aiqbench analysis
S3-GFN addresses the scalability and flexibility limitations of prior hard-constrained GFlowNets by introducing soft regularization and off-policy replay training to enforce synthesizability. By leveraging rich molecular priors from large-scale corpora, the model achieves a synthesizable rate of at least 95% while maintaining high rewards across diverse drug discovery tasks. This approach offers a more practical framework for generating de novo molecules that are both chemically valid and feasible for experimental synthesis.
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
arXiv:2602.04119v2 Announce Type: replace Abstract: The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice. Previous works have leveraged Generative Flow Networks (GFlowNets) to impose hard synthesizability constraints through the design of state and action spaces based on predefined reaction templates and building blocks. Despite the promising prospects of this approach, it currently lacks flexibility and scalability. As an alternative, we propose S3-GFN, which generates synthesizable SMILES molecules via simple soft regularization of a sequence-based GFlowNet. Our approach leverages rich molecular priors learned from large-scale SMILES corpora to steer molecular generation towards high-reward, synthesizable chemical spaces. The model induces constraints through off-policy replay training with a contrastive learning signal based on separate buffers of synthesizable and unsynthesizable samples. Our experiments show that S3-GFN learns to generate synthesizable molecules ($\geq 95\%$) with higher rewards in diverse tasks.
Abstract:The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in practice. Previous works have leveraged Generative Flow Networks (GFlowNets) to impose hard synthesizability constraints through the design of state and action spaces based on predefined reaction templates and building blocks. Despite the promising prospects of this approach, it currently lacks flexibility and scalability. As an alternative, we propose S3-GFN, which generates synthesizable SMILES molecules via simple soft regularization of a sequence-based GFlowNet. Our approach leverages rich molecular priors learned from large-scale SMILES corpora to steer molecular generation towards high-reward, synthesizable chemical spaces. The model induces constraints through off-policy replay training with a contrastive learning signal based on separate buffers of synthesizable and unsynthesizable samples. Our experiments show that S3-GFN learns to generate synthesizable molecules ($\geq 95\%$) with higher rewards in diverse tasks.
Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
arXiv:2602.04119 [cs.LG]
(or arXiv:2602.04119v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.04119 Focus to learn more arXiv-issued DOI via DataCite
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