Beyond Major Product Prediction - Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset

Aug 4, 2026

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Mechanistic understanding of organic reactions supports reaction development, impurity prediction, and, in principle, reaction discovery. Recent machine learning approaches predict reaction products, yet extending them to reaction mechanisms is blocked by the absence of a matching mechanistic dataset. This study constructs a mechanistic dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates, training multiple models on 5,184,184 elementary steps. Models are assessed for pathway prediction and for reproducing roles of catalysts and reagents, while also testing impurity prediction and generalizability to new reaction types.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages105

Reading Time4 h 25 min

AccessFree to download

Abstract

Introduction

Mechanistic prediction as a chemistry challenge

Quantum chemical approaches and limitations

Data-driven major product prediction

Need for mechanistic datasets

Patent-derived reaction datasets and heuristic inference

Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset Joonyoung F. Joung 1, Mun Hong Fong 1, Jihye Roh 1, Zhengkai Tu2, John Bradshaw 1, and Connor W. Coley 1, 2, * 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States 2Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States *E-mail: [ccoley@mit.edu](ccoley@mit.edu) (C.W.C.) Abstract Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation. Introduction Anticipating the outcomes of chemical reactions given specific reactants and conditions remains a formidable challenge in the field of chemistry. Expert chemists rely on a comprehension of reaction mechanisms as a guiding framework for predicting the probable outcomes of reactions, often recorded as “arrow pushing” diagrams. However, discerning a plausible mechanism through which a reaction proceeds by intuition alone is not always straightforward, resulting in development of computational methods and quantitative analyses for their investigation. First principles quantum chemical calculations offer one approach to the proposal and validation of reaction mechanisms when provided with reactants and known products. Despite the development of techniques such as growing string[1] and nudged elastic band[2] to uncover elementary reaction pathways and transition states connecting initial and final states, these methods are computationally expensive and reliant on expertise to predefine hypothetical pathways for evaluation. Studies focused on reaction mechanisms using quantum chemical calculations often encounter limitations, primarily restricting their scope to reactions involving relatively small molecule sizes. [3] If the goal of reaction outcome prediction is simplified to the prediction of major products, abstracting away details of the chemistry, the problem becomes amenable to data-driven solutions. A variety of machine learning models trained on experimental data reported in journal articles and patents have been applied to this task in recent years, leveraging problem formulations such as graph edit prediction with graph neural networks[4], machine translation of reactant SMILES strings[5] or encoded reactant graphs[6] into products, prediction of electron paths in the reaction[7], and classification of reaction templates. [8] While useful in many contexts, these end-to-end machine learning models often face criticism for their inability to explain the formation of products from given reactants in terminology consistent with how organic chemists explain reactivity. In principle, machine learning models for product prediction could be retrained on mechanistic datasets to predict intermediate products as well.

FAQs

What problem does the study target in reaction modeling?

It addresses the difficulty of predicting reaction mechanisms from reactants and conditions, which is limited by the lack of a mechanistic dataset compatible with organic-chemistry understanding.

How is the mechanistic dataset constructed?

Intermediates are imputed between experimentally reported reactants and products using expert reaction templates, generating training data covering 5,184,184 elementary steps.

What model capabilities are evaluated in the study?

The work evaluates pathway prediction performance and the ability to recapitulate the roles of catalysts and reagents, and it also demonstrates potential mechanistic models for impurity prediction.

Aug 4, 2026

Upload by Maeve