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. The ability to reliably predict the products of chemical reactions is of central importance to the manufacture of medicines and materials, and to understand many processes in molecular biology. Theoretically, all chemical reactions can be described by the stepwise rearrangement of electrons in molecules (Herges, 1994b) . This sequence of bond-making and breaking is known as the reaction mechanism. Understanding the reaction mechanism is crucial because it not only determines the products (formed at the last step of the mechanism), but it also provides insight into why the products are formed on an atomistic level. Mechanisms can be treated at different levels of abstraction. On the lowest level, quantum-mechanical simulations of the electronic structure can be performed, which are prohibitively computationally expensive for most systems of interest. On the other end, chemical reactions can be treated as rules that "rewrite" reactant molecules to products, which abstracts away the individual electron redistribution steps into a single, global transformation step. To combine the advantages of both approaches, chemists use a powerful qualitative model of quantum chemistry colloquially called "arrow pushing", which simplifies the stepwise electron shifts using sequences of arrows which indicate the path of electrons throughout molecular graphs (Herges, 1994b) .Recently , there have been a number of machine learning models proposed for directly predicting the products of chemical reactions BID2 Jin et al., 2017; Schwaller et al., 2018; Segler and Waller, 2017a; Segler et al., 2018; Wei et al., 2016) , largely using graph-based or machine translation models. The task of reaction product prediction is shown on the left-hand side of FIG0 .In this paper we propose a machine learning model to predict the reaction mechanism, as shown on the right-hand side of FIG0 , for a particularly important subset of organic reactions. We argue that our The reaction product prediction problem: Given the reactants and reagents, predict the structure of the product. (Right) The reaction mechanism prediction problem: Given the reactants and reagents, predict how the reaction occurred to form the products.model is not only more interpretable than product prediction models, but also allows easier encoding of constraints imposed by chemistry. Proposed approaches to predicting reaction mechanisms have often been based on combining hand-coded heuristics and quantum mechanics BID0 Kim et al., 2018; Nandi et al., 2017; Segler and Waller, 2017b; Rappoport et al., 2014; Simm and Reiher, 2017; Zimmerman, 2013) , rather than using machine learning. We call our model ELECTRO , as it directly predicts the path of electrons through molecules (i.e., the reaction mechanism). To train the model we devise a general technique to obtain approximate reaction mechanisms purely from data about the reactants and products. This allows one to train our a model on large, unannotated reaction datasets such as USPTO (Lowe, 2012) . We demonstrate that not only does our model achieve impressive results, surprisingly it also learns chemical properties it was not explicitly trained on. In this paper we proposed ELECTRO, a model for predicting electron paths for reactions with linear electron flow. These electron paths, or reaction mechanisms, describe how molecules react together. Our model (i) produces output that is easy for chemists to interpret, and (ii) exploits the sparsity and compositionality involved in chemical reactions. As a byproduct of predicting reaction mechanisms we are also able to perform reaction product prediction, comparing favorably to the strongest baselines on this task.
A generative model for reaction prediction that learns the mechanistic electron steps of a reaction directly from raw reaction data.
{"src": "scitldr-AIC_train", "paper_id": "r1x4BnCqKX"}