Introduction: Artificial intelligence (AI) is exhibiting tremendous potential to reduce
the massive costs and long timescales of drug discovery. There are however important
challenges currently limiting the impact and scope of AI models.
Areas covered: In this perspective, the authors discuss a range of data issues (bias,
inconsistency, skewness, irrelevance, small size, high dimensionality), how they
challenge AI models, and which issue-specific mitigations have been effective. Next,
they point out the challenges faced by uncertainty quantification techniques aimed at
enhancing and trusting the predictions from these AI models. They also discuss how
conceptual errors, unrealistic benchmarks and performance misestimation can
confound the evaluation of models and thus their development. Lastly, the authors
explain how human bias, whether from AI experts or drug discovery experts,
constitutes another challenge that can be alleviated by gaining more prospective
experience.
Expert opinion: AI models are often developed to excel on retrospective benchmarks
unlikely to anticipate their prospective performance. As a result, only a few of these
models are ever reported to have prospective value (e.g. by discovering potent and
innovative drug leads for a therapeutic target). The authors have discussed what can go
wrong in practice with AI for drug discovery. We hope that this will help inform the
decisions of editors, funders investors and researchers working in this area.