Drug-likeness scoring based on unsupervised learning.

ABSTRACT: Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learni

SUBMITTER: Lee K 

PROVIDER: S-EPMC8729801 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature