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Due to the advances of various methods for the prediction of toxicity of organic compounds and ionic liquids (ILs), it is necessary to review these methods for scientists and students. It is essential to compare the advantages and shortcomings of these methods. Since many organic compounds and ILs are synthesized each year, this book introduces suitable models for the assessment of their toxicities. This book reviews the best predictive methods for the prediction of toxicity of organic compounds and ILs, which were derived by in vitro or in vivo experiments. Different available quantitative structure‐toxicity relationship (QSTR) models based on various descriptors have been discussed to predict toxicity parameters such as LD50 (50% lethal dose), EC50 (the concentration of the desired IL that produces mortality of 50 percent of the bacterial population) and log(IGC50-1) (logarithm of 50% growth inhibitory concentration of T. pyriformis) of various classes of organic compounds and ILs. The reliability of these methods is compared and discussed. Each chapter contains some complimentary problems with their answers, which can improve the experience of students and researchers. The introduced subjects are suitable for advanced students in chemistry, biochemistry, medicinal chemistry, and chemical engineering.
A wide range of chemical products (especially fine chemicals) are important for a healthy and enjoyable modern life; therefore efficient syntheses of these materials are essential. Traditional stoichiometric processes need to be replaced by modern catalytical methods in the change to sustainable chemistry and the production of lower amounts of waste. This book summarizes the wide variety of catalytic methods that have been developed and applied on an industrial scale in recent years to fulfill this goal. The synthesis of compound classes such as pharmaceuticals, agrochemicals, flavoring, and fragrance compounds as well as food additives such as vitamins exemplify the use of these modern catalytic methods in the modern chemical industry.
"Scientific Data: A 50 Steps Guide using Python" is your guide towards experimental scientific data. It aims to bridge the gap between classical natural sciences as taught in universities and the ever-growing need for technological/digital capabilities, particularly in industrial research. Topics covered include instructions for setting up a workspace, guidelines for structuring data, examples for interfacing with results files and suggestions for drawing scientific conclusions therefrom. Additionally, concepts for designing experiments and visualizing the corresponding results are highlighted next to ways of extracting meaningful characteristics and leveraging those in terms of multi-objective optimizations. The concise problem-solution-discussion structure used throughout supported by Python code snippets emphasizes the work’s focus on practitioners. This guide will provide you with a solid understanding of how to process and understand experimental data within a natural scientific context while ensuring sustainable use of your findings and processing as seen through a programmer’s eyes.
Of the thousands of novel compounds that a drug discovery project team invents and that bind to the therapeutic target, typically only a fraction of these have sufficient ADME/Tox properties to become a drug product. Understanding ADME/Tox is critical for all drug researchers, owing to its increasing importance in advancing high quality candidates to clinical studies and the processes of drug discovery. If the properties are weak, the candidate will have a high risk of failure or be less desirable as a drug product. This book is a tool and resource for scientists engaged in, or preparing for, the selection and optimization process. The authors describe how properties affect in vivo pharmacological activity and impact in vitro assays. Individual drug-like properties are discussed from a practical point of view, such as solubility, permeability and metabolic stability, with regard to fundamental understanding, applications of property data in drug discovery and examples of structural modifications that have achieved improved property performance. The authors also review various methods for the screening (high throughput), diagnosis (medium throughput) and in-depth (low throughput) analysis of drug properties. - Serves as an essential working handbook aimed at scientists and students in medicinal chemistry - Provides practical, step-by-step guidance on property fundamentals, effects, structure-property relationships, and structure modification strategies - Discusses improvements in pharmacokinetics from a practical chemist's standpoint
QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment. Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment. - Provides comprehensive content about the QSAR techniques and models in facilitating the safety evaluation of drugs and consumer products and risk assesment of environmental chemicals - Includes some of the most cutting-edge methodologies such as deep learning and machine learning for QSAR - Offers detailed procedures of modeling and provides examples of each model's application in real practice
Discusses the applications of classical QSAR and molecular modeling analysis to the discovery of new agrochemicals. Examines hydrophobicity parameters derived from various partitioning systems. Includes chapters focusing on the use of three-dimensional QSAR analyses such as CoMFA and DISCO. Presents information on the use of QSAR to study transport and toxicology of agrochemicals.