Interpreting GFlowNets for Drug Discovery: Extracting Actionable Insights for Medicinal Chemistry

TL;DR

This paper introduces an interpretability framework for GFlowNets in drug discovery, revealing how internal decision processes relate to chemical properties and enabling transparent, controllable molecular design.

Contribution

It presents a novel interpretability approach for SynFlowNet, combining saliency, autoencoders, and motif probes to elucidate the chemical logic within GFlowNets.

Findings

Atomic environments influence reward and structural changes affect outcomes.

Latent factors correspond to physicochemical properties.

Functional groups are explicitly encoded and linearly decodable.

Abstract

Generative Flow Networks, or GFlowNets, offer a promising framework for molecular design, but their internal decision policies remain opaque. This limits adoption in drug discovery, where chemists require clear and interpretable rationales for proposed structures. We present an interpretability framework for SynFlowNet, a GFlowNet trained on documented chemical reactions and purchasable starting materials that generates both molecules and the synthetic routes that produce them. Our approach integrates three complementary components. Gradient based saliency combined with counterfactual perturbations identifies which atomic environments influence reward and how structural edits change molecular outcomes. Sparse autoencoders reveal axis aligned latent factors that correspond to physicochemical properties such as polarity, lipophilicity, and molecular size. Motif probes show that functional groups including aromatic rings and halogens are explicitly encoded and linearly decodable from the internal embeddings. Together, these results expose the chemical logic inside SynFlowNet and provide actionable and mechanistic insight that supports transparent and controllable molecular design.