Learning to SMILE(S)
This addresses a crucial problem in computer-aided drug design by bridging cheminformatics and NLP, offering a novel approach for drug discovery.
The paper tackles activity prediction against target proteins in cheminformatics by applying NLP methods to SMILES representations, achieving state-of-the-art results and providing structural insights.
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.