Data-centric challenges with the application and adoption of artificial intelligence for drug discovery

TL;DR

This paper discusses the various data-centric challenges in applying AI to drug discovery, including data quality issues, evaluation pitfalls, and human biases, and suggests mitigation strategies to improve AI model reliability and prospective utility.

Contribution

It provides a comprehensive overview of data-related challenges in AI-driven drug discovery and highlights effective mitigation approaches and the importance of prospective validation.

Findings

Data issues like bias and high dimensionality hinder AI performance.

Uncertainty quantification techniques face challenges in reliable prediction.

Many AI models lack prospective validation and real-world applicability.

Abstract

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.