Title : QSAR analysis in drug design: A retrospective, current state, and case studies of mapping structure–activity landscapes of thiazolopyridine-based APIs
Abstract:
The development of modern therapeutics relies heavily on Quantitative Structure-Activity Relationship (QSAR) methodologies to guide rational drug design, optimize lead compounds, and minimize empirical trial-and-error. This presentation provides a comprehensive review of QSAR analysis in drug design, tracing its evolution from classic linear models (Hansch and Free-Wilson analysis) to contemporary 3D-QSAR and machine learning-driven computational approaches. To illustrate the practical utility of these models, recent case studies involving thiazolopyridine derivatives are examined. The analysis highlights how specific structural descriptors, such as electronic, hydrophobic, and steric parameters on the fused thiazolopyridine core, correlate with target enzyme affinities and biological potencies across anticancer, antimicrobial, and anti-inflammatory pathways. Finally, current challenges in model validation, domain of applicability, and the integration of AI-assisted QSAR in scaffold optimization are discussed. Acknowledgement: This work was supported by the Ministry of Health of Ukraine out of the state budget within the research project No. 0126U001841 "Molecular-oriented design of new antioxidants based on thiazole-containing heterocycles and flavonoids".
