Integrating Molecular Modeling, Cheminformatics, and High-Throughput Screening: Paving the Way for the Future of Drug Discovery

The process of drug discovery is slowly entering the era of change, wherein more efficient and effective approaches to identify the potential therapeutic compounds are sought. This review article explores the future of drug discovery by emphasizing the integration of three pivotal approaches: molecular modeling, cheminformatics and High Throughput Screening (HTS). Structure-based and ligand-based drug design use computations for the prediction of molecular behavior, some of which include molecular docking and Quantitative Structure Activity Relationship (QSAR) analysis. Integrated with data mining and virtual screening of chemical databases, cheminformatics refines the picture of structure-activity relationship or SAR in drug design. High-throughput screening advances new approaches to testing large compendia of compound libraries for biological activity while integrating automated augmented intelligence for data acquisition and analysis. However, there are some drawbacks that affiliates HTS, including high false-positive results, which require secondary tests. This article specifically focuses on the compatibility of these methodologies where examples are provided on cases where their integration yielded promising drug leads. In the future, existing and advanced technologies like artificial intelligence, machine learning, genetics and Genomic data using CRISPR etc., will be helpful for the improvement of pharmaceutical productivity and will help cutting down on the drug development time. In conclusion, this review emphasizes the role of the multi-disciplinary approach in casting the advancement within drug discovery and try to call for further investment these inter-disciplinary strategies as a way of meeting future health care needs.