Towards Interpretable Models of Chemist Preferences for Human-in-the-Loop Assisted Drug Discovery

In recent years, there has been growing interest in leveraging human preferences for drug discovery to build models that capture chemists’ intuition for de novo molecular design, lead optimization, and prioritization for experimental validation. However, existing models derived from human preferences in chemistry are often black-boxes, lacking interpretability regarding how humans form their preferences. Enhancing transparency in human-in-the-loop learning is crucial to ensure that such approaches in drug discovery are not unduly affected by subjective bias, noise or inconsistency. Moreover, interpretability can promote the development and use of multi-user models in drug design projects, integrating multiple expert perspectives and insights into multi-objective optimization frameworks for de novo molecular design. This also allows for assigning more or less weight to experts based on their knowledge of specific properties. In this paper, we present a methodology for decomposing human preferences based on binary responses (like/dislike) to molecules essentially proposed by generative chemistry models, and inferring interpretable preference models that represent human reasoning. Our approach aims to bridge the gap between human-in-the-loop learning and user model interpretability in drug discovery applications, providing a transparent framework that elucidates how human judgments can shape molecular design outcomes.