Computational Chemistry Approaches in Drug Repurposing for Emerging Diseases

Abstract

Traditional drug discovery processes are cumbersome due to their high failure rates, lengthy and expensive development times, and the critical requirement for quick therapeutic interventions during new disease outbreaks. To combat these kinds of emergencies more quickly and cheaply, the practice of drug repurposing—the process of finding new therapeutic applications for already-approved or experimental pharmaceuticals—has been gaining popularity. A modern repurposing pipeline would not be complete without computational chemistry, which has allowed for the rapid identification of candidates and the systematic investigation of drug-target interactions. Pharmacophore mapping, molecular docking, QSAR modeling, and molecular dynamics simulations are some of the techniques that scientists use to screen large chemical libraries and make accurate predictions about binding affinities. By combining these methodologies with network pharmacology and systems biology, their applicability has been greatly enhanced, allowing for a more comprehensive understanding of polypharmacological effects and intricate disease pathways. The predictive capacity of computational models has been significantly improved by recent advancements in machine learning and artificial intelligence. This has made it easier to find new candidates and migrate from in silico forecasts to experimental validation.