Artificial Intelligence for Drug Discovery: Are We There Yet?

TL;DR

This paper reviews how artificial intelligence is transforming drug discovery by improving efficiency and success rates, especially in small molecule development, while highlighting challenges like reproducibility and the need for human oversight.

Contribution

It provides a comprehensive overview of AI applications across drug discovery stages, emphasizing recent advances and future challenges in the field.

Findings

AI enables several compounds to enter clinical trials.

Generative chemistry and multi-property optimization are key AI techniques.

Reproducibility remains a critical challenge in AI-driven drug discovery.

Abstract

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.