Learning to SMILE(S)

Abstract

This paper shows how one can directly apply natural language processing (NLP) methods to classification problems in cheminformatics. Connection between these seemingly separate fields is shown by considering standard textual representation of compound, SMILES. The problem of activity prediction against a target protein is considered, which is a crucial part of computer aided drug design process. Conducted experiments show that this way one can not only outrank state of the art results of hand crafted representations but also gets direct structural insights into the way decisions are made.

Accepted as a workshop contribution to ICLR 2016

References in corpus (1)

• Deep Convolutional Networks on Graph-Structured Data

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