Computational Chemistry Approaches in Drug Repurposing for Emerging Diseases

Abstract

Traditional de novo drug development is frequently too sluggish and costly to handle critical health crises, thereby exacerbating the worldwide burden of new infectious and non-infectious diseases. As a result, there is an increased demand for efficient and quick drug discovery methodologies. One exciting strategy to speed up treatment possibilities is medication repurposing, which involves finding new therapeutic uses for already-existing pharmaceuticals. Computational chemistry offers strong tools to make this process more efficient. current developments in computational approaches utilized for medication repurposing in the context of new diseases, such as molecular docking, virtual screening, pharmacophore mapping, quantitative structure-activity relationship (QSAR) modeling, and molecular dynamics simulations. To find new drug-target interactions in huge chemical libraries, it is necessary to combine molecular modeling with machine learning and artificial intelligence, which increases the predictive accuracy even further. The use of artificial silico methods in the quick identification of candidates such as remdesivir, chloroquine derivatives, and repurposed protease inhibitors was demonstrated in case studies involving recent health events like the COVID-19 pandemic. The wide applicability of computational repurposing has been demonstrated by its contributions to oncology, neurological illnesses, uncommon diseases, and infectious diseases, among others. Data quality, biological complexity, and validation in both experimental and clinical contexts are some of the remaining obstacles to converting computational predictions into clinical efficacy, notwithstanding recent achievements.