RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916686937980928 |
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| author | Gaiński, Piotr Koziarski, Michał Maziarz, Krzysztof Segler, Marwin Tabor, Jacek Śmieja, Marek |
| author_facet | Gaiński, Piotr Koziarski, Michał Maziarz, Krzysztof Segler, Marwin Tabor, Jacek Śmieja, Marek |
| contents | Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery. Although a target molecule can often be synthesized with multiple different reactions, it is not clear how to verify the feasibility of a reaction, because the available datasets cover only a tiny fraction of the possible solutions. Consequently, the existing models are not encouraged to explore the space of possible reactions sufficiently. In this paper, we propose a novel single-step retrosynthesis model, RetroGFN, that can explore outside the limited dataset and return a diverse set of feasible reactions by leveraging a feasibility proxy model during the training. We show that RetroGFN achieves competitive results on standard top-k accuracy while outperforming existing methods on round-trip accuracy. Moreover, we provide empirical arguments in favor of using round-trip accuracy, which expands the notion of feasibility with respect to the standard top-k accuracy metric. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18739 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets Gaiński, Piotr Koziarski, Michał Maziarz, Krzysztof Segler, Marwin Tabor, Jacek Śmieja, Marek Machine Learning Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery. Although a target molecule can often be synthesized with multiple different reactions, it is not clear how to verify the feasibility of a reaction, because the available datasets cover only a tiny fraction of the possible solutions. Consequently, the existing models are not encouraged to explore the space of possible reactions sufficiently. In this paper, we propose a novel single-step retrosynthesis model, RetroGFN, that can explore outside the limited dataset and return a diverse set of feasible reactions by leveraging a feasibility proxy model during the training. We show that RetroGFN achieves competitive results on standard top-k accuracy while outperforming existing methods on round-trip accuracy. Moreover, we provide empirical arguments in favor of using round-trip accuracy, which expands the notion of feasibility with respect to the standard top-k accuracy metric. |
| title | RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.18739 |