Application of Artificial Neural Networks to Predict the Intrinsic Solubility of Drug-Like Molecules.

ABSTRACT: Machine learning (ML) approaches are receiving increasing attention from pharmaceutical companies and regulatory agencies, given their ability to mine knowledge from available data. In drug discovery, for example, they are employed in quantitative structure-property relationship (QSPR) models to predict biological properties from the chemical structure of a drug molecule. In this paper, following the Second Solubility Challenge (SC-2), a QSPR model based on artificial neural networks (ANNs) was built to predict the intrinsic solubility (logS₀) of the 100-compound low-variance tight set and the 32-compound high-variance loose set provided by SC-2 as test datasets. First, a training dataset of 270 drug-like molecules with logS₀ value experimentally determi

SUBMITTER: Tosca EM 

PROVIDER: S-EPMC8309152 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature