Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation.

ABSTRACT: Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between physicochemical and biological properties and conventional molecular featurization schemes, complicates the development of robust molecular machine learning models. Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks. Yet, existing molecular SSL methods largely overlook chemical knowledge, including molecular structure similarity, scaffold composition, and the context-dependent aspects of molecula

SUBMITTER: Wan Y 

PROVIDER: S-EPMC11704287 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature