Neural networks for the prediction organic chemistry reactions

TL;DR

This paper explores neural network models combined with reaction fingerprinting and SMARTS transformations to predict organic chemistry reactions, aiming to improve reaction prediction accuracy and support synthetic planning.

Contribution

It introduces a novel neural network approach with a new reaction fingerprinting method and integrates it with SMARTS transformations for reaction prediction.

Findings

Successfully predicts reaction types from textbook problems

Demonstrates the potential of neural networks in reaction prediction

Provides a new tool for aiding synthetic chemistry planning

Abstract

Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and re- actants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.