Hit Expansion via Localized Exploration of Synthesizable Chemical Space

Generative models for drug design which directly produce synthetic pathways have gained significant popularity due to their ability to constrain the search space to synthetically accessible molecules. However, existing methods have focused primarily on de novo molecular design, and rarely start the generation process from known binders. In this paper, we present HELiX: a template-based GFlowNet for localized exploration of chemical space. HELiX learns to partially decompose a given synthetic trajectory to an intermediate state, and then perform forward synthesis in a manner that preserves synthetic accessibility, leading to diverse, high-reward analog generation. Moreover, we prioritize sample efficiency by incorporating Bayesian optimization into the training procedure. We diagnose problems inherent to training GFlowNets with Bayesian optimization and introduce a greedy acquisition strategy which effectively balances between exploration and exploitation without the need for reward shaping. Finally, we show that local exploration is inherently robust to noisy oracle evaluations, a common problem in drug development when using unreliable proxies for binding affinity.