A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning

• URL: http://arxiv.org/abs/2504.02191v2
• Date: Fri, 04 Apr 2025 01:56:47 GMT
• Title: A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
• Authors: Shivesh Prakash, Hans-Arno Jacobsen, Viki Kumar Prasad,
• Abstract summary: MHNpath is a machine learning-driven retrosynthetic tool for computer-aided synthesis planning.<n>We demonstrate its effectiveness through case studies involving complex molecules from ChemByDesign.<n>Our case studies reveal that the tool can generate shorter, cheaper, moderate-temperature routes employing green solvents.
• Score: 9.502407569651321
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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, replicating experimentally validated "gold-standard" pathways from PaRoutes. 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.

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