Advances in Drug Discovery: Integrating CADD, Medicinal Chemistry, and Pharmacology

The swift progress of computational techniques has significantly impacted contemporary drug research and development. In silico methodologies, including molecular docking, molecular dynamics simulations, pharmacophore modeling, QSAR, and predictive ADMET profiling, are currently pivotal in expediting the discovery of bioactive drugs. These methods enhance conventional medicinal chemistry and experimental pharmacology by facilitating rational design, lowering costs, and decreasing failure rates in subsequent phases of drug development. Notwithstanding these advancements, obstacles persist in the appropriate integration of computational predictions with experimental validation to guarantee translational success. Addressing this gap necessitates interdisciplinary cooperation across computational chemistry, synthetic chemistry, and biological evaluations to establish stable and efficient drug discovery pipelines.This Research Topic aims to address the critical need for stronger integration between computational drug discovery approaches and experimental validation strategies. While in silico tools have become indispensable for identifying and optimizing therapeutic candidates, their translational reliability depends on robust experimental confirmation. The primary objective of this collection is to promote research that combines computational modeling with medicinal chemistry and biological evaluation to produce experimentally validated outcomes. Particular emphasis will be placed on studies demonstrating clear integration between computational predictions and experimental evidence, including synthesis, target engagement studies, mechanistic assays, pharmacological screening, or in vivo validation. We seek contributions highlighting innovative multidisciplinary workflows that improve hit identification, lead optimization, pharmacokinetic profiling, and therapeutic efficacy. Special attention will also be given to translational case studies where computational approaches directly informed experimental success in drug discovery and development. Importantly, submissions based solely on computational predictions without experimental validation will be considered outside the scope of this Research Topic, in accordance with journal policy and translational objectives.This Research Topic welcomes interdisciplinary contributions involving computational chemistry, medicinal chemistry, pharmacology, and translational drug discovery. Topics of interest include, but are not limited to: molecular docking and molecular dynamics simulations, pharmacophore modeling, QSAR studies, ADMET prediction, structure-based and ligand-based drug design, synthetic optimization of bioactive compounds, target validation, mechanistic pharmacology, and experimental therapeutic evaluation.Submissions integrating computational predictions with experimental validation through biochemical assays, cell-based studies, animal models, or pharmacokinetic investigations are particularly encouraged. Manuscripts focused exclusively on theoretical or in silico findings without accompanying experimental evidence will not be considered.The collection also welcomes studies on natural product-inspired drug discovery, nanomedicine, hybrid computational-experimental platforms, and emerging AI-assisted methodologies that demonstrate tangible translational relevance. Accepted manuscript types include original research articles, reviews, mini-reviews, perspectives, and methodological studies. Topic editors: Dr. SUMEER AHMED, Dr. Ajmal Rashid Bhat, Siva S. Panda Source: https://www.frontiersin.org/research-topics/81061/advances-in-drug-discovery-integrating-cadd-medicinal-chemistry-and-pharmacology