A Generative Model For Electron Paths

TL;DR

This paper introduces ELECTRO, a generative model that predicts electron paths in chemical reactions from raw data, offering interpretability, chemical constraints, and improved reaction prediction performance.

Contribution

The paper presents a novel electron path prediction model that learns from raw reaction data, incorporating chemical constraints and interpretability, outperforming existing baselines.

Findings

Achieves high accuracy on USPTO reaction dataset

Recovers basic chemical knowledge without explicit training

Provides interpretable electron movement sequences

Abstract

Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants.We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.