title: Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction.
abstract: Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule-based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10-accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.
authors.affiliation: Organisch-Chemisches Institut and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, Corrensstr. 40, 48149, Münster, Germany.
authors.forename: Marwin H S
authors.initials: MHS
authors.lastname: Segler
authors.affiliation: Organisch-Chemisches Institut and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, Corrensstr. 40, 48149, Münster, Germany.|Department of Physics and International Center for Quantum and Molecular Structures, Shanghai University, Shangda Road 99, 200444, Shanghai, China.
authors.forename: Mark P
authors.initials: MP
authors.lastname: Waller
keywords: artificial intelligence; machine learning; retrosynthesis; synthesis design; total synthesis