Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913972403306496 |
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| author | Andronov, Mikhail Andronova, Natalia Wand, Michael Schmidhuber, Jürgen Clevert, Djork-Arné |
| author_facet | Andronov, Mikhail Andronova, Natalia Wand, Michael Schmidhuber, Jürgen Clevert, Djork-Arné |
| contents | AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits their utility for high-throughput synthesizability screening in de novo drug design. We propose a method for accelerating multi-step synthesis planning systems that rely on SMILES-to-SMILES transformers as single-step retrosynthesis models. Our approach reduces the latency of SMILES-to-SMILES transformers powering multi-step synthesis planning in AiZynthFinder through speculative beam search combined with a scalable drafting strategy called Medusa. Replacing standard beam search with our approach allows the CASP system to solve 26\% to 86\% more molecules under the same time constraints of several seconds. Our method brings AI-based CASP systems closer to meeting the strict latency requirements of high-throughput synthesizability screening and improving general user experience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search Andronov, Mikhail Andronova, Natalia Wand, Michael Schmidhuber, Jürgen Clevert, Djork-Arné Machine Learning Artificial Intelligence AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits their utility for high-throughput synthesizability screening in de novo drug design. We propose a method for accelerating multi-step synthesis planning systems that rely on SMILES-to-SMILES transformers as single-step retrosynthesis models. Our approach reduces the latency of SMILES-to-SMILES transformers powering multi-step synthesis planning in AiZynthFinder through speculative beam search combined with a scalable drafting strategy called Medusa. Replacing standard beam search with our approach allows the CASP system to solve 26\% to 86\% more molecules under the same time constraints of several seconds. Our method brings AI-based CASP systems closer to meeting the strict latency requirements of high-throughput synthesizability screening and improving general user experience. |
| title | Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.01459 |