Open access
Sep 2026
Interpretable Machine Learning Frameworks for QSAR Modeling: A Comparative Study of Classical Algorithms and Transformer-CNN Architectures
An interpretable Quantitative Structure–Activity Relationship (QSAR) framework that integrates classical machine learning approaches with a hybrid deep learning architecture to model enzyme inhibition using a curated and diverse data set from ChEMBL is developed.
Fatima Ezzahra Belkhanchi, M. Mbarki, S. Chtita et al.
· ACS Omega · 0 citations