Transforming modern drug discovery with molecular modeling and artificial intelligence

Background: Techniques such as Molecular Modeling and Artificial Intelligence in drug discovery have revolutionized the field by improving target identification, virtual screening, lead optimization, and toxicity prediction. Although many computational techniques exist, the literature offers few systematic, comprehensive reviews of molecular modeling combined with AI-driven techniques. This article evaluates the application of molecular modeling technologies, such as Quantum Mechanics, Molecular Mechanics, Molecular Dynamics, molecular docking, and computer-aided drug design, using AI methods. Methodology: The author conducted a narrative review based on published information from scientific literature databases such as PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar. Results and Discussion: Molecular modeling enables structural and mechanistic models of ligand–target binding, while AI accelerates virtual screening, de novo molecular design, ADMET prediction, and lead optimization. Novel technologies such as AlphaFold, graph neural networks, and generative AI are expanding applied computational drug design. Nevertheless, several challenges remain, including data quality, model interpretability, external validation, dataset bias, overfitting, limited experimental validation, and translational reliability. Conclusion: Integrating molecular modeling and AI technologies represents a major step toward faster, more economical drug discovery processes. Future success will depend on increased use of standardized datasets, the development of explainable AI, and thorough experimental validation.