Optimizing Multi-Task Learning with Evolutionary Relatedness Metrics for Enhanced QSAR-Based Natural Product Activity Prediction

Abstract

Natural products exhibit diverse and typically nonflat structures, which could be essential in drug–target interactions. Given limited bioactivity data for natural products in public databases, multitask learning (MTL) offers a promising strategy to improve quantitative structure–activity relationship (QSAR)-based predictions. This study optimized MTL with evolutionary relatedness metrics of proteins to enhance the prediction of natural product bioactivity, particularly when data are scarce, and identified conditions under which MTL is most effective. A curated data set of predicted natural products with bioactivity against enzymes from ChEMBL was constructed using binary classification filtering. Single-task learning (STL) served as the baseline, feature-based MTL (FBMTL) was applied across all proteins within each protein group, and instance-based MTL (IBMTL), a variant of FBMTL, incorporated evolutionary relatedness metrics. IBMTL outperformed STL and FBMTL across most protein groups, suggesting that evolutionary relatedness improves performance. Significant improvements were observed in the kinase and cytochrome P450 protein groups, whose proteins are classified at more specific levels of ChEMBL’s 6-level hierarchical protein classification. In the kinase group, IBMTL performed best at the target parent level, highlighting a trade-off between relatedness and data set size. This study demonstrates the potential of MTL in natural product-based drug discovery by leveraging evolutionary relatedness despite limited data availability.