":"Luechtefeld","year":"2018","journal-title":"Toxicol. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib117","doi-asserted-by":"crossref","DOI":"10.1016\/j.tox.2024.153824","article-title":"Quantitative read-across structure-activity relationship (q-RASAR): a novel approach to estimate the subchronic oral safety (NOAEL) of diverse organic chemicals in rats","volume":"505","author":"Ghosh","year":"2024","journal-title":"Toxicology"},{"key":"10.1016\/j.ejmech.2025.117269_bib118","doi-asserted-by":"crossref","DOI":"10.1016\/j.aquatox.2024.107136","article-title":"Elucidation of molecular mechanisms involved in tadpole toxicity employing QSTR and q-RASAR approach","volume":"277","author":"Khan","year":"2024","journal-title":"Aquat. Toxicol."},{"key":"10.1016\/j.ejmech.2025.117269_bib119","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejmech.2024.116628","article-title":"An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization","volume":"275","author":"Zhai","year":"2024","journal-title":"Eur. J. Med. Chem."},{"issue":"2023","key":"10.1016\/j.ejmech.2025.117269_bib120","doi-asserted-by":"crossref","first-page":"4908","DOI":"10.1038\/s41598-023-31169-8","article-title":"Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations","volume":"13","author":"Sharma","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejmech.2025.117269_bib121","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1002\/psp4.12870","article-title":"Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction","volume":"11","author":"Gill","year":"2022","journal-title":"CPT Pharmacometrics Syst. Pharmacol."},{"key":"10.1016\/j.ejmech.2025.117269_bib122","doi-asserted-by":"crossref","first-page":"4520","DOI":"10.1021\/acs.molpharmaceut.1c00718","article-title":"Greene, Machine learning models for human in vivo pharmacokinetic parameters with in-house validation","volume":"18","author":"Miljkovi\u0107","year":"2021","journal-title":"Mol. Pharm."},{"key":"10.1016\/j.ejmech.2025.117269_bib123","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.jconrel.2022.11.014","article-title":"Application of machine learning techniques to the analysis and prediction of drug pharmacokinetics","volume":"352","author":"Ota","year":"2022","journal-title":"J. Contr. Release"},{"key":"10.1016\/j.ejmech.2025.117269_bib124","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.2147\/IJN.S344208","article-title":"Predicting nanoparticle delivery to tumors using machine learning and artificial intelligence approaches","volume":"17","author":"Lin","year":"2022","journal-title":"Int. J. Nanomed."},{"key":"10.1016\/j.ejmech.2025.117269_bib125","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.jconrel.2023.07.040","article-title":"An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice","volume":"361","author":"Chou","year":"2023","journal-title":"J. Contr. Release"},{"key":"10.1016\/j.ejmech.2025.117269_bib126","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/toxsci\/kfac101","article-title":"Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling","volume":"191","author":"Chou","year":"2023","journal-title":"Toxicol. Sci."},{"key":"10.1016\/j.ejmech.2025.117269_bib127","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1038\/s42256-021-00357-4","article-title":"Deep learning prediction of patient response time course from early data via neural-pharmacokinetic\/pharmacodynamic modelling","volume":"3","author":"Lu","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.ejmech.2025.117269_bib128","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.ejmech.2025.117269_bib129","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.ejpb.2020.12.001","article-title":"An integrated computational methodology with data-driven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design","volume":"158","author":"Gao","year":"2021","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"10.1016\/j.ejmech.2025.117269_bib130","doi-asserted-by":"crossref","first-page":"4893","DOI":"10.1021\/acs.jcim.9b00460","article-title":"Prediction of Oral Bioavailability in Rats: transferring insights from in vitro correlations to (deep) machine learning models using in silico model outputs and chemical structure parameters","volume":"59","author":"Schneckener","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib131","doi-asserted-by":"crossref","DOI":"10.1016\/j.drudis.2024.103946","article-title":"Transfer learning empowers accurate pharmacokinetics prediction of small samples","volume":"29","author":"Guo","year":"2024","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.ejmech.2025.117269_bib132","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1038\/s41467-021-23720-w","article-title":"Algebraic graph-assisted bidirectional transformers for molecular property prediction","volume":"12","author":"Chen","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.ejmech.2025.117269_bib133","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1021\/acs.molpharmaceut.8b00816","article-title":"An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction","volume":"16","author":"Ye","year":"2019","journal-title":"Mol. Pharm."},{"issue":"2020","key":"10.1016\/j.ejmech.2025.117269_bib134","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s00894-020-04385-6","article-title":"Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?","volume":"26","author":"Agoni","year":"2020","journal-title":"J. Mol. Model."},{"key":"10.1016\/j.ejmech.2025.117269_bib135","doi-asserted-by":"crossref","first-page":"7189","DOI":"10.1021\/acs.jcim.3c01429","article-title":"ChatGPT in Drug Discovery: a case study on anticocaine addiction drug development with chatbots","volume":"63","author":"Wang","year":"2023","journal-title":"J. Chem. Inf. Model."}],"container-title":["European Journal of Medicinal Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0223523425000340?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0223523425000340?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T18:03:43Z","timestamp":1740247423000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0223523425000340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":135,"alternative-id":["S0223523425000340"],"URL":"https:\/\/doi.org\/10.1016\/j.ejmech.2025.117269","relation":{},"ISSN":["0223-5234"],"issn-type":[{"value":"0223-5234","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Progress of machine learning in the application of small molecule druggability prediction","name":"articletitle","label":"Article Title"},{"value":"European Journal of Medicinal Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejmech.2025.117269","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 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":"117269"}}