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Drug discovery has gone through several paradigm shifts over the past century, from having diseases with no curable medicine to medicines being studied for a span of two to three decades before being functional. However, because decades are too long to wait for a medicine, researchers have taken advantage of bioinformatics and reduced the time of identification of possible drug molecule candidates from decades to mere weeks. Apart from identification of drug molecules, it is equally important to have identification of the drug target (protein and/or DNA molecule) that pertains to the disease and to which drug molecules would bind. To properly and efficiently identify the target molecule, sequence analysis (of DNA and/or protein) needs to be done with utmost accuracy and within the limits of time. One such technique that is used to analyze biological sequences is known as Basic Local Alignment Search Tool (BLAST). It identifies sequences of interest that share similarity with other sequences, thus allowing the pool of putative drug targets to increase. BLAST also helps in the prediction of 3D structures of proteins in silico, which again saves decades of time and millions of dollars in experimentation. However, the final validation of predicted proteins (drug targets) is mandatorily done via experimentation, although BLAST saves time and money by the process of elimination and concentrates the pool of candidates. In this chapter, we briefly explain the background concepts required to comprehensively understand the functions of BLAST, followed by in-depth explanation of how the BLAST algorithm works. We also explain how use of BLAST is pivotal in identifying drug targets using computer aided drug discovery.

Identification of potential drug compounds and drug–target interactions are fundamental steps in the drug-designing process. However, the conventional process of drug discovery is expensive, time consuming, and insufficient to explore novel interactions and activity predictions. Thus arises the need for in silico chemoinformatics approaches with improved strategies that are useful to elevate predictive performance in drug designing. The objective of this study is to discuss the current approaches and major chemoinformatics tools available to perform data extraction, structure-based virtual screening, designing of new molecule identification using quantitative structure–activity relationship (QSAR), and applications of machine learning (ML) in biological or chemical evaluation. Recently, a large amount of research has been done and considered for applications of chemoinformatics in drug discovery. Reviewing various tools and techniques, the chapter mainly discusses different aspects such as (1) compound databases/libraries, (2) important tools of structure-based and ligand-based virtual screening, including molecular docking and pharmacophore-designing tools, (3) different toolkit and ML algorithms with a strong emphasis on QSAR modeling, and (4) future challenges and directions in the present state. This study would likely provide insights into advanced approaches in utilizing effective strategies and the generation of favorable bioprediction methods in drug designing.