Approaches, Strategies, and Advances in Computational Drug Discovery and Drug Repurposing

Drug discovery is a challenging, expensive, and time-consuming procedure that has an extremely low success rate. When it comes to the early phases of drug discovery, computational techniques are very beneficial since it substantially reduce attrition rates in the drug development process. The use of artificial intelligence, particularly machine learning and deep learning methodologies, has become in grained in the drug development process. Computational drug discovery and development is experiencing tremendous advancement in recent times. These approaches effectively exploits known targets, drugs, pathways or disease biomarkers by utilizing 28various bioinformatics, chemo-informatics, system biology and network biology tools. Ligand-based and structure-based approaches are widely used computational methods in the drug discovery process. Three-dimensional quantitative structure activity relationship (3D QSAR) and pharmacophore modeling are most commonly used techniques in ligand-based approaches for giving predictive models for lead generation and optimization. Structure-based approaches use structural data obtained experimentally or through computational homology modeling. Molecular docking, structure-based virtual screening (SBVS) and molecular dynamics (MD) are frequently used SBDD strategies for analysis of molecular recognition events of binding energetic, molecular interactions and induced conformational changes. Drug repurposing is the program of drug discovery, which involves finding new indications for pre-existing marketed drugs, failed drugs or withdrawn drugs. Drug repurposing has lately acquired recognized as an effective alternative capable of delivering medication. The chapter highlights diverse drug repurposing tactics and overviews commonly used resources, open source databases/tools, workflow systems, pipelines in the form of codes, software tools. Computer based methodologies that are comprehensively used in drug repurposing studies have been summarized. Various challenges and limitations met in computational drug repurposing studies are also addressed along with further research directions.