DirectMultiStep: Direct Route Generation for Multi-Step Retrosynthesis

• URL: http://arxiv.org/abs/2405.13983v1
• Date: Wed, 22 May 2024 20:39:05 GMT
• Title: DirectMultiStep: Direct Route Generation for Multi-Step Retrosynthesis
• Authors: Yu Shee, Haote Li, Anton Morgunov, Victor Batista,
• Abstract summary: We introduce a transformer-based model that generates multi-step synthetic routes as a single string by conditionally predicting each molecule based on all preceding ones. The model accommodates specific conditions such as the desired number of steps and starting materials, outperforming state-of-the-art methods on the PaRoutes dataset. It also successfully predicts routes for FDA-approved drugs not included in the training data, showcasing its generalization capabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional computer-aided synthesis planning (CASP) methods rely on iterative single-step predictions, leading to exponential search space growth that limits efficiency and scalability. We introduce a transformer-based model that directly generates multi-step synthetic routes as a single string by conditionally predicting each molecule based on all preceding ones. The model accommodates specific conditions such as the desired number of steps and starting materials, outperforming state-of-the-art methods on the PaRoutes dataset with a 2.2x improvement in Top-1 accuracy on the n$_1$ test set and a 3.3x improvement on the n$_5$ test set. It also successfully predicts routes for FDA-approved drugs not included in the training data, showcasing its generalization capabilities. While the current suboptimal diversity of the training set may impact performance on less common reaction types, our approach presents a promising direction towards fully automated retrosynthetic planning.

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