Deep learning

Graph

Neuroscience

Perception

Feature (linguistics)

Distrust

Psychotherapist

Politics

Epistemology

Variety (cybernetics)

Machine learning

Philosophy

Political science

Artificial intelligence

Psychology

Chemistry

Data science

Representation (politics)

Computer science

Linguistics

Drug discovery

Theoretical computer science

Mechanism (biology)

Deep neural networks

Biochemistry

Feature learning

Journal of Medicinal Chemistry

Hunting for chemicals with favorable 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 data sets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of data sets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns nonlocal intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.

Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism