This book provides an up-to-date overview of data driven and evidence-based empirical approaches in the efficient application of chemodescriptors and biodescriptors. Currently there is a steady increase in the use of data analytics and model-based decision support systems in basic and applied research in chemoinformatics, bioinformatics, pharmaceutical drug design, predictive toxicology, and computational biology. Since there are a plethora of modeling methods and a large number of chemodescriptors and biodescriptors available today, robust statistical and machine learning methods are applied throughout. In addition, the development of statistically robust predictive models in rank deficient cases using chemodescrip tors and biodescriptors is discussed. Readers are provided with an up-to-date overview of the theoretical background, calculation methods, and proper use of chemodescriptors and biodescriptors in model building, with special emphasis on computer-assisted organic synthesis, new drug discovery, hazard assessment of chemicals, and computational biology of emerging global pathogens. The book also discusses the applications of alignment-free sequence descriptors (AFSDs) in vaccine design and the characterization of emerging global pathogens such as the Zika virus and SARS-CoV-2. The utility of molecular fragment-based descriptors in building useful quantitative structure-activity relationship (Q)SAR) models is detailed as is the use of mathematical structural descriptors in chemical synthesis planning.

Contains the needed background, calculation methods, and proper use of chemodescriptors and biodescriptors in model building with special emphasis on computer-assisted organic synthesis Provides a comprehensive review of topological and quantum chemical descriptor calculation methods for basic chemical research, drug design, and predictive toxicology Includes a review of calculation methods and the use of quantum chemical indices derived from different levels of ab initio techniques, including density functional theory (DFT) and information theory-based descriptors

Autorentext

Dr. Subhash C. Basak is a retired Adjunct Professor at the Department of Chemistry and Biochemistry, University of Minnesota Duluth, USA. Dr. Basak received his PhD in biochemistry from the University of Calcutta, India. During the past four decades he has pioneered research in the development of novel mathematical chemodescriptors and biodescriptors principally via applications of discrete mathematics on chemical and biological systems. He also published extensively on the use of such descriptors along with proper statistical methods in drug design, predictive toxicology, characterization of emerging global pathogens as well as nanotoxicology. He has collaborated with over seventy research scientists located in four continents: Asia, Europe, North America, and South America, whom he fondly calls members of his "virtual team". Such collaborations resulted in the publication of more than 300 journal papers and 35 book chapters. He is a full member of the International Academy of Mathematical Chemistry (IAMC) and a former Editor-in-Chief (EIC) of the international journal: Current Computer Aided Drug Design (CCADD).

Inhalt

Approaching modeling in chemoinformatics and bioinformatics using mathematical descriptors: Some comments on the emerging landscape and future directions.- Hierarchy of descriptors: from topology to bio-descriptors.- Chirality Descriptors for Numerical Characterization of Enantiomers and Diastereomers.- QSAR Modeling Using Molecular Fragment Descriptors.- Quantitative Structure-Activity Analysis using Conceptual DFT and Information Theory-based descriptors.- Complexity of Molecular Ensembles with Basak's Indices.- Descriptors from calculated stereo-electronic properties and molecular electrostatic potentials (MEPs) may provide a powerful interaction pharmacophore for drug discovery.- Network-based molecular descriptors for protein dynamics and allosteric regulation.