Artificial intelligence in drug discovery — what it is, where we stand and the path forward

Cireşan, D. C., Meier, U., Gambardella, L. M. & Schmidhuber, J. Deep, big, simple neural nets for handwritten digit recognition. Neural Comput. 22, 3207–3220 (2010).Article PubMed Google Scholar Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012) 1097–1105 (Curran Associates, Inc., 2012).Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).Article CAS PubMed Google Scholar Walters, W. P. & Barzilay, R. Critical assessment of AI in drug discovery. Expert. Opin. Drug Discov. 16, 937–947 (2021).Article CAS PubMed Google Scholar Rodriguez, A. et al. Unlocking the Potential of AI in Drug Discovery (Boston Consulting Group Report, 2023).Jayatunga, M. K. P., Xie, W., Ruder, L., Schulze, U. & Meier, C. AI in small-molecule drug discovery: a coming wave? Nat. Rev. Drug Discov. 21, 175–176 (2022).Article CAS PubMed Google Scholar Mullard, A. 2024 FDA approvals. Nat. Rev. Drug Discov. 24, 75–82 (2025).Article CAS PubMed Google Scholar Phares, S., Phillip, K. & Trusheim, M. Clinical development success rates for durable cell and gene therapies. Nat. Rev. Drug Discov. 24, 329–330 (2025).Article CAS PubMed Google Scholar Wouters, O. J. et al. Differential legal protections for biologics vs small-molecule drugs in the US. J. Am. Med. Assoc. 332, 2101–2108 (2024).Article CAS Google Scholar Jayatunga, M., Ayers, M., Bruens, L., Jayanth, D. & Meier, C. How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons. Drug Discov. Today 29, 104009 (2024).Article Google Scholar Lowe, D. AI drugs so far. In the Pipeline https://www.science.org/content/blog-post/ai-drugs-so-far (2024).Pitt, W. R. et al. Real-world applications and experiences of AI/ML deployment for drug discovery. J. Med. Chem. 68, 851–859 (2025).Article CAS PubMed Google Scholar Marshall, S. et al. Model-Informed drug discovery and development: current industry good practice and regulatory expectations and future perspectives. CPT Pharmacometrics Syst. Pharmacol. 8, 87–96 (2019).Article CAS PubMed PubMed Central Google Scholar Madhavan, S. & Shaywitz, D. A. AI: an essential tool for managing the burgeoning complexity of clinical development in pharmaceutical R&D. Drug Discov. Today 30, 104271 (2025).Article PubMed Google Scholar Wodak, S. J., Vajda, S., Lensink, M. F., Kozakov, D. & Bates, P. A. Critical assessment of methods for predicting the 3D structure of proteins and protein complexes. Annu. Rev. Biophys. 52, 183–206 (2023).Article CAS PubMed PubMed Central Google Scholar Vázquez Torres, S. et al. De novo designed proteins neutralize lethal snake venom toxins. Nature 639, 225–231 (2025).Article PubMed PubMed Central Google Scholar Du, H. et al. Targeting peptide antigens using a multiallelic MHC I-binding system. Nat. Biotechnol. 43, 1683–1693 (2024).Article PubMed PubMed Central Google Scholar De Jonghe, J. et al. A community effort to track commercial single-cell and spatial ’omic technologies and business trends. Nat. Biotechnol. 42, 1017–1023 (2024).Article PubMed Google Scholar Göller, A. H. et al. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades. Drug Discov. Today 25, 1702–1709 (2020).Article PubMed Google Scholar Scannell, J. W. et al. Predictive validity in drug discovery: what it is, why it matters and how to improve it. Nat. Rev. Drug Discov. 21, 915–931 (2022).Article CAS PubMed Google Scholar Clark, D. E. What has computer-aided molecular design ever done for drug discovery? Expert Opin. Drug Discov. 1, 103–110 (2006).Article CAS PubMed Google Scholar Bender, A. & Cortés-Ciriano, I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: ways to make an impact, and why we are not there yet. Drug Discov. Today 26, 511–524 (2021).Article CAS PubMed Google Scholar Paul, S. et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).Article CAS PubMed Google Scholar Dreiman, G. H. S. et al. Changing the HTS Paradigm: AI-driven iterative screening for hit finding. SLAS Discov. 26, 257–262 (2021).Article PubMed Google Scholar Morgan, P. et al. Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat. Rev. Drug Discov. 17, 167–181 (2018).Article CAS PubMed Google Scholar Fernando, K. et al. Achieving end-to-end success in the clinic: Pfizer’s learnings on R&D productivity. Drug Discov. Today 27, 697–704 (2022).Article CAS PubMed Google Scholar Wong, C. H., Siah, K. W. & Lo, A. W. Estimation of clinical trial success rates and related parameters. Biostatistics 20, 273–286 (2019).Article PubMed PubMed Central Google Scholar Minikel, E. V. et al. Refining the impact of genetic evidence on clinical success. Nature 629, 624–629 (2024).Article CAS PubMed PubMed Central Google Scholar Bender, A. & Cortes-Ciriano, I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discov. Today 26, 1040–1052 (2021).Article CAS PubMed PubMed Central Google Scholar Ortiz de Montellano, P. R. Cytochrome P450-activated prodrugs. Future Med. Chem. 5, 213–228 (2013).Article CAS PubMed PubMed Central Google Scholar Rodriguez-Antona, C. & Ingelman-Sundberg, M. Cytochrome P450 pharmacogenetics and cancer. Oncogene 25, 1679–1691 (2006).Article CAS PubMed Google Scholar Zevin, S. & Benowitz, N. L. Drug interactions with tobacco smoking. An update. Clin. Pharmacokinet. 36, 425–438 (1999).Article CAS PubMed Google Scholar Zimmermann, M. et al. Separating host and microbiome contributions to drug pharmacokinetics and toxicity. Science 363, eaat9931 (2019).Article CAS PubMed PubMed Central Google Scholar Safikhani, Z. et al. Revisiting inconsistency in large pharmacogenomic studies. F1000Research 5, 2333 (2016).Article PubMed PubMed Central Google Scholar Samad, S. S., Schwartz, J. M. & Francavilla, C. Functional selectivity of receptor tyrosine kinases regulates distinct cellular outputs. Front. Cell Dev. Biol. 11, 1348056 (2024).Article PubMed PubMed Central Google Scholar Zhang, Z., Pan, Q., Lu, M. & Zhao, B. Intermediate endpoints as surrogates for outcomes in cancer immunotherapy: a systematic review and meta-analysis of phase 3 trials. eClinicalMedicine 63, 102156 (2023).Article PubMed PubMed Central Google Scholar Nogales, C. et al. Network medicine-based unbiased disease modules for drug and diagnostic target identification in ROSopathies. Handb. Exp. Pharmacol. 264, 49–68 (2021).Article CAS PubMed Google Scholar Grotzinger, A. D. et al. Mapping the genetic landscape across 14 psychiatric disorders. Nature https://doi.org/10.1038/s41586-025-09820-3 (2025).Article PubMed PubMed Central Google Scholar Polishchuk, P. G., Madzhidov, T. I. & Varnek, A. Estimation of the size of drug-like chemical space based on GDB-17 data. J. Comput. Aided Mol. Des. 27, 675–679 (2013).Article CAS PubMed Google Scholar Balestriero, R. et al. Learning in high dimension always amounts to extrapolation. Preprint at https://arxiv.org/abs/2110.09485 (2021).Sheridan, R. P. Time-split cross-validation as a method for estimating the goodness of prospective prediction. J. Chem. Inf. Model. 53, 783–790 (2013).Article CAS PubMed Google Scholar Lee, K., Moldagulov, G. & Grzybowski, B. A. The fragility of bioactivity prediction: rigorous dataset splits expose the illusion of ML accuracy. Chem. Eur. J. 15, e71208 (2026).Article Google Scholar Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4, 100804 (2023).Article PubMed PubMed Central Google Scholar Rodgers, S., Glen, R. C. & Bender, A. Characterizing bitterness: identification of key structural features and development of a classification model. J. Chem. Inf. Model. 46, 569–576 (2006).Article CAS PubMed Google Scholar Fierro, F., Giorgetti, A., Carloni, P., Meyerhof, W. & Alfonso-Prieto, M. Dual binding mode of “bitter sugars” to their human bitter taste receptor target. Sci. Rep. 9, 8437 (2019).Article PubMed PubMed Central Google Scholar Huang, G., Lv, M., Hu, J., Huang, K. & Xu, H. Glycosylation and activities of natural products. Mini Rev. Med. Chem. 16, 1013–1016 (2016).Article CAS PubMed Google Scholar D’Amour, A. et al. Underspecification presents challenges for credibility in modern machine learning. J. Mach. Learn. Res. 23, 1–61 (2022). Google Scholar Bender, A. et al. Evaluation guidelines for machine learning tools in the chemical sciences. Nat. Rev. Chem. 6, 428–442 (2022).Article PubMed Google Scholar Church, K. & Kordoni, V. Emerging trends: SOTA-chasing. Nat. Lang. Eng. 28, 249–269 (2020).Article Google Scholar Peters, B., Brenner, S. E., Wang, E., Slonim, D. & Kann, M. G. Putting benchmarks in their rightful place: the heart of computational biology. PLoS Comput. Biol. 14, e1006494 (2018).Article PubMed PubMed Central Google Scholar Moult, J., Pedersen, J. T., Judson, R. & Fidelis, K. A large-scale experiment to assess protein structure prediction methods. Proteins 23, 2–5 (1995).Article Google Scholar Newman, J. et al. Practical aspects of the SAMPL challenge: providing an extensive experimental data set for the modeling community. J. Biomol. Screen. 14, 1245–1250 (2009).Article CAS PubMed Google Scholar Ackloo, S. et al. CACHE (Critical Assessment of Computational Hit-finding Experiments): a public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding. Nat. Rev. Chem. 6, 287–295 (2022).Article PubMed PubMed Central Google Scholar Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).Article CAS PubMed Google Scholar Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).Article CAS PubMed Google Scholar