Drug discovery has evolved from empirical experimentation into a data-driven, computation-intensive discipline, yet challenges of high attrition, rising costs, and translational failure persist. Addressing this complexity demands a mechanistic and predictive approach to molecular design.
Computer-Aided Drug Design: Principles, Methods, and Applications in Drug Discovery presents a rigorous and integrated framework for modern CADD, encompassing molecular modeling, QSAR, docking, molecular dynamics, virtual screening, pharmacophore modeling, and fragment-based design. Rather than isolating techniques, the text connects algorithms, physicochemical principles, and biological systems to explain how computational methods guide rational drug development .Designed for postgraduate pharmacy students, doctoral researchers, and professionals, the book delivers conceptual clarity and algorithmic understanding while supporting structured academic responses and practical workflow competence.Its distinguishing strength lies in mechanistic depth and decision-focused reasoning-each method is examined in terms of its assumptions, parameter sensitivity, and real-world applicability, bridging computational prediction with pharmaceutical relevance.As drug discovery increasingly integrates artificial intelligence and large-scale data, this work provides a clear intellectual pathway to navigate and advance the future of rational therapeutic design.
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