Retro-fallback: retrosynthetic planning in an uncertain world
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913312908771328 |
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| author | Tripp, Austin Maziarz, Krzysztof Lewis, Sarah Segler, Marwin Hernández-Lobato, José Miguel |
| author_facet | Tripp, Austin Maziarz, Krzysztof Lewis, Sarah Segler, Marwin Hernández-Lobato, José Miguel |
| contents | Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by algorithms may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_09270 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Retro-fallback: retrosynthetic planning in an uncertain world Tripp, Austin Maziarz, Krzysztof Lewis, Sarah Segler, Marwin Hernández-Lobato, José Miguel Artificial Intelligence Machine Learning Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules. While previous works have proposed algorithms to find optimal solutions for a range of metrics (e.g. shortest, lowest-cost), these works generally overlook the fact that we have imperfect knowledge of the space of possible reactions, meaning plans created by algorithms may not work in a laboratory. In this paper we propose a novel formulation of retrosynthesis in terms of stochastic processes to account for this uncertainty. We then propose a novel greedy algorithm called retro-fallback which maximizes the probability that at least one synthesis plan can be executed in the lab. Using in-silico benchmarks we demonstrate that retro-fallback generally produces better sets of synthesis plans than the popular MCTS and retro* algorithms. |
| title | Retro-fallback: retrosynthetic planning in an uncertain world |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2310.09270 |