Artificial Intelligence for Drug Discovery: Are We There Yet?
Catrin Hasselgren Tudor I. Oprea
Drug discovery is adapting to novel technologies such as data science,
informatics, and artificial intelligence (AI) to accelerate effective treatment
development while reducing costs and animal experiments. AI is transforming
drug discovery, as indicated by increasing interest from investors, industrial
and academic scientists, and legislators. Successful drug discovery requires
optimizing properties related to pharmacodynamics, pharmacokinetics, and
clinical outcomes. This review discusses the use of AI in the three pillars of
drug discovery: diseases, targets, and therapeutic modalities, with a focus on
small molecule drugs. AI technologies, such as generative chemistry, machine
learning, and multi-property optimization, have enabled several compounds to
enter clinical trials. The scientific community must carefully vet known
information to address the reproducibility crisis. The full potential of AI in
drug discovery can only be realized with sufficient ground truth and
appropriate human intervention at later pipeline stages.