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

arXiv: https://arxiv.org/abs/2504.02191 · HTML: https://mlsanity.com/paper/2504.02191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 3 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Hopfield Networks is All You Need](https://mlsanity.com/paper/2008.02217/) (2020)
- [Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction](https://mlsanity.com/paper/1811.02633/) (2018)
- [Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits](https://mlsanity.com/paper/2006.15426/) (2020)
- [Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search](https://mlsanity.com/paper/2006.15820/) (2020)
- [A Generative Model For Electron Paths](https://mlsanity.com/paper/1805.10970/) (2018)
- [Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search](https://mlsanity.com/paper/2407.06334/) (2024)
- [Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction](https://mlsanity.com/paper/2106.07801/) (2021)

## Abstract (arXiv)

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.