Unknown causative features in drug activity prediction

Identify currently unknown causative molecular features that determine small-molecule activity against their cognate targets and incorporate these features into the representations used by AI models to improve predictive performance.

Background

In the discussion on feature relevance, the authors note that predictive features can correlate with but not cause the property of interest, and express concern that some causative features may be missing from models either because they are unknown or cannot be effectively computed. They illustrate related known but hard-to-quantify factors (e.g., ligand and pocket desolvation, induced-fit effects) that limit the accuracy of scoring functions, underscoring the need to discover and include missing causative determinants.