Molecule Attention Transformer (2020-02-19T00:00:00.000000Z)

TL;DR

This work proposes Molecule Attention Transformer, a key innovation is to augment the attention mechanism in Transformer using inter-atomic distances and the molecular graph structure, and shows that attention weights learned are interpretable from the chemical point of view.

Abstract

Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry. To move towards this goal, we propose Molecule Attention Transformer (MAT). Our key innovation is to augment the attention mechanism in Transformer using inter-atomic distances and the molecular graph structure. Experiments show that MAT performs competitively on a diverse set of molecular prediction tasks. Most importantly, with a simple self-supervised pretraining, MAT requires tuning of only a few hyperparameter values to achieve state-of-the-art performance on downstream tasks. Finally, we show that attention weights learned by MAT are interpretable from the chemical point of view.

Authors

Stanislaw Jastrzebski

5 papers

J. Tabor

2 papers

Lukasz Maziarka

1 papers

TL;DR

Abstract

Authors

References55 items

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