MOLECULAR DOCKING AND VIRTUAL SCREENING FOR DRUG DISCOVERY IN INFECTIOUS DISEASES: LEVERAGING COMPUTATIONAL TOOLS FOR TARGET IDENTIFICATION

Abstract

Molecular docking and virtual screening have, indeed, contributed much to drug discovery by providing fast computational strategies for the identification of potential therapeutic compounds against infections. These methods allow for rapid screening of large chemical libraries so that researchers can predict the binding affinity of small molecules with respect to biological targets. In addition, artificial intelligence and machine learning developments have further improved accuracy and efficiency in virtual screening pipelines. This study covers the integration of molecular docking, virtual screening, and deep learning algorithms into drug discovery. The case studies presented offer insights from computational drug screening against bacteria, viruses, and fungi in the context of accelerating drug development. The methodologies of molecular docking and virtual screening have substantially accelerated the discovery of novel therapeutic agents aimed at combating infectious diseases. The application of deep learning and AI-enhanced predictive modeling has refurbished traditional computational screening approaches with greater accuracy. The future course of research may well involve increasing the accuracy of AI-based docking models and the ability to synergistically mine multi omic data for further improvements in the drug discovery pipeline. The fact that more and more structural and omics data became available for developing personalized molecular docking is also highly relevant. It means that by integrating genomic, proteomic, and metabolomic data, drug discovery can be much more patient-specific and thus improved in therapeutic efficacy. These techniques in high-throughput screening are straightforwardly integrated into computational modeling, allowing for the rapid identification of lead compounds for the treatment of infectious diseases.