Aug 2026· Nature reviews. Drug discovery· 10 citations· 181 references
Medicine
TL;DR
Recommendations are provided for the development of AI in drug discovery with the aim of increasing its translational relevance, including benchmarking studies of AI tools in drug discovery need to move on from model validation and instead focus on their ability to improve decision making.
As a result of the integration of AI, both preclinical and clinical research are undergoing profound transformation, with justified hope that effective and safe therapies can finally be developed more rapidly and cost-efficiently.
O. Nayler, Martin H. Bolli· Therapeutische Umschau. Revu...· 0 citations
This article examines how the close integration of computational and medicinal chemistry coupled with a deep understanding of chemical shape and protein interactions enabled targeted computational molecular design to produce disproportionate gains in efficacy and selectivity.
G. McGaughey· Journal of Chemical Informat...· 0 citations
A critical review is needed to distinguish the stages in which AI can improve search, prioritization, and decision-making from those that remain limited by experimental validation, safety assessment, and clinical evidence generation.
J. Fernández-Martínez· Expert Opinion on Drug Disco...· 0 citations
How machine learning, deep learning, natural language processing, and related computational methods are being applied across the drug discovery process is reviewed, with particular attention to AlphaFold-based protein structure prediction, AI-supported virtual screening, generative chemistry, retrosynthetic planning, d...
Yue Peng· International Journal of Bio...· 0 citations
Simple Summary Only 4.1% of potential cancer therapeutics reach the clinic despite taking roughly fourteen years to develop at a cost of more than a billion USD. Large datasets and artificial intelligence (AI) are promising new tools to improve the odds. Cancer drug discovery produces enormous amounts of data related t...
Fakhar U. Singhera, J. Overhulse, Terrence M. Lee et al.· Cancers· 0 citations
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.