Understanding Conformation Importance in Data-Driven Property Prediction Models.

ABSTRACT: The prediction of molecular properties is essential in chemoinformatics and has many applications in drug discovery and materials design. Molecular representations play a key role in the prediction models to achieve high prediction accuracy. Nevertheless, appropriate molecular descriptors, including the utilization of conformational information, have been unclear due to a lack of systematic analysis of property prediction models and control. This study investigates the influence of using multiple conformers in machine learning-based property prediction, comparing two- and three-dimensional descriptors using three independent data sets: a large-scale quantum mechanical property, a medium-scale melting point, and small-scale enantioselective chemical reaction data sets. One unique aspect of

SUBMITTER: Hamakawa Y 

PROVIDER: S-EPMC12004525 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature