Work

Neural Networks for the Prediction of Organic Chemistry Reactions

Year: 2016

Type: article

Abstract: Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite 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 reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.

Source: ACS Central Science

DOI: 10.1021/acscentsci.6b00219

Authors: Jennifer N. Wei, David Duvenaud, Alán Aspuru-Guzik

Institution: Harvard University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Drug Discovery

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

Open Access status: diamond

Funders: National Science Foundation, Samsung Advanced Institute of Technology, Samsung, Division of Graduate Education

Awards: G4462204876, Graduate Research Fellowship Program (GRFP)