Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908625629347840 |
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| author | Gaiński, Piotr Boussif, Oussama Rekesh, Andrei Shevchuk, Dmytro Parviz, Ali Tyers, Mike Batey, Robert A. Koziarski, Michał |
| author_facet | Gaiński, Piotr Boussif, Oussama Rekesh, Andrei Shevchuk, Dmytro Parviz, Ali Tyers, Mike Batey, Robert A. Koziarski, Michał |
| contents | Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose Recursive Cost Guidance, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an Exploitation Penalty that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a Dynamic Library mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable and Cost-Efficient de Novo Template-Based Molecular Generation Gaiński, Piotr Boussif, Oussama Rekesh, Andrei Shevchuk, Dmytro Parviz, Ali Tyers, Mike Batey, Robert A. Koziarski, Michał Biomolecules Artificial Intelligence Machine Learning Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose Recursive Cost Guidance, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an Exploitation Penalty that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a Dynamic Library mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation. |
| title | Scalable and Cost-Efficient de Novo Template-Based Molecular Generation |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.19865 |