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This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is no standard or introductory publication available that introduces this important topic to students of chemistry and pharmacy. With this in mind, the authors have carefully compiled this brief in order to provide a thorough and painless introduction to the fundamental concepts of QSAR/QSPR modelling. The brief is aimed at novice readers.

Inhaltsverzeichnis

Frontmatter

Chapter 1. QSAR/QSPR Modeling: Introduction

Development of predictive quantitative structure–activity relationship (QSAR) models plays a significant role in the design of purpose-specific fine chemicals including pharmaceuticals. Considering the wide application of different types of chemicals in human life, QSAR modeling is a useful tool for prediction of biological activity, physicochemical property, and toxicological responses of untested chemical compounds. Descriptors play a crucial role in the development of any QSAR model since they represent quantitatively the encoded chemical information. They not only help in the derivation of a mathematical correlation between the chemical structure information and the response of interest, but also enable exploration of the mechanistic aspect involved in a biochemical process. QSAR analysis is now widely employed as a rational tool for the prediction and design of chemicals of health benefits, industrial/laboratory process, or household applications.
Kunal Roy, Supratik Kar, Rudra Narayan Das

Chapter 2. Statistical Methods in QSAR/QSPR

QSAR/QSPR studies are aimed at developing correlation models using a response of chemicals (activity/property) and chemical information data in a statistical approach. The regression- and classification-based strategies are employed to serve the purpose of developing models for quantitative and graded response data, respectively. In addition to the conventional methods, various machine learning tools are also useful for QSAR/QSPR modeling analysis especially for studies involving high-dimensional and complex chemical information data bearing a nonlinear relationship with the response under consideration.
Kunal Roy, Supratik Kar, Rudra Narayan Das

Chapter 3. QSAR/QSPR Methods

QSAR/QSPR analysis started with different classical approaches constituting the core concept of predictive modeling analysis in the context of structure–activity relationships. Such classical techniques have been based on various postulates and hypotheses. With the passage of time, various dimensional features have taken an important role in diagnosis of chemical information and thereby in the development of successful QSAR/QSPR models. Development of computer technology has provided an essential support for easy and accurate implementation of complex molecular modeling calculations and data generation. The present chapter provides an account of the classical QSAR/QSPR approaches along with glimpses of two- and three-dimensional QSAR/QSPR techniques. The impact of the usage of computer and computational chemistry techniques in the paradigm of QSAR/QSPR has also been discussed.
Kunal Roy, Supratik Kar, Rudra Narayan Das

Chapter 4. Newer Directions in QSAR/QSPR

The QSAR/QSPR technique is now a widely practiced tool in chemical research both in the industry and academia. Because of the enormous potential applications of predictive modeling analysis, various newer methods have recently been developed to improve the usefulness and applicability of QSAR techniques. Binary QSAR, hologram QSAR (HQSAR), group-based QSAR (G-QSAR), multivariate image analysis (MIA)-based QSAR (MIA-QSAR), etc., are some of the new approaches in the realm of QSAR formalisms. Furthermore, QSAR techniques are also employed in various newer research areas in addition to the conventional drug design and predictive toxicology paradigm. QSAR models have been observed to be fruitful in modeling various property endpoints in the field of material informatics. In addition to that, predictive modeling of properties and/or toxicities of nanoparticles (NPs), cosmetics, peptides, ionic liquids, phytochemicals, etc., also represents the emerging application areas of the QSAR technique. This present chapter gives an overview of both the new methods and new application areas of QSAR studies.
Kunal Roy, Supratik Kar, Rudra Narayan Das
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