학술논문
Applying deep learning to iterative screening of medium-sized molecules for protein–protein interaction-targeted drug discovery.
Document Type
Article
Author
Source
Subject
*DRUG discovery
*DEEP learning
*MACHINE learning
*MOLECULES
*PROTEIN-protein interactions
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Language
ISSN
1359-7345
Abstract
We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein–protein interaction target. This was demonstrated by inhibition assays using a PPI target, Kelch-like ECH-associated protein 1/nuclear factor erythroid 2-related factor 2 (Keap1/Nrf2), and a deep neural network model based on the first-round assay data showed a highest hit rate of 27.3%. Using the models, we identified novel active and non-flat compounds far from public datasets, expanding the chemical space. [ABSTRACT FROM AUTHOR]