Designing a molecule with desired properties is one of the biggest challenges in drug development. Read our latest article and learn about our solutions for augmenting compound design process.
Molecule Attention Transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, Stanisław Jastrzebski
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.
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Expert Contribution
Reviewed by: Dr. Krzysztof Rataj, PhD
Role: Scientist, AI‑Driven Drug Discovery
Expertise: Bioinformatics, computational biology, ML‑driven drug discovery workflows, data analysis pipelines
