RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
<|reference_start|>RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets: 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.<|reference_end|>
@article{gaiński2024retrogfn:,
    title={RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets},
    author={Piotr Gai'nski, Micha{l} Koziarski, Krzysztof Maziarz, Marwin
  Segler, Jacek Tabor, Marek 'Smieja},
    journal={arXiv preprint arXiv:2406.18739},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18739},
    primaryClass={cs.LG}
}