TransQSAR-pf: A Bio-Informed QSAR Framework Using Plasmodium falciparum Stress Signatures for Enhanced Antiplasmodial Activity Prediction

Traditional QSAR modeling relies solely on molecular descriptors, neglecting the biological state of target organisms.While prior approaches have integrated biological data with molecular features for activity prediction, we developed TransQSAR-pf, a methodological framework that integrates Plasmodium falciparum transcriptomic stress signatures with molecular descriptors to construct biologically informed activity prediction models.Applied to 125 triazolopyrimidine derivatives, the framework distilled 764 transcriptomic features into 13 key predictors through Boruta selection, constructing an interpretable model (R 2 = 0.762, RMSE = 0.470) that demonstrated improved performance over the baseline QSAR-only model (R 2 = 0.719, RMSE = 0.529).Biological mapping revealed that 71.2% of feature importance derived from conserved unknown-function genes, representing largely uncharacterized stress response pathways that correlate with compound efficacy and warrant experimental characterization, demonstrating the framework's utility for generating mechanistic hypotheses.This work presents a novel computational pipeline for building biology-aware QSAR models that prioritize experimental targets for antimalarial discovery.