March 1, 2017
Rafa Gómez-Bombarelli
Harvard Chemistry and Chemical Biology
Deep learning chemical space: a variational autoencoder for automatic molecular design
Abstract: Virtual screening is increasingly proven as a tool to test new molecules for a given application. Through simulation and regression we can gauge whether a molecule will be a promising candidate in an automatic and robust way. A large remaining challenge, however, is how to perform optimizations over a discrete space of size at least 10^60. Despite the size of chemical space, or perhaps precisely because of it, coming up with novel, stable, makeable molecules that are effective is not trivial. First-principles approaches to generating new molecules fail to capture the intuition embedded in the ~100 million existing molecules. I will report our progress towards developing an autoencoder that allows us to project molecular space into a continuous, differentiable representation where we can perform molecular optimization.
For more information on the Broad Institute and MIA visit:
http://www.broadinstitute.org/MIA
Copyright Broad Institute, 2017. All rights reserved.
