Density Functional Theory in Computer-Aided Drug Design: A Systematic Review of Applications, Methodological Trends, and Integration with Molecular Modeling Workflows

Density Functional Theory (DFT) is becoming an increasingly valuable tool in computer-aided molecular design because it provides quantum-level insight that can strengthen practical drug discovery workflows. Beyond its traditional role in electronic structure analysis, DFT is now widely used to support reactivity assessment, binding interpretation, and molecular descriptor generation within integrated in silico strategies. This systematic review examines how DFT has been applied in recent drug discovery research, with particular attention to methodological trends, therapeutic targets, and its integration with other computational approaches. Following PRISMA 2020 guidelines, studies published between January 2021 and February 2025 were identified through searches of Google Scholar, PubMed/MEDLINE, Web of Science, Scopus, and IEEE Xplore. Of 815 records screened, 81 studies met the inclusion criteria. B3LYP and 6-31G(d,p) remained the most commonly used functional and basis set, appearing in 67.9% and 71.6% of studies, respectively. DFT was most often combined with molecular docking (76.5%), ADMET prediction (55.6%), and molecular dynamics (34.6%), highlighting its growing role in multi-method drug design workflows. The main application areas were infectious diseases, cancer, and metabolic disorders. Overall, the evidence suggests that DFT is no longer used primarily as a stand-alone quantum chemical method, but rather as an integrated component of modern molecular design pipelines that supports mechanistic understanding and rational compound optimization. Future advances will likely come from closer integration with machine learning, better treatment of conformational flexibility, and more realistic solvent-aware simulations.