In-silico Drug Designing and Development for Genetic Disorders- A Review

Abstract

Genetic disorders arise from pathogenic genomic alterations that disrupt essential cellular, biochemical, and physiological functions, collectively imposing a substantial global health burden. Conventional drug discovery for inherited diseases is challenged by complex molecular defects, including loss- or gain-of-function mutations, altered protein stability, undruggable targets, limited patient populations, and high development costs. In-silico drug designing offers a mechanism-driven approach by integrating multi-omics data, structural bioinformatics, cheminformatics, artificial intelligence, and computational systems biology. This framework encompasses disease-target identification and prioritization through genomic, expression, and interaction network analyses; comparative structural assessment of wild-type and mutant proteins to elucidate mutation-induced changes in folding, binding sites, and molecular dynamics; virtual screening and molecular docking to identify potential therapeutic compounds; and molecular dynamics simulations to evaluate protein–ligand stability and binding energetics. Additionally, pharmacophore modeling and quantitative structure–activity relationship (QSAR) analysis facilitate the identification and optimization of promising chemical scaffolds, while network pharmacology and drug repurposing reveal multi-target therapeutic opportunities and potentially accelerate clinical translation. Early assessment of absorption, distribution, metabolism, excretion, and toxicity (ADMET) further supports drug developability. Integrating these computational strategies with multi-omics and systems-level insights can transform genetic disease drug discovery from conventional empirical screening into a mutation-aware, mechanism-based approach, advancing personalized and precision medicine.