Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Researchers have developed a new method called S3-GFN for generating molecules that are both synthesizable and possess desirable properties. This approach uses a sequence-based Generative Flow Network (GFlowNet) with soft regularization, incorporating rich molecular priors learned from large datasets. By employing contrastive learning with separate buffers of synthesizable and unsynthesizable molecules, S3-GFN effectively guides the generation process towards high-reward chemical spaces, achieving over 95% synthesizability in experiments. AI
IMPACT Introduces a more flexible and scalable approach to generating synthesizable molecules, potentially accelerating drug discovery.