Challenges with the application and adoption of artificial intelligence for drug discovery

Abstract

Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges limiting the impact and scope of AI models. Typically, these models are evaluated on benchmarks that are unlikely to anticipate their prospective performance, which inadvertently misguides their development. Indeed, while all the developed models excel in a selected benchmark, only a small proportion of them are ultimately reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). Here we 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, we point out the challenges faced by uncertainty quantification techniques aimed at enhancing these AI models. We also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, we explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated with prospective studies.