Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
93 tokens/sec
Gemini 2.5 Pro Premium
54 tokens/sec
GPT-5 Medium
22 tokens/sec
GPT-5 High Premium
17 tokens/sec
GPT-4o
101 tokens/sec
DeepSeek R1 via Azure Premium
91 tokens/sec
GPT OSS 120B via Groq Premium
441 tokens/sec
Kimi K2 via Groq Premium
225 tokens/sec
2000 character limit reached

DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis (2405.13983v3)

Published 22 May 2024 in cs.LG and cs.AI

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 series of transformer-based models, that leverage a mixture of experts approach to directly generate multistep synthetic routes as a single string, conditionally predicting each transformation based on all preceding ones. Our DMS Explorer XL model, which requires only target compounds as input, outperforms state-of-the-art methods on the PaRoutes dataset with 1.9x and 3.1x improvements in Top-1 accuracy on the n$_1$ and n$_5$ test sets, respectively. Providing additional information, such as the desired number of steps and starting materials, enables both a reduction in model size and an increase in accuracy, highlighting the benefits of incorporating more constraints into the prediction process. The top-performing DMS-Flex (Duo) model scores 25-50% higher on Top-1 and Top-10 accuracies for both n$_1$ and n$_5$ sets. Additionally, our models successfully predict routes for FDA-approved drugs not included in the training data, demonstrating strong generalization capabilities. While the limited diversity of the training set may affect performance on less common reaction types, our multistep-first approach presents a promising direction towards fully automated retrosynthetic planning.

Citations (1)

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.