Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model
<|reference_start|>Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model: Retrosynthesis is a fundamental but challenging task in organic chemistry, with broad applications in fields such as drug design and synthesis. Given a target molecule, the goal of retrosynthesis is to find out a series of reactions which could be assembled into a synthetic route which starts from purchasable molecules and ends at the target molecule. The uncertainty of reactions used in retrosynthetic planning, which is caused by hallucinations of backward models, has recently been noticed. In this paper we propose a succinct probabilistic model to describe such uncertainty. Based on the model, we propose a new retrosynthesis planning algorithm called retro-prob to maximize the successful synthesis probability of target molecules, which acquires high efficiency by utilizing the chain rule of derivatives. Experiments on the Paroutes benchmark show that retro-prob outperforms previous algorithms, retro* and retro-fallback, both in speed and in the quality of synthesis plans.<|reference_end|>
@article{tian2024retro-prob:,
    title={Retro-prob: Retrosynthetic Planning Based on a Probabilistic Model},
    author={Chengyang Tian and Yangpeng Zhang and Yang Liu},
    journal={arXiv preprint arXiv:2405.16123},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16123},
    primaryClass={cs.AI q-bio.BM}
}