Current Trends In Computational Modeling For Drug Discovery
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Current Trends in Computational Modeling for Drug Discovery
This contributed volume offers a comprehensive discussion on how to design and discover pharmaceuticals using computational modeling techniques. The different chapters deal with the classical and most advanced techniques, theories, protocols, databases, and tools employed in computer-aided drug design (CADD) covering diverse therapeutic classes. Multiple components of Structure-Based Drug Discovery (SBDD) along with its workflow and associated challenges are presented while potential leads for Alzheimer’s disease (AD), antiviral agents, anti-human immunodeficiency virus (HIV) drugs, and leads for Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) disease are discussed in detail. Computational toxicological aspects in drug design and discovery, screening adverse effects, and existing or future in silico tools are highlighted, while a novel in silico tool, RASAR, which can be a major technique for small to big datasets when not much experimental data are present, is presented. The book also introduces the reader to the major drug databases covering drug molecules, chemicals, therapeutic targets, metabolomics, and peptides, which are great resources for drug discovery employing drug repurposing, high throughput, and virtual screening. This volume is a great tool for graduates, researchers, academics, and industrial scientists working in the fields of cheminformatics, bioinformatics, computational biology, and chemistry.
Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning
Author: Tahmeena Khan
language: en
Publisher: Bentham Science Publishers
Release Date: 2024-11-19
Artificial Intelligence: A Multidisciplinary Approach towards Teaching and Learning explores the evolving role of AI in education, covering applications in fields such as bioinformatics, environmental science, physics, chemistry, economics, and language learning. Written by experts, this book provides a comprehensive overview of AI's integration into diverse subjects, offering insights into the future of AI in education and its potential to enhance academic research and pedagogy. Targeted at faculty, students, and professionals, the book addresses AI's role in blended learning environments and offers practical tools for educators seeking to incorporate AI into their teaching practices. Key Features: - Multidisciplinary exploration of AI in teaching and learning. - Practical tools and methodologies for educators. - Insights into AI-driven innovations in research. - Relevant to a broad audience, from students to professionals.
New Approach for Drug Repurposing Part A
New approach for drug repurposing represents drug discovery and development which is a tedious process that requires 10-15 years of time, investments up to $1-2 billion, and have high risk of failure to enter into market for clinical applications. As the drugs has to pass through number of developmental phase, the likelihood for a drug to be approved from phase I clinical trial to United States of Food and Drug Administration (USFDA) approval is less than 10%. More than 90% of drugs failed in due to toxicity, efficacy and clinical trials. Drug repurposing is one of the roadway to accelerating drug discovery and development for treating disease and thus to providing better quality of life. This volume covers an overview of drug repurposing, novel methods, mechanism of action, lab on chip for drug repurposing, computational biology, system biology, artificial intelligence and machine learning for drug repurposing, target identification, target mining, high throughput drug screening, clinical trial of repurposed drug, repurposed biologics, and regulatory consideration and intellectual property right of repurposed drug. This volume highlights a number of aspects of the drug repurposing that can help the basic understanding of students, researchers, clinicians, entrepreneurs, and stakeholders to perform their research with great interest. - To offer drug repurposing, novel methods, mechanism of action, lab on chip for drug repurposing, - To offer computational biology, system biology, artificial intelligence and machine learning for drug repurposing, - To offer high throughput drug screening, clinical trial of repurposed drug, repurposed biologics, and regulatory consideration