QSAR and Machine Learning Predictors

This chapter delves into the fundamental principles and applications of quantitative structure–activity relationship (QSAR) and machine learning (ML)-based predictors in the realm of drug design and chemical biology. QSAR establishes a quantitative relationship between the chemical structure of molecules and their biological activities or physicochemical properties. The evolution of QSAR from its first reports to its modern applications was covered comprising the theoretical foundations, encompassing descriptors, mathematical models (followed by brief examples of ML applied to this field), and statistical validation techniques employed in QSAR analysis. Interestingly, many recognized and accepted good practices and validation protocols align with OECD guidelines for QSAR applications for regulatory purposes. In this sense, notably, the QSAR field became important outside of the academic boundaries. Additionally, this chapter discusses current challenges and emerging trends in QSAR research, including the incorporation of machine learning algorithms and big data analytics for enhanced predictive accuracy and applicability. Overall, this chapter serves as a comprehensive guide for researchers and practitioners in understanding and leveraging QSAR as a pivotal tool in rational drug design and chemical biology.