CUED Publications database

Automatic chemical design using a data-driven continuous representation of molecules

Gómez-Bombarelli, R and Duvenaud, D and Hernández-Lobato, JM and Aguilera-Iparraguirre, J and Hirzel, TD and Adams, RP and Aspuru-Guzik, A Automatic chemical design using a data-driven continuous representation of molecules. (Unpublished)

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Abstract

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This generative model allows efficient search and optimization through open-ended spaces of chemical compounds. We train deep neural networks on hundreds of thousands of existing chemical structures to construct two coupled functions: an encoder and a decoder. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to the discrete representation from this latent space. Continuous representations allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the design of drug-like molecules as well as organic light-emitting diodes.

Item Type: Article
Uncontrolled Keywords: cs.LG cs.LG physics.chem-ph
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 20:07
Last Modified: 18 Jul 2017 08:50
DOI: