Transfer Learning Without Chemical Amnesia for Predicting Natural Product Bioactivity Across Rare Scaffolds, New Targets, and Data-Sparse Therapeutic Domains
Year: 2025
Type: article
Abstract: Natural products remain a valuable but challenging source of bioactive compounds due to their structural diversity, stereochemical complexity, and uneven representation in public datasets, which often diverge from the distributions used to train contemporary machine-learning models. Transfer learning provides a promising solution for data-sparse prediction tasks, yet adaptation to new targets can inadvertently suppress information that is critical for chemically rare scaffolds, a phenomenon term
DOI: 10.51847/h9vptvjuoa
Authors: Rafael Mendoza, Isabela Cruz, Carlos Lopez
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7220442583