{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:16:59Z","timestamp":1793409419936,"version":"4.5.0"},"reference-count":147,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>Artificial intelligence (AI) is emerging as a valuable complementary tool in small-molecule drug discovery, augmenting traditional methodologies rather than replacing them. This review examines the evolution of AI from early rule-based systems to advanced deep learning, generative models, diffusion models, and autonomous agentic AI systems, highlighting their applications in target identification, hit discovery, lead optimization, and safety prediction. We present both successes and failures to provide a balanced perspective. Notable achievements include baricitinib (BenevolentAI\/Eli Lilly, an existing drug repurposed through AI-assisted analysis for COVID-19 and rheumatoid arthritis), halicin (MIT, preclinical antibiotic), DSP-1181 (Exscientia, discontinued after Phase I), and ISM001-055\/rentosertib (Insilico Medicine, positive Phase IIa results). However, several AI-assisted compounds have also faced challenges in clinical development. DSP-1181 was discontinued after Phase I, despite a favorable safety profile, highlighting that the acceleration of discovery timelines by AI does not guarantee clinical success. Despite progress, challenges such as data quality, model interpretability, regulatory hurdles, and ethical concerns persist. We provide practical insights for integrating AI into drug discovery workflows, emphasizing hybrid human-AI approaches and the emergence of agentic AI systems that can autonomously navigate discovery pipelines. A critical evaluation of current limitations and future opportunities reveals that while AI offers significant potential as a complementary technology, realistic expectations and careful implementation are crucial for delivering innovative therapeutics.<\/jats:p>","DOI":"10.3390\/ph18091271","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T10:35:17Z","timestamp":1756204517000},"page":"1271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0513-0336","authenticated-orcid":false,"given":"Sarfaraz K.","family":"Niazi","sequence":"first","affiliation":[{"name":"College of Pharmacy, University of Illinois, Chicago, IL 60612, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20420986251321704","DOI":"10.1177\/20420986251321704","article-title":"Artificial intelligence in drug development: Reshaping the therapeutic landscape","volume":"16","author":"Niazi","year":"2025","journal-title":"Ther. Adv. Drug. Saf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Niazi, S.K., and Mariam, Z. (2023). Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals, 17.","DOI":"10.3390\/ph17010022"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41587-024-02143-0","article-title":"A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models","volume":"43","author":"Ren","year":"2025","journal-title":"Nat. Biotechnol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1038\/s41573-023-00774-7","article-title":"Artificial intelligence for natural product drug discovery","volume":"22","author":"Mullowney","year":"2023","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.2147\/DDDT.S424991","article-title":"The Coming of Age of AI\/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives","volume":"17","author":"Niazi","year":"2023","journal-title":"Drug Des. Devel. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Niazi, S.K. (2025). Regulatory Perspectives for AI\/ML Implementation in Pharmaceutical GMP Environments. Pharmaceuticals, 18.","DOI":"10.3390\/ph18060901"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1038\/s43856-025-00910-x","article-title":"Regulating the AI-enabled ecosystem for human therapeutics","volume":"5","author":"Singh","year":"2025","journal-title":"Commun. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","article-title":"Innovation in the pharmaceutical industry: New estimates of R&D costs","volume":"47","author":"DiMasi","year":"2016","journal-title":"J. Health Econ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","article-title":"Drug discovery with explainable artificial intelligence","volume":"2","author":"Grisoni","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1038\/s42256-022-00465-9","article-title":"Dual use of artificial-intelligence-powered drug discovery","volume":"4","author":"Urbina","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Niazi, S.K. (2022). Molecular Biosimilarity\u2014An AI-Driven Paradigm Shift. Int. J. Mol. Sci., 23.","DOI":"10.20944\/preprints202208.0194.v1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1021\/acs.jnatprod.9b01285","article-title":"Natural products as sources of new drugs over the nearly four decades from 01\/1981 to 09\/2019","volume":"83","author":"Newman","year":"2020","journal-title":"J. Nat. Prod."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1038\/nrd3681","article-title":"Diagnosing the decline in pharmaceutical R&D efficiency","volume":"11","author":"Scannell","year":"2012","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/j.drudis.2011.09.007","article-title":"What makes a good drug target?","volume":"16","author":"Gashaw","year":"2011","journal-title":"Drug Discov. Today"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/nrd3368","article-title":"Impact of high-throughput screening in biomedical research","volume":"10","author":"Macarron","year":"2011","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","article-title":"Principles of early drug discovery","volume":"162","author":"Hughes","year":"2011","journal-title":"Br. J. Pharmacol."},{"key":"ref_17","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":"ref_18","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":"ref_19","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":"ref_20","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":"ref_21","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1016\/j.drudis.2020.11.037","article-title":"Artificial intelligence in drug discovery: What is realistic, what are illusions?","volume":"26","author":"Bender","year":"2021","journal-title":"Drug Discov. Today"},{"key":"ref_22","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_23","first-page":"14421","article-title":"Deep generative models for molecular design: A review","volume":"12","author":"Leach","year":"2021","journal-title":"Chem. Sci."},{"key":"ref_24","first-page":"1","article-title":"Comprehensive survey of graph neural networks","volume":"31","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1021\/acs.jcim.6b00740","article-title":"Protein-ligand scoring with convolutional neural networks","volume":"57","author":"Ragoza","year":"2017","journal-title":"J. Chem. Inf. Model"},{"key":"ref_27","first-page":"3036","article-title":"DeepSite: Protein-binding site predictor using 3D-convolutional neural networks","volume":"34","author":"Doerr","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"D1388","DOI":"10.1093\/nar\/gkaa971","article-title":"PubChem in 2021: New data content and improved web interfaces","volume":"49","author":"Kim","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_30","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":"ref_31","doi-asserted-by":"crossref","first-page":"eaap7885","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":"ref_32","unstructured":"Hoogeboom, E., Satorras, V.G., Vignac, C., and Welling, M. (2022, January 17\u201323). Equivariant Diffusion for Molecule Generation in 3D. Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, USA. Available online: https:\/\/proceedings.mlr.press\/v162\/hoogeboom22a.html."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.drudis.2014.12.004","article-title":"Active-learning strategies in computer-assisted drug discovery","volume":"20","author":"Reker","year":"2015","journal-title":"Drug Discov. Today"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1021\/acs.jcim.8b00839","article-title":"GuacaMol: Benchmarking models for de novo molecular design","volume":"59","author":"Brown","year":"2019","journal-title":"J. Chem. Inf. Model"},{"key":"ref_35","first-page":"e83390","article-title":"The Agentic Era: Why Biopharma Must Embrace Artificial Intelligence That Acts, Not Just Informs","volume":"17","author":"Lakhan","year":"2025","journal-title":"Cureus"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/194178b0","article-title":"Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients","volume":"194","author":"Hansch","year":"1962","journal-title":"Nature"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1021\/ci060117s","article-title":"On outliers and activity cliffs\u2014Why QSAR often disappoints","volume":"46","author":"Maggiora","year":"2006","journal-title":"J. Chem. Inf. Model"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/S0969-2126(00)00060-5","article-title":"Structure-based drug design: Progress, results and challenges","volume":"2","author":"Verlinde","year":"1994","journal-title":"Structure"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/0022-2836(82)90153-X","article-title":"A geometric approach to macromolecule-ligand interactions","volume":"161","author":"Kuntz","year":"1982","journal-title":"J. Mol. Biol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1002\/anie.200300626","article-title":"A planning strategy for diversity-oriented synthesis","volume":"43","author":"Burke","year":"2004","journal-title":"Angew. Chem. Int. Ed."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.drudis.2010.03.013","article-title":"Pharmacophore modeling and applications in drug discovery: Challenges and recent advances","volume":"15","author":"Yang","year":"2010","journal-title":"Drug Discov. Today"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1126\/science.166.3902.178","article-title":"Computer-assisted design of complex organic syntheses","volume":"166","author":"Corey","year":"1969","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1039\/b104620a","article-title":"Computer-aided organic synthesis","volume":"34","author":"Todd","year":"2005","journal-title":"Chem. Soc. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mayr, A., Klambauer, G., Unterthiner, T., and Hochreiter, S. (2016). DeepTox: Toxicity prediction using deep learning. Front. Environ. Sci., 3.","DOI":"10.3389\/fenvs.2015.00080"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1021\/ci500747n","article-title":"Deep neural nets as a method for quantitative structure-activity relationships","volume":"55","author":"Ma","year":"2015","journal-title":"J. Chem. Inf. Model"},{"key":"ref_48","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume":"28","author":"Duvenaud","year":"2015","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","article-title":"Molecular graph convolutions: Moving beyond fingerprints","volume":"30","author":"Kearnes","year":"2016","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1021\/acscentsci.9b00576","article-title":"Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction","volume":"5","author":"Schwaller","year":"2019","journal-title":"ACS Cent. Sci."},{"key":"ref_51","unstructured":"Wallach, I., Dzamba, M., and Heifets, A. (2015). AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-Based Drug Discovery. arXiv, Available online: https:\/\/arxiv.org\/abs\/1510.02855."},{"key":"ref_52","unstructured":"Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., and Brunskill, E. (2021). On the Opportunities and Risks of Foundation Models. arXiv, Available online: https:\/\/arxiv.org\/abs\/2108.07258."},{"key":"ref_53","first-page":"1708","article-title":"Deep learning for multi-omics data integration in cancer research","volume":"20","author":"Zeng","year":"2019","journal-title":"Brief Bioinform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine: Opportunities and challenges","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat. Med."},{"key":"ref_55","first-page":"271","article-title":"Text mining in drug discovery: Applications and opportunities","volume":"22","author":"Baker","year":"2017","journal-title":"Drug Discov. Today"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.clinthera.2015.12.001","article-title":"IBM Watson: How cognitive computing can be applied to big data challenges in life sciences research","volume":"38","author":"Chen","year":"2016","journal-title":"Clin. Ther."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.inffus.2018.09.012","article-title":"Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities","volume":"50","author":"Zitnik","year":"2018","journal-title":"Inf. Fusion."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e26726","DOI":"10.7554\/eLife.26726","article-title":"Systematic integration of biomedical knowledge prioritizes drugs for repurposing","volume":"6","author":"Himmelstein","year":"2017","journal-title":"eLife"},{"key":"ref_59","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":"ref_60","doi-asserted-by":"crossref","first-page":"146","DOI":"10.2174\/157340911795677602","article-title":"Molecular docking: A powerful approach for structure-based drug discovery","volume":"7","author":"Meng","year":"2011","journal-title":"Curr. Comput. Aided Drug Des."},{"key":"ref_61","first-page":"82","article-title":"Deep learning models for virtual screening: A critical review","volume":"63","author":"Parks","year":"2020","journal-title":"Curr. Opin. Struct. Biol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1021\/acs.jcim.8b00545","article-title":"Comparative assessment of scoring functions: The CASF-2016 update","volume":"59","author":"Su","year":"2019","journal-title":"J. Chem. Inf. Model"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1021\/acs.jcim.9b00387","article-title":"Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation","volume":"59","author":"Lim","year":"2019","journal-title":"J. Chem. Inf. Model"},{"key":"ref_64","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E. (2017, January 6\u201311). Neural Message Passing for Quantum Chemistry. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia. Available online: https:\/\/proceedings.mlr.press\/v70\/gilmer17a.html."},{"key":"ref_65","unstructured":"Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., and Pande, V. (2020, January 30). Strategies for Pre-Training Graph Neural Networks. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia. Available online: https:\/\/openreview.net\/forum?id=HJlWWJSFDH."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Dietterich, T.G. (2000, January 21\u201323). Ensemble Methods in Machine Learning. Proceedings of the International Workshop on Multiple Classifier Systems, Cagliari, Italy.","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model"},{"key":"ref_68","unstructured":"Ramsundar, B., Eastman, P., Walters, P., Pande, V., Leswing, K., and Wu, Z. (2019). Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More, O\u2019Reilly Media."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/S0960-894X(02)00244-5","article-title":"Drugs, leads, and drug-likeness: An analysis of some recently launched drugs","volume":"12","author":"Proudfoot","year":"2002","journal-title":"Bioorg. Med. Chem. Lett."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"51","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. Cheminform."},{"key":"ref_71","first-page":"1","article-title":"Optimization of molecules via deep reinforcement learning","volume":"9","author":"Zhou","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Daina, A., Michielin, O., and Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 7.","DOI":"10.1038\/srep42717"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.1039\/C8SC00148K","article-title":"Large-scale comparison of machine learning methods for drug target prediction on ChEMBL","volume":"9","author":"Mayr","year":"2018","journal-title":"Chem. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"31357","DOI":"10.1021\/jacs.4c11686","article-title":"Deep Lead Optimization: Leveraging Generative AI for Structural Modification","volume":"146","author":"Zhang","year":"2024","journal-title":"J. Am. Chem. Soc."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1038\/nrd1799","article-title":"Computer-based de novo design of drug-like molecules","volume":"4","author":"Schneider","year":"2005","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_77","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":"Wei","year":"2018","journal-title":"ACS Cent. Sci."},{"key":"ref_78","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding Variational Bayes. arXiv, Available online: https:\/\/arxiv.org\/abs\/1312.6114."},{"key":"ref_79","unstructured":"Guimaraes, G.L., Sanchez-Lengeling, B., Outeiral, C., Farias, P.L.C., and Aspuru-Guzik, A. (2017). Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models. arXiv, Available online: https:\/\/arxiv.org\/abs\/1705.10843."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_81","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"104413","DOI":"10.1016\/j.drudis.2025.104413","article-title":"Unraveling the potential of diffusion models in small-molecule generation","volume":"30","author":"Zhang","year":"2025","journal-title":"Drug Discov. Today"},{"key":"ref_83","unstructured":"Settles, B. (2009). Active Learning Literature Survey, Technical Report 1648; University of Wisconsin-Madison Computer Sciences. Available online: https:\/\/minds.wisconsin.edu\/handle\/1793\/60660."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_85","first-page":"1656","article-title":"A generative model for electron paths","volume":"10","author":"Bradshaw","year":"2019","journal-title":"Chem. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1039\/C8SC04228D","article-title":"A graph-convolutional neural network model for the prediction of chemical reactivity","volume":"10","author":"Coley","year":"2019","journal-title":"Chem. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1038\/nrd4609","article-title":"An analysis of the attrition of drug candidates from four major pharmaceutical companies","volume":"14","author":"Waring","year":"2015","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_88","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":"ref_89","unstructured":"Holzinger, A., Biemann, C., Pattichis, C.S., and Kell, D.B. (2017). What do We Need to Build Explainable AI Systems for the Medical Domain?. arXiv, Available online: https:\/\/arxiv.org\/abs\/1712.09923."},{"key":"ref_90","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":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_91","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv, Available online: https:\/\/arxiv.org\/abs\/1409.0473."},{"key":"ref_92","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-019-0151-1","article-title":"AqSolDB, a curated reference set of aqueous solubility and octanol\/water partition coefficient data","volume":"6","author":"Sorkun","year":"2019","journal-title":"Sci. Data"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1021\/acs.jcim.5b00642","article-title":"ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting","volume":"56","author":"Wang","year":"2016","journal-title":"J. Chem. Inf. Model."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.drudis.2016.02.015","article-title":"DILIrank: The largest reference drug list ranked by the risk for developing drug-induced liver injury in humans","volume":"21","author":"Chen","year":"2016","journal-title":"Drug Discov. Today"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with graph attention mechanisms","volume":"63","author":"Xiong","year":"2020","journal-title":"J. Med. Chem."},{"key":"ref_97","first-page":"1991","article-title":"In silico ADMET profiling: Progress and challenges","volume":"12","author":"Dang","year":"2020","journal-title":"Future Med. Chem."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"5302","DOI":"10.1021\/acs.molpharmaceut.8b00785","article-title":"Predicting Fraction Unbound in Human Plasma from Chemical Structure: Improved Accuracy in the Low Value Ranges","volume":"15","author":"Watanabe","year":"2018","journal-title":"Mol. Pharm."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"E4304","DOI":"10.1073\/pnas.1803294115","article-title":"Deep learning improves prediction of drug-drug and drug-food interactions","volume":"115","author":"Ryu","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"e30","DOI":"10.1016\/S0140-6736(20)30304-4","article-title":"Baricitinib as potential treatment for 2019-nCoV acute respiratory disease","volume":"395","author":"Richardson","year":"2020","journal-title":"Lancet"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1056\/NEJMoa2031994","article-title":"Baricitinib plus Remdesivir for Hospitalized Adults with COVID-19","volume":"384","author":"Kalil","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"ref_102","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":"ref_103","first-page":"1903","article-title":"Exscientia progress report: AI-designed molecules in the clinic","volume":"27","author":"Hopkins","year":"2022","journal-title":"Drug Discov. Today"},{"key":"ref_104","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":"ref_105","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/d41573-022-00025-1","article-title":"AI in small-molecule drug discovery: A coming wave?","volume":"21","author":"Jayatunga","year":"2022","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/S1473-3099(20)30132-8","article-title":"COVID-19: Combining antiviral and anti-inflammatory treatments","volume":"20","author":"Stebbing","year":"2020","journal-title":"Lancet Infect. Dis."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Gholap, A.D., Uddin, M.J., Faiyazuddin, M., Omri, A., Gowri, S., and Khalid, M. (2024). Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput. Biol. Med., 178.","DOI":"10.1016\/j.compbiomed.2024.108702"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nrd.2018.21","article-title":"Kinase inhibitors: The road ahead","volume":"17","author":"Ferguson","year":"2018","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1097\/BOR.0b013e3283378e6f","article-title":"Kinase inhibitors: A new approach to rheumatoid arthritis treatment","volume":"22","author":"Cohen","year":"2010","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11910-024-01354-x","article-title":"Artificial Intelligence and Multiple Sclerosis","volume":"24","author":"Amin","year":"2024","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"ref_111","first-page":"e1999","article-title":"Effect of ocrelizumab on vaccine responses in patients with multiple sclerosis","volume":"95","author":"Calkwood","year":"2020","journal-title":"Neurology"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1038\/nchembio.118","article-title":"Network pharmacology: The next paradigm in drug discovery","volume":"4","author":"Hopkins","year":"2008","journal-title":"Nat. Chem. Biol."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1038\/549445a","article-title":"The drug-maker\u2019s guide to the galaxy","volume":"549","author":"Mullard","year":"2017","journal-title":"Nature"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1038\/nrd.2018.168","article-title":"Drug repurposing: Progress, challenges and recommendations","volume":"18","author":"Pushpakom","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_115","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":"ref_116","doi-asserted-by":"crossref","unstructured":"Mouchlis, V.D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A.G., Aidinis, V., Lynch, I., Greco, D., and Melagraki, G. (2021). Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22041676"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1021\/ar500432k","article-title":"The chemical space project","volume":"48","author":"Reymond","year":"2015","journal-title":"Acc. Chem. Res."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1021\/acs.jcim.7b00622","article-title":"SCScore: Synthetic complexity learned from a reaction corpus","volume":"58","author":"Coley","year":"2018","journal-title":"J. Chem. Inf. Model"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","article-title":"Self-supervised learning for molecular representations","volume":"4","author":"Wang","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"ref_120","first-page":"6","article-title":"ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction","volume":"62","author":"Irwin","year":"2022","journal-title":"J. Chem. Inf. Model"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv, Available online: https:\/\/arxiv.org\/abs\/1906.02243.","DOI":"10.18653\/v1\/P19-1355"},{"key":"ref_122","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv, Available online: https:\/\/arxiv.org\/abs\/1503.02531."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1145\/3381831","article-title":"Green AI","volume":"63","author":"Schwartz","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"10856","DOI":"10.1021\/acs.chemrev.8b00803","article-title":"Quantum chemistry in the age of quantum computing","volume":"119","author":"Cao","year":"2019","journal-title":"Chem. Rev."},{"key":"ref_125","first-page":"031007","article-title":"The variational quantum eigensolver: A new paradigm for quantum chemistry","volume":"6","author":"McClean","year":"2016","journal-title":"Phys. Rev. X"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1162\/dint_r_00024","article-title":"FAIR principles: Interpretations and implementation","volume":"2","author":"Jacobsen","year":"2020","journal-title":"Data Intell."},{"key":"ref_128","first-page":"837","article-title":"MELLODDY: Federated learning for drug discovery","volume":"3","author":"Li","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"D1056","DOI":"10.1093\/nar\/gky1133","article-title":"Open Targets Platform: New developments and updates two years on","volume":"47","author":"Pierleoni","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_130","unstructured":"Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., and Leskovec, J. (2021). Therapeutics Data Commons: Machine learning datasets and tasks for drug discovery and development. arXiv, Available online: https:\/\/arxiv.org\/abs\/2102.09548."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1021\/ci100176x","article-title":"Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research","volume":"50","author":"Fourches","year":"2010","journal-title":"J. Chem. Inf. Model"},{"key":"ref_132","first-page":"847","article-title":"Time to change how we do QSAR","volume":"27","author":"Sheridan","year":"2013","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_133","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":"ref_134","doi-asserted-by":"crossref","first-page":"20141289","DOI":"10.1098\/rsif.2014.1289","article-title":"Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases","volume":"12","author":"Williams","year":"2015","journal-title":"J. R. Soc. Interface"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.1021\/acs.jcim.9b00628","article-title":"Graph convolutional neural networks for predicting drug-target interactions","volume":"59","author":"Torng","year":"2019","journal-title":"J. Chem. Inf. Model"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1002\/psp4.12049","article-title":"Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation","volume":"5","author":"Marshall","year":"2016","journal-title":"CPT Pharmacomet. Syst. Pharmacol."},{"key":"ref_137","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":"ref_138","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41467-019-13807-w","article-title":"De novo generation of hit-like molecules from gene expression signatures using artificial intelligence","volume":"11","author":"Baillif","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"eabb1655","DOI":"10.1126\/scitranslmed.abb1655","article-title":"Reproducibility in machine learning for health research: Still a ways to go","volume":"13","author":"McDermott","year":"2021","journal-title":"Sci. Transl. Med."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s13321-019-0393-0","article-title":"Randomized SMILES strings improve the quality of molecular generative models","volume":"11","author":"Johansson","year":"2019","journal-title":"J. Cheminform."},{"key":"ref_141","first-page":"155","article-title":"Rise of robot chemists sparks debate over AI patents","volume":"574","author":"Reardon","year":"2019","journal-title":"Nature"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/nrd.2017.232","article-title":"Automating drug discovery","volume":"17","author":"Schneider","year":"2018","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1002\/cpt.59","article-title":"From adaptive licensing to adaptive pathways: Delivering a flexible life-span approach to bring new drugs to patients","volume":"97","author":"Eichler","year":"2015","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_145","first-page":"1395","article-title":"Digital twins: The convergence of physical and virtual in healthcare","volume":"37","author":"Rashidi","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_146","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":"ref_147","doi-asserted-by":"crossref","first-page":"S55","DOI":"10.1038\/d41586-018-05267-x","article-title":"How artificial intelligence is changing drug discovery","volume":"557","author":"Fleming","year":"2018","journal-title":"Nature"}],"container-title":["Pharmaceuticals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8247\/18\/9\/1271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:59Z","timestamp":1760034779000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8247\/18\/9\/1271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"references-count":147,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["ph18091271"],"URL":"https:\/\/doi.org\/10.3390\/ph18091271","relation":{},"ISSN":["1424-8247"],"issn-type":[{"value":"1424-8247","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,26]]}}}