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

Rafael Gomez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamin Manuel Sanchez, Dennis Sheberla, Jorge Aguilera Iparraguirre, Timothy D Hirzel, Ryan P Adams, Alan Aspuru-Guzik

Abstract:

We report a method to convert discrete
representations of molecules to and from a multidimensional
continuous representation. This model allows us to generate new
molecules for efficient exploration and optimization through
open-ended spaces of chemical compounds. A deep neural
network was trained on hundreds of thousands of existing
chemical structures to construct three coupled functions: an
encoder, a decoder, and a predictor. The encoder converts the
discrete representation of a molecule into a real-valued
continuous vector, and the decoder converts these continuous
vectors back to discrete molecular representations. The predictor
estimates chemical properties from the latent continuous vector
representation of the molecule. Continuous representations of molecules 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 domain of
drug-like molecules and also in a set of molecules with fewer that nine heavy atoms