In:
Chemical Communications, Royal Society of Chemistry (RSC), Vol. 59, No. 44 ( 2023), p. 6722-6725
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.
Type of Medium:
Online Resource
ISSN:
1359-7345
,
1364-548X
Language:
English
Publisher:
Royal Society of Chemistry (RSC)
Publication Date:
2023
detail.hit.zdb_id:
1472881-3
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