Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules.

ABSTRACT: A range of generative machine learning models for the design of novel molecules and materials have been proposed in recent years. Models that can generate three-dimensional structures are particularly suitable for quantum chemistry workflows, enabling direct property prediction. The performance of generative models is typically assessed based on their ability to produce novel, valid, and unique molecules. However, equally important is their ability to learn the prevalence of functional groups and certain chemical moieties in the underlying training data, that is, to faithfully reproduce the chemical space spanned by the training data. Here, we investigate the ability of the autoregressive generative machine learning model G-SchNet to reproduce the chemical space and property distributions

SUBMITTER: Koczor-Benda Z 

PROVIDER: S-EPMC12264931 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature