Artificial Intelligence for Drug Discovery: Are We There Yet?
It addresses the challenge of improving efficiency and reproducibility in drug discovery for pharmaceutical researchers and developers, but is incremental as a review of existing progress.
This review examines the application of artificial intelligence (AI) in drug discovery, highlighting its role in accelerating treatment development and reducing costs, with AI technologies enabling several compounds to enter clinical trials.
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.