Advancements in Computational Chemistry for Personalized Medicine and Drug Discovery

Computational chemistry has become an essential component in the advancement of personalized medicine and drug discovery, enabling the prediction of drug-target interactions, optimization of drug candidates, and acceleration of therapeutic development. However, the field still faces a knowledge gap in fully integrating molecular modeling, artificial intelligence, and multi-omics data for accurate and individualized treatment design. This review systematically explores computational methods such as molecular docking, QSAR, molecular dynamics simulations, and machine learning approaches applied in drug discovery. By analyzing case studies on cancer and cardiovascular diseases, the study reveals that computational tools significantly enhance the precision of drug design, reduce development costs, and improve the effectiveness of tailored therapies. Results demonstrate that integrating data-driven models and computational chemistry optimizes both target identification and compound screening, directly contributing to more effective and safer personalized treatments. The findings underscore the importance of interdisciplinary collaboration, real-time data analysis, and regulatory adaptation to fully exploit the potential of computational chemistry in modern healthcare.