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

TL;DR

MHNpath presents a user-tunable, ML-driven retrosynthetic framework that combines Modern Hopfield Network–based template prioritization with a global greedy tree-search strategy to generate multi-step, cost-aware, and environmentally friendly synthesis routes. The approach supports a multi-objective scoring system prioritizing precursor cost, reaction temperature, and solvent toxicity, enabling greener and more practical routes. Evaluations on PaRoutes and ChemByDesign show strong template-prioritization performance, high pathway replication of gold-standard routes, and the discovery of shorter, cheaper pathways (e.g., a three-step route for dronabinol at $0.12/g) through enzymatic-synthetic hybrids. Supplementary analyses provide detailed hyperparameter tuning, scaffold diversity, pathway visualizations, and case studies that demonstrate MHNpath’s capacity to replicate, improve, and extend established synthetic strategies.

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 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.

Figures (12)

• Figure 1: (a) Data processing and model architecture. Enzymatic and synthetic databases were cleaned, and reaction templates were algorithmically extracted from them. A Modern Hopfield Network-based template prioritizer was trained to predict the reaction template associated with each product molecule in the reaction database. (b) Tree search methodology. Multi-modal-guided-global-greedy tree search approach was used to explore the retrosynthetic search space. The precursors are denoted as grey and yellow circles for the synthetic and enzymatic reactions, respectively. The nodes are assigned a score based on a methodology that promotes low cost, the use of green solvents, and moderate-temperature reactions. A priority queue is maintained to track the highest-scoring node at all times. The highest-scoring node from the queue is explored. The queue and tree are updated iteratively until the tree is fully explored to a certain depth, or we reach a time limit.
• Figure 2: (a) Tree of reaction pathways. The tree shows a representative example for a pathway presented in PaRoutes D2DD00015F. Our predicted pathway is producible using cheap precursors and less toxic, naturally occurring solvents. (b) Performance metrics for literature comparison. These plots present the number of molecules solved, the average number of pathways predicted, and the distribution of predicted pathway lengths.
• Figure 3: (a-g) Published pathways. Overview of existing pathways to produce dronabinol. (h) Previously predicted pathways by Levin et al. levin2022merging. Four-step reaction pathway to produce dronabinol as predicted by Levin et al. levin2022merging. (i) Our predicted pathway. Three-step reaction to produce dronabinol from cheap precursors in ambient temperatures. We also replicated some of the other pathways. (j) Performance metrics for comparison with other models. These plots present the number of molecules solved, the average number of pathways predicted, and the distribution of predicted pathway lengths levin2022mergingfinnigan2021retrobiocat.
• Figure 4: Training and validation metrics for the enzymatic template prioritizer over 10 epochs. The top panel shows the training and validation loss. The middle panel illustrates the top-1 validation accuracy. The bottom panel depicts the top-100 validation accuracy.
• Figure 5: Training and validation metrics for the first synthetic template prioritizer over 10 epochs. The top panel shows the training and validation loss. The middle panel illustrates the top-1 validation accuracy. The bottom panel depicts the top-100 validation accuracy.