New GFlowNet method generates highly synthesizable molecules

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.

RANK_REASON The cluster contains an academic paper detailing a new method for molecular generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• GFlowNets
• Hyeonah Kim
• S3-GFN

• paper
• model release

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