STRUCTURE-ACTIVITY RELATIONSHIP STUDIES IN PHARMACEUTICAL CHEMISTRY: ADVANCING DRUG DESIGN AND DEVELOPMENT

Abstract

Preoxygenation and the maintenance of adequate ventilation before tracheal intubation are foundational steps in the safe conduct of general anaesthesia1-2. The physiological rationale underlying preoxygenation is straightforward: room air breathing leaves the functional residual capacity (FRC) composed predominantly of nitrogen, with only a modest oxygen reservoir of approximately 450 mL. When a patient breathes 100% oxygen for an adequate period, nitrogen within the FRC is progressively washed out and replaced with oxygen, expanding the reservoir to nearly 3000 mL in a healthy adult. This “denitrogenation” markedly prolongs the duration of safe apnoea – the interval between the onset of apnoea following induction and neuromuscular blockade, and the point at which arterial oxygen saturation begins to fall to unsafe levels. Because induction of anaesthesia is almost invariably followed by a period without spontaneous ventilation, whether due to drug-induced apnoea, loss of airway tone, or the time required to achieve a laryngoscopic view, the size of this oxygen reserve determines the safety margin available to the anaesthesiologist before hypoxaemia supervenes. Failure of oxygenation and ventilation during this critical window remains one of the most consistently reported preventable causes of anaesthesia-related morbidity and mortality, as highlighted by the Fourth National Audit Project of the Royal College of Anaesthetists and the Difficult Airway Society, which identified airway and ventilation-related events as major contributors to catastrophic outcomes during general anaesthesia.1Structure-activity relationship (SAR) studies play a central role in pharmaceutical chemistry by establishing the connection between molecular structure and biological activity, thereby guiding rational drug design and development. This review aims to provide a comprehensive synthesis of recent advancements in SAR and their contribution to modern drug discovery, with emphasis on structural determinants, computational modelling, artificial intelligence integration, applications, challenges, and emerging trends. A comprehensive literature review approach was employed, involving the systematic identification, screening, and thematic classification of relevant peer-reviewed studies from major scientific databases. The findings reveal that SAR remains a fundamental framework in drug discovery, significantly enhanced by computational techniques such as QSAR modelling, molecular docking, and molecular dynamics simulations, which improve predictive accuracy and reduce experimental effort. Additionally, artificial intelligence and machine learning have transformed SAR into a data-driven discipline capable of analysing complex datasets and generating novel drug candidates. Despite these advancements, challenges, including biological complexity, data limitations, and translational gaps, continue to hinder drug development success. Overall, the integration of classical SAR principles with modern computational and AI-driven approaches is reshaping pharmaceutical research and holds significant potential for accelerating the discovery of safer, more effective, and personalised therapeutic agents.