Computer Aided Drug Discovery Driven Approaches for Discovery of Antimicrobial Agents

Abstract

Computer-Aided Drug Design/Discovery (CADD) is a powerful and evolving field that employs computational techniques to facilitate the discovery and optimization of new therapeutic agents. The global problem of antibiotic resistance has been addressed by recent developments in Computer-Aided Drug Design (CADD), which have greatly improved the development of antimicrobial agents. Using computational techniques such as molecular docking, quantitative structure-activity relationship (QSAR) modeling, virtual screening, quantum computing and molecular dynamics simulations, CADD provides novel approaches for the logical design of novel antimicrobial compounds. These methods greatly impact on the time and expense associated with conventional drug discovery procedures by enabling the identification of possible drug candidates, the optimization of their pharmacokinetic characteristics, and the prediction of drug-target interactions. There are still a number of restrictions on using CADD for antimicrobial drug discovery, even with these developments. Additionally, problems with bioavailability, toxicity, and off-target effects cause many antimicrobial agents to fall short of in silico predictions in terms of clinical efficacy. With the combination of artificial intelligence (AI) and machine learning (ML) improving predictive accuracy and efficiency, the future of CADD in antimicrobial development looks bright. AI/ML models can offer fresh perspectives on the mechanisms underlying microbial resistance and further optimize drug design. Furthermore, the creation of hybrid computational methods that combine experimental data and CADD may accelerate the discovery of new antimicrobial agents. The future of developing antimicrobial drugs will be shaped by the computational resources through ongoing evolution.