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QSAR—An Important In-Silico Tool in Drug Design and Discovery

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Advances in Computational Modeling and Simulation

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

QSAR (Quantitative structure–activity relationship) study is important thirst area in drug design and discovery via computational studies of chemistry. The hypothesis, alterations in structure of molecules reflect proportional variations in the pharmacological or biological activity, is the centre for focus of QSAR analysis. Currently one dimensional to six dimensional QSAR methods is available, and they are used in lead optimization, classification, and prediction of pharmacological or biological activity, pharmacokinetic properties, and toxicity of chemical compounds. The accomplishment of various models of QSAR depends on many factors or criteria’s such as input data accuracy, selection of descriptors, feature selection, model development, and validation as reported in OECD principle for QSAR. Validation is an important step in QSAR, and it is used to establish reliability and significance of a procedure of specific purpose. However, to be useful, QSAR models should be revealing and easily understandable in explaining the essential molecular features that plays a major role in the alteration of biological activity. Thus, the goal of the current chapter is to briefly discuss the principle, prerequisites to set up correct models, methods, validation, limitations of model applications, and the significance of QSAR in drug discovery.

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Authors and Affiliations

  1. Pharmaceutical Chemistry Unit, Faculty of Pharmacy, AIMST University, 08100, Semeling, Kedah, Malaysia

    Ravichandran Veerasamy

  2. Centre of Excellence for Biomaterial Engineering, AIMST University, 08100, Semeling, Kedah, Malaysia

    Ravichandran Veerasamy

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  1. Ravichandran Veerasamy
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Correspondence to Ravichandran Veerasamy .

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Editors and Affiliations

  1. Department of Civil Engineering, Birla Institute of Technology and Scienc, Pilani, Rajasthan, India

    Rallapalli Srinivas

  2. Department of Mathematics, Birla Institute of Technology and Scienc, Pilani, Rajasthan, India

    Rajesh Kumar

  3. Department of Biotechnology, Birla Institute of Technology and Scienc, Dubai, United Arab Emirates

    Mainak Dutta

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Veerasamy, R. (2022). QSAR—An Important In-Silico Tool in Drug Design and Discovery. In: Srinivas, R., Kumar, R., Dutta, M. (eds) Advances in Computational Modeling and Simulation. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-7857-8_16

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