Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism.
J Med Chem
; 63(16): 8749-8760, 2020 08 27.
Article
de En
| MEDLINE
| ID: mdl-31408336
ABSTRACT
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Composés chimiques organiques
/
Découverte de médicament
/
Apprentissage profond
Type d'étude:
Prognostic_studies
Langue:
En
Journal:
J Med Chem
Sujet du journal:
QUIMICA
Année:
2020
Type de document:
Article
Pays d'affiliation:
Chine