MHNpath用机器学习优化合成路径，可调成本温度毒性，生成更绿色高效的路线。
A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
- 基于霍普菲尔德网络和新度量，智能排序反应模板。
- 在PaRoutes数据集上解决率达85.4%，复现69.2%的实验验证路径。
- 支持用户自定义优先级，适合绿色化学与药物研发人员使用。
我们提出MHNpath，一种基于机器学习的逆合成规划工具，利用现代霍普菲尔德网络与新型比较度量，高效优先排序反应模板，提升逆合成预测的可扩展性与准确性。该工具具备可调评分系统，支持用户按成本、反应温度、毒性等指标优先选择路径，助力设计更环保、低成本的合成路线。通过ChemByDesign中的复杂分子案例研究，验证其可预测新颖的合成与酶促路径。在PaRoutes数据集上基准测试中，解决方案率达85.4%，复现69.2%的实验验证“黄金标准”路径。案例显示，该工具能生成更短、更便宜、中等温度、使用绿色溶剂的路线，如大麻二酚、阿福莫特罗和卢宾宁等化合物。
原文摘要 · Abstract (English)
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.
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