Kunal Roy - Böcker

Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. Includes numerous practical examples related to QSAR methods and applications Follows the Organization for Economic Co-operation and Development principles for QSAR model development Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools

In Silico Drug Design: Repurposing Techniques and Methodologies explores the application of computational tools that can be utilized for this approach. The book covers theoretical background and methodologies of chem-bioinformatic techniques and network modeling and discusses the various applied strategies to systematically retrieve, integrate and analyze datasets from diverse sources. Other topics include in silico drug design methods, computational workflows for drug repurposing, and network-based in silico screening for drug efficacy. With contributions from experts in the field and the inclusion of practical case studies, this book gives scientists, researchers and R&D professionals in the pharmaceutical industry valuable insights into drug design.

Discusses the theoretical background and methodologies of useful techniques of cheminformatics and bioinformatics that can be applied for drug repurposing Offers case studies relating to the in silico modeling of FDA-approved drugs for the discovery of antifungal, anticancer, antiplatelet agents, and for drug therapies against diseases Covers tools and databases that can be utilized to facilitate in silico methods for drug repurposing

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates.

Presents chemometrics, cheminformatics and machine learning methods under a single reference Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design Highlights special topics of computational drug design and available tools and databases

Cheminformatic Modelling and Data Gap Filling for a Green and Sustainable Environment covers the theory and practices of chemical informatics, focusing on modeling various properties and endpoints related to chemicals for improved chemical management and the design of safer chemicals to promote environmental sustainability. Across four sections, the book outlines modeling techniques such as quantitative structure-property relationship (QSPR), read-across, and machine learning for modeling environmental endpoints of chemicals. OECD guidelines are discussed and considered for model development and validation, documentation using the QSAR modeling reporting format (QMRF), and regulatory requirements for result presentation.The book offers full datasets, algorithm information, and real-world case studies for all models, along with worked examples. It will serve as an essential resource for chemists and environmental scientists working in green and sustainable chemistry, but will be a great resource for students and academics at graduate level and above studying cheminformatics. This book will also be of interest to researchers developing new and sustainable chemicals and for decision-makers looking to make industrial processes more sustainable.Presents multiple algorithms for QSPR models and machine learning methods for modeling environmental endpointsDiscusses crucial emerging topics in sustainable chemistry, such as mixture property modeling, microplastic toxicity modeling, and natural language models for toxicity and ecotoxicity predictionProvides a comprehensive framework for modeling physicochemical properties, environmental thresholds, and acute and chronic toxicity endpointsIncludes more than 20 real-world case studies, featuring datasets for environmental endpoints, with examples of model development and methodology

This volume focuses on computational modeling of the ecotoxicity of chemicals and presents applications of quantitative structure–activity relationship models (QSARs) in the predictive toxicology field in a regulatory context.

This volume focuses on computational modeling of the ecotoxicity of chemicals and presents applications of quantitative structure–activity relationship models (QSARs) in the predictive toxicology field in a regulatory context.

This essential volume explores a variety of tools and protocols of structure-based (homology modeling, molecular docking, molecular dynamics, protein-protein interaction network) and ligand-based (pharmacophore mapping, quantitative structure-activity relationships or QSARs) drug design for ranking and prioritization of candidate molecules in search of effective treatment strategy against coronaviruses. Beginning with an introductory section that discusses coronavirus interactions with humanity and COVID-19 in particular, the book then continues with sections on tools and methodologies, literature reports and case studies, as well as online tools and databases that can be used for computational anti-coronavirus drug research. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical detail and implementation advice that ensures high quality results in the lab. Comprehensive and timely, In Silico Modeling ofDrugs Against Coronaviruses: Computational Tools and Protocols is an ideal reference for researchers working on the development of novel anti-coronavirus drugs for SARS-CoV-2 and for coronaviruses that will likely appear in the future.

This essential volume explores a variety of tools and protocols of structure-based (homology modeling, molecular docking, molecular dynamics, protein-protein interaction network) and ligand-based (pharmacophore mapping, quantitative structure-activity relationships or QSARs) drug design for ranking and prioritization of candidate molecules in search of effective treatment strategy against coronaviruses. Beginning with an introductory section that discusses coronavirus interactions with humanity and COVID-19 in particular, the book then continues with sections on tools and methodologies, literature reports and case studies, as well as online tools and databases that can be used for computational anti-coronavirus drug research. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical detail and implementation advice that ensures high quality results in the lab. Comprehensive and timely, In Silico Modeling ofDrugs Against Coronaviruses: Computational Tools and Protocols is an ideal reference for researchers working on the development of novel anti-coronavirus drugs for SARS-CoV-2 and for coronaviruses that will likely appear in the future.

This second edition volume expands on the previous edition with updated descriptions on different computational methods encompassing ligand-based, structure-based, and combined approaches with their recent applications in anti-Alzheimer drug design. Different background topics like recent advancements in research on the development of novel therapies and their implications in the treatment of Alzheimer’s Disease (AD) have also been covered for completeness. Special topics like basic information science methods for insight into neurodegenerative pathogenesis, drug repositioning and network pharmacology, and online tools to predict ADMET behavior with reference to anti-Alzheimer drug development have also been included. In the Neuromethods series style, chapter include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.Cutting-edge and thorough, Computational Modeling of Drugs Against Alzheimer’s Disease, Second Edition is a valuable resource for all researchers and scientists interested in learning more about this important and developing field.

This second edition volume expands on the previous edition with updated descriptions on different computational methods encompassing ligand-based, structure-based, and combined approaches with their recent applications in anti-Alzheimer drug design.

CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY Explore chemometric and cheminformatic techniques and tools in aquatic toxicology Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms. You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods. Readers will also benefit from the inclusion of: A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data miningAn exploration of aquatic toxicity databases, chemometric software tools, and webserversPractical examples and case studies to highlight and illustrate the concepts contained within the bookA concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity dataPerfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.

This volume describes different computational methods encompassing ligand-based approaches (QSAR, pharmcophore), structure-based approaches (homology modeling, docking, molecular dynamics simulation), and combined approaches (virtual screening) with applications in anti-Alzheimer drug design.

This volume describes different computational methods encompassing ligand-based approaches (QSAR, pharmcophore), structure-based approaches (homology modeling, docking, molecular dynamics simulation), and combined approaches (virtual screening) with applications in anti-Alzheimer drug design.

This detailed book showcases recent advances in computational design of multi-target drug candidates involving various ligand and structure-based strategies.

Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline—from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug–target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.

This brief offers an introduction to the fascinating new field of quantitative read-across structure-activity relationships (q-RASAR) as a cheminformatics modeling approach in the background of quantitative structure-activity relationships (QSAR) and read-across (RA) as data gap-filling methods.

This contributed volume focuses on the application of machine learning and cheminformatics in predictive modeling for organic materials, polymers, solvents, and energetic materials.

This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics.

This contributed volume explores the integration of machine learning and cheminformatics within materials science, focusing on predictive modeling techniques.

This brief introduces the readers of predictive cheminformatics to the concept of cliffs in the structure-activity landscape, which may greatly affect the data set modelability and the quality of predictions, hence generating disappointment from the performance of Quantitative Structure-Activity Relationship (QSAR) models. Although QSAR models are based on the assumption of a smooth activity landscape, where similar molecules are expected to have similar activities, some similar molecules can occasionally exhibit large differences in activity (for example, 100-fold). The definition of similarity for identifying activity cliffs may be based on chemical fingerprints or descriptors (classical activity cliffs), substructures (chirality cliffs, matched molecular pair cliffs), three-dimensional structure-based cliffs (3D cliffs), or the target-set-dependent potency difference. Some prediction outliers, even within the applicability domain of QSAR models, may arise due to the activity cliff (AC) behavior. In addition to compound pairs, activity cliffs may also be visualized in coordinated networks forming AC clusters. Despite using high-quality data, the data set's modelability may be significantly compromised in the presence of ACs, among other factors. The modelability of the dataset has been studied using different approaches like modelability index (MODI), weighted modelability index (WMODI), rivality index, etc. At the same time, the applicability domain of QSAR models is evaluated using a variety of methods, including leverage, principal components, standardization methods, and distance to the model in X-space, among others. Different methods for identifying activity cliffs have been proposed, such as the structure-activity landscape index (SALI), the structure-activity relationship (SAR) index, and the structure-activity similarity (SAS) maps. Recently, the Arithmetic Residuals in K-Groups Analysis (ARKA) has been shown to be successful in identifying activity cliffs. This approach has also been applied in small data set classification modeling. A multiclass ARKA approach has also been developed for its possible application in regression-based problems by integrating it with the quantitative read-across structure-activity relationship (q-RASAR) framework. This book showcases the evolution and the current status of the concept of activity cliffs as relevant to QSAR predictions and indicates the future directions in the research on activity cliffs. Researchers in the fields of medicinal chemistry, predictive toxicology, nanosciences, food science, agricultural sciences, and materials informatics should benefit from the concept of activity cliffs, impacting model-derived predictions.

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.

The book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included – two areas which have only recently been able to exploit this versatile tool. This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR.

The book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included – two areas which have only recently been able to exploit this versatile tool. This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR.