Al-Powered Molecular Docking in Modern Drug Discovery

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology in modern drug discovery by improving the speed, accuracy, and efficiency of molecular docking studies. Molecular docking is a computational technique used to predict the interaction between a drug molecule (ligand) and a target protein, helping researchers identify potential drug candidates. Traditional docking methods are often time-consuming and computationally intensive. The integration of AI, particularly machine learning and deep learning algorithms, has significantly enhanced docking accuracy by predicting binding affinity, optimizing ligand conformations, and reducing false-positive results. AI-powered molecular docking enables rapid virtual screening of millions of chemical compounds, thereby accelerating the identification of promising lead molecules. It also assists in predicting protein structures, analysing drug-target interactions, and improving hit-to-lead optimization. These advancements reduce the time and cost involved in drug development while increasing the probability of successful drug discovery. AI-driven docking has shown remarkable applications in discovering novel therapeutic agents for diseases such as cancer, Alzheimer's disease, infectious diseases, and emerging viral infections. Despite its advantages, AI-powered molecular docking faces challenges, including the requirement for high-quality datasets, model interpretability, and experimental validation of computational predictions. Future developments combining AI with molecular dynamics simulations, structural bioinformatics, and high-performance computing are expected to further revolutionize pharmaceutical research. This seminar highlights the principles, applications, advantages, limitations, and future prospects of AI-powered molecular docking, emphasizing its vital role in accelerating the development of safe and effective drugs for modern healthcare.