Quantitative Structure Activity Relationship

Quantitative Structure-Activity Relationship (QSAR) research aims to predict the biological activity of molecules based on their chemical structure, accelerating drug discovery and materials science. Current efforts focus on improving model accuracy and generalizability using advanced machine learning techniques, including graph neural networks, transformers, and various deep learning architectures, often incorporating pre-training and self-supervised learning strategies to address challenges like activity cliffs. These advancements are improving the prediction of diverse molecular properties and enabling more efficient design of novel compounds with desired characteristics, impacting fields ranging from drug development to materials science. Furthermore, research emphasizes robust uncertainty quantification and interpretability of QSAR models to enhance reliability and facilitate informed decision-making.