Supporting Medicinal Chemists in Iterative Hypothesis Generation for Drug Target Identification

TL;DR

HAPPIER is an AI tool that helps medicinal chemists generate and validate hypotheses for drug target identification by integrating multiple criteria and supporting iterative exploration.

Contribution

We developed HAPPIER, an integrated AI system that enhances hypothesis generation for drug target discovery through multi-criteria support and iterative validation.

Findings

Increased number of high-confidence hypotheses generated.

Enhanced support for iterative hypothesis cycles.

Improved confidence in AI-generated suggestions.

Abstract

While drug discovery is vital for human health, the process remains inefficient. Medicinal chemists must navigate a vast protein space to identify target proteins that meet three criteria: physical and functional interactions, therapeutic impact, and docking potential. Prior approaches have provided fragmented support for each criterion, limiting the generation of promising hypotheses for wet-lab experiments. We present HAPPIER, an AI-powered tool that supports hypothesis generation with integrated multi-criteria support for target identification. HAPPIER enables medicinal chemists to 1) efficiently explore and verify proteins in a single integrated graph component showing multi-criteria satisfaction and 2) validate AI suggestions with domain knowledge. These capabilities facilitate iterative cycles of divergent and convergent thinking, essential for hypothesis generation. We evaluated HAPPIER with ten medicinal chemists, finding that it increased the number of high-confidence hypotheses and support for the iterative cycle, and further demonstrated the relationship between engaging in such cycles and confidence in outputs.