In Silico Molecular Docking of Bioactive Molecules: An Overview

Abstract

Computational approaches have become indispensable in modern drug discovery and development. Among them, in silico molecular docking serves as a vital tool for predicting the binding affinity, orientation, and interactions of bioactive molecules within the active site of biological targets. Docking helps understand structure–activity relationships (SARs), guides lead optimization, and reduces experimental costs by prioritizing promising compounds before synthesis and biological evaluation. This review provides an overview of molecular docking theory, types of docking algorithms, commonly used software, validation parameters, and examples of applications in studying bioactive natural products and synthetic molecules. The integration of docking with molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, and in vitro assays is also discussed. Challenges such as receptor flexibility, scoring inaccuracies, and water-mediated interactions are considered, along with emerging advances in artificial intelligence (AI) and deep learning-based docking algorithms.

Overall, in silico molecular docking remains a cornerstone computational method for rational drug design and bioactivity prediction of natural and synthetic molecules.