AI-Based Virtual Screening for Identifying Novel Drug Candidates

VR screening uses computers to find large databases of chemical compounds and predict their potential as medications based on factors such as binding affinity, pharmacokinetics, and toxicity. This accelerates the process of discovering novel medications by concentrating on compounds with a higher likelihood of efficacy. This results in decreased expenses and increased effectiveness over an extended period. The study demonstrates the utilization of many artificial intelligence techniques in virtual screening, including deep learning networks, machine learning models, and other sophisticated computer technologies. The analysis examines the training of AI models on diverse datasets, incorporating data from existing databases, research publications, and experimental studies. This demonstrates the potential of AI in discovering novel chemicals for the treatment of cancer, infectious diseases, neurological disorders, and uncommon ailments. There are limitations associated with utilizing Molecular structure-based CADD methods. This research reviews current tools, applications, and methods for medication production speed and cost reduction. Structure-Based Virtual Screening (SBVS) is essential to medication development, according to research.