MACHINE LEARNING-BASED QSAR MODELING OF TRICLOSAN ANALOGS FOR ANTIPLASMODIAL ACTIVITY PREDICTION USING SHRINKAGE REGRESSION MODEL AND TOPOLOGICAL-PHYSICOCHEMICAL DESCRIPTORS
Drug-resistant Plasmodium falciparum continues to threaten malaria control, necessitating new antimalarial discovery strategies. QSAR modeling with machine learning offers a cost-effective approach to relate molecular features to biological activity and prioritize candidate compounds for further development. In particu...