Uma revisão da literatura sobre abordagens computacionais na identificação de alvos biológicos para o desenvolvimento de fármacos

Abstract

This study presents a systematic literature review on computational approaches applied to the identification of biological targets in drug discovery. Techniques such as virtual screening, molecular docking, QSAR, artificial intelligence, and machine learning were analyzed in terms of their effectiveness, integration, and experimental validation. The research, conducted in the Web of Science database, identified 38 relevant studies published between 2015 and 2025, with a particular emphasis on receptor-based approaches (SBVS). The application of molecular dynamics simulations, quantum methods (DFT), and the lack of studies focused on neglected diseases were also discussed. The study concludes that the combination of in silico modeling and experimental validation is essential for the reliability of findings, highlighting the importance of integrating computational, structural, and biological data to make the drug discovery process more efficient, accessible, and comprehensive.