.png){kind=icon}
.png){kind=icon}
Return
A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning
.png){kind=icon}
OA
AI
DOI：10.1039/D5DD00562K.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 molecules such as dronabinol; arformoterol; and lupinine.
Keywords:
MHNpath
retrosynthetic analysis
machine learning
synthesis planning
green chemistry
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.6
Papers:
979
Citations:
1.7K
Organization
No organization information available