Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism.

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Abstract

Hunting for chemicals with favourable pharmacological, toxicological and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery datasets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of datasets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns non-local intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.