A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| author | Prakash, Shivesh Patel, Nandan Jacobsen, Hans-Arno Prasad, Viki Kumar |
| author_facet | Prakash, Shivesh Patel, Nandan Jacobsen, Hans-Arno Prasad, Viki Kumar |
| contents | We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metrics, MHNpath efficiently prioritizes reaction templates, improving the scalability and accuracy of retrosynthetic predictions. The tool incorporates a tunable scoring system that allows users to prioritize pathways based on cost, reaction temperature, and toxicity, thereby facilitating the design of greener and cost-effective reaction routes. We demonstrate its effectiveness through case studies involving complex molecules from ChemByDesign, showcasing its ability to predict novel synthetic and enzymatic pathways. Furthermore, we benchmark MHNpath against existing frameworks using the PaRoutes dataset, achieving a solution rate of 85.4% and replicating 69.2% of experimentally validated "gold-standard" pathways. Our case studies reveal that the tool can generate shorter, cheaper, moderate-temperature routes employing green solvents, as exemplified by compounds such as dronabinol, arformoterol, and lupinine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02191 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning Prakash, Shivesh Patel, Nandan Jacobsen, Hans-Arno Prasad, Viki Kumar Computational Engineering, Finance, and Science Machine Learning We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metrics, MHNpath efficiently prioritizes reaction templates, improving the scalability and accuracy of retrosynthetic predictions. The tool incorporates a tunable scoring system that allows users to prioritize pathways based on cost, reaction temperature, and toxicity, thereby facilitating the design of greener and cost-effective reaction routes. We demonstrate its effectiveness through case studies involving complex molecules from ChemByDesign, showcasing its ability to predict novel synthetic and enzymatic pathways. Furthermore, we benchmark MHNpath against existing frameworks using the PaRoutes dataset, achieving a solution rate of 85.4% and replicating 69.2% of experimentally validated "gold-standard" pathways. Our case studies reveal that the tool can generate shorter, cheaper, moderate-temperature routes employing green solvents, as exemplified by compounds such as dronabinol, arformoterol, and lupinine. |
| title | A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning |
| topic | Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2504.02191 |