AI-Assisted Molecular Docking in Drug Discovery "Transforming Drug Discovery through Artificial Intelligence

Abstract

Artificial Intelligence (AI) has revolutionized pharmaceutical research by enhancing the speed and accuracy of drug discovery. Molecular docking is a computational technique used to predict the interaction between a drug molecule (ligand) and a target protein (receptor). The integration of AI with molecular docking improves virtual screening, predicts binding affinity more accurately, and accelerates the identification of potential drug candidates while reducing time, cost, and experimental effort. This review was conducted by analyzing published research articles, review papers, and scientific databases related to artificial intelligence, molecular docking, and computer-aided drug design (CADD). Information from peer-reviewed journals was used to assess the current applications, benefits, and limitations of AI-assisted molecular docking in drug discovery. The findings indicate that AI-assisted molecular docking significantly improves the prediction of protein–ligand interactions, enhances virtual screening efficiency, and accelerates lead compound identification. AI-based models reduce false-positive predictions, improve binding affinity estimation, and support the discovery of novel therapeutic agents for diseases such as cancer, diabetes, Alzheimer's disease, and infectious diseases. AI-assisted molecular docking is an advanced and promising approach in pharmaceutical chemistry that enhances the drug discovery process by improving prediction accuracy, reducing development time, and lowering research costs. The integration of AI with molecular docking has the potential to accelerate the development of safer, more effective, and personalized medicines.