LLM-Augmented Chemical Synthesis and Design Decision Programs
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917475673702400 |
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| author | Wang, Haorui Guo, Jeff Kong, Lingkai Ramprasad, Rampi Schwaller, Philippe Du, Yuanqi Zhang, Chao |
| author_facet | Wang, Haorui Guo, Jeff Kong, Lingkai Ramprasad, Rampi Schwaller, Philippe Du, Yuanqi Zhang, Chao |
| contents | Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development. Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways. Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry. In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem. We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy, moving beyond the conventional step-by-step reactant prediction. Through comprehensive evaluations, we show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Augmented Chemical Synthesis and Design Decision Programs Wang, Haorui Guo, Jeff Kong, Lingkai Ramprasad, Rampi Schwaller, Philippe Du, Yuanqi Zhang, Chao Artificial Intelligence Computation and Language Machine Learning Neural and Evolutionary Computing Chemical Physics Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development. Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways. Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry. In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem. We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy, moving beyond the conventional step-by-step reactant prediction. Through comprehensive evaluations, we show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design. |
| title | LLM-Augmented Chemical Synthesis and Design Decision Programs |
| topic | Artificial Intelligence Computation and Language Machine Learning Neural and Evolutionary Computing Chemical Physics |
| url | https://arxiv.org/abs/2505.07027 |