Abstract:

Computational Drug Design represents a transformative approach in modern pharmaceutical research, where chemistry, biology, and computer science converge to accelerate the discovery of safer and more effective therapeutic molecules. Instead of relying solely on traditional trial-and-error experimentation, this strategy employs advanced algorithms, molecular modelling, and artificial intelligence to predict how drug candidates interact with biological targets at the atomic level. By simulating molecular behaviour, researchers can identify promising compounds, optimise lead structures, and evaluate pharmacokinetic and toxicity profiles before laboratory synthesis, significantly reducing time, cost, and experimental failure. Techniques such as molecular docking, virtual screening, quantitative structure–activity relationship (QSAR) analysis, molecular dynamics simulations, and ADMET prediction enable precise understanding of drug–target interactions and biological responses. Ultimately, it reshapes drug discovery into a smarter, faster, and more predictive scientific process, paving the way for next-generation therapeutics and improved global healthcare outcomes.

Keywords:

Computational Drug Design, Molecular Docking, Virtual Screening, QSAR, ADMET Prediction, Molecular Modelling, Drug Discovery, Lead Optimisation, In Silico Studies, Artificial Intelligence.

Aim/Objectives

Methods