Download or read book Molecular Dynamics and Machine Learning in Drug Discovery written by Sergio Decherchi and published by Frontiers Media SA. This book was released on 2021-06-08 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr. Sergio Decherchi and Dr. Andrea Cavalli are co-founders of BiKi Technologies s.r.l. - a company that commercializes a Molecular Dynamics-based software suite for drug discovery. All other Topic Editors declare no competing interests with regards to the Research Topic subject.

Download or read book Artificial Intelligence in Drug Discovery written by Nathan Brown and published by Royal Society of Chemistry. This book was released on 2020-11-04 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.

Download or read book Molecular Modeling in Drug Design written by Outi Salo-Ahen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the first attempts at structure-based drug design about four decades ago, molecular modelling techniques for drug design have developed enormously, along with the increasing computational power and structural and biological information of active compounds and potential target molecules. Nowadays, molecular modeling can be considered to be an integral component of the modern drug discovery and development toolbox. Nevertheless, there are still many methodological challenges to be overcome in the application of molecular modeling approaches to drug discovery. The eight original research and five review articles collected in this book provide a snapshot of the state-of-the-art of molecular modeling in drug design, illustrating recent advances and critically discussing important challenges. The topics covered include virtual screening and pharmacophore modelling, chemoinformatic applications of artificial intelligence and machine learning, molecular dynamics simulation and enhanced sampling to investigate contributions of molecular flexibility to drug-receptor interactions, the modeling of drug-receptor solvation, hydrogen bonding and polarization, and drug design against protein-protein interfaces and membrane protein receptors.

Download or read book Molecular Modeling in Drug Design written by Rebecca Wade and published by MDPI. This book was released on 2019-03-26 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the first attempts at structure-based drug design about four decades ago, molecular modelling techniques for drug design have developed enormously, along with the increasing computational power and structural and biological information of active compounds and potential target molecules. Nowadays, molecular modeling can be considered to be an integral component of the modern drug discovery and development toolbox. Nevertheless, there are still many methodological challenges to be overcome in the application of molecular modeling approaches to drug discovery. The eight original research and five review articles collected in this book provide a snapshot of the state-of-the-art of molecular modeling in drug design, illustrating recent advances and critically discussing important challenges. The topics covered include virtual screening and pharmacophore modelling, chemoinformatic applications of artificial intelligence and machine learning, molecular dynamics simulation and enhanced sampling to investigate contributions of molecular flexibility to drug–receptor interactions, the modeling of drug–receptor solvation, hydrogen bonding and polarization, and drug design against protein–protein interfaces and membrane protein receptors.

Download or read book Computational Drug Discovery and Design written by Mohini Gore and published by Springer Nature. This book was released on 2023-10-09 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition provides new and updated methods and techniques for identification of drug target, binding sites prediction, high- throughput virtual screening, lead discovery and optimization, conformational sampling, prediction of pharmacokinetic properties using computer-based methodologies. Chapters also focus on the application of the latest artificial intelligence technologies for computer aided drug discovery. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Computational Drug Discovery and Design, Second Edition aims to effectively utilize computational methodologies in discovery and design of novel drugs.

Download or read book Cheminformatics QSAR and Machine Learning Applications for Novel Drug Development written by Kunal Roy and published by Elsevier. This book was released on 2023-05-23 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Converging Pharmacy Science and Engineering in Computational Drug Discovery written by Tripathi, Rati Kailash Prasad and published by IGI Global. This book was released on 2024-04-22 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.

Download or read book Advanced AI Techniques and Applications in Bioinformatics written by Loveleen Gaur and published by CRC Press. This book was released on 2021-10-17 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: The advanced AI techniques are essential for resolving various problematic aspects emerging in the field of bioinformatics. This book covers the recent approaches in artificial intelligence and machine learning methods and their applications in Genome and Gene editing, cancer drug discovery classification, and the protein folding algorithms among others. Deep learning, which is widely used in image processing, is also applicable in bioinformatics as one of the most popular artificial intelligence approaches. The wide range of applications discussed in this book are an indispensable resource for computer scientists, engineers, biologists, mathematicians, physicians, and medical informaticists. Features: Focusses on the cross-disciplinary relation between computer science and biology and the role of machine learning methods in resolving complex problems in bioinformatics Provides a comprehensive and balanced blend of topics and applications using various advanced algorithms Presents cutting-edge research methodologies in the area of AI methods when applied to bioinformatics and innovative solutions Discusses the AI/ML techniques, their use, and their potential for use in common and future bioinformatics applications Includes recent achievements in AI and bioinformatics contributed by a global team of researchers

Download or read book Computational Drug Discovery written by Vasanthanathan Poongavanam and published by John Wiley & Sons. This book was released on 2024-01-19 with total page 882 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Drug Discovery A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery. Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented. To offer the most up-to-date information on computational methods utilized in computational drug discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts. The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery. Key topics covered in the book include: Application of molecular dynamics simulations and related approaches in drug discovery The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening. Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. Methods for performing ultra-large-scale virtual screening for hit identification. Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery This book will provide readers an overview of the latest advancements in computational drug discovery and serve as a valuable resource for professionals engaged in drug discovery.

Download or read book Biophysical and Computational Tools in Drug Discovery written by Anil Kumar Saxena and published by Springer Nature. This book was released on 2021-10-18 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews recent physicochemical and biophysical techniques applied in drug discovery research, and it outlines the latest advances in computational drug design. Divided into 10 chapters, the book discusses about the role of structural biology in drug discovery, and offers useful application cases of several biophysical and computational methods, including time-resolved fluorometry (TRF) with Förster resonance energy transfer (FRET), X-Ray crystallography, nuclear magnetic resonance spectroscopy, mass spectroscopy, generative machine learning for inverse molecular design, quantum mechanics/molecular mechanics (QM/MM,ONIOM) and quantum molecular dynamics (QMT) methods. Particular attention is given to computational search techniques applied to peptide vaccines using novel mathematical descriptors and structure and ligand-based virtual screening techniques in drug discovery research. Given its scope, the book is a valuable resource for students, researchers and professionals from pharmaceutical industry interested in drug design and discovery.

Download or read book Protein Allostery in Drug Discovery written by Jian Zhang and published by Springer Nature. This book was released on 2019-11-09 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on protein allostery in drug discovery. Allosteric regulation, ʹthe second secret of lifeʹ, fine-tunes virtually most biological processes and controls physiological activities. Allostery can both cause human diseases and contribute to development of new therapeutics. Allosteric drugs exhibit unparalleled advantages compared to conventional orthosteric drugs, rendering the development of allosteric modulators as an appealing strategy to improve selectivity and pharmacodynamic properties in drug leads. The Series delineates the immense significance of protein allostery—as demonstrated by recent advances in the repertoires of the concept, its mechanistic mechanisms, and networks, characteristics of allosteric proteins, modulators, and sites, development of computational and experimental methods to predict allosteric sites, small-molecule allosteric modulators of protein kinases and G-protein coupled receptors, engineering allostery, and the underlying role of allostery in precise medicine. Comprehensive understanding of protein allostery is expected to guide the rational design of allosteric drugs for the treatment of human diseases. The book would be useful for scientists and students in the field of protein science and Pharmacology etc.

Download or read book CADD and Informatics in Drug Discovery written by Mithun Rudrapal and published by Springer Nature. This book was released on 2023-05-12 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book updates knowledge on recent advances in computational, biophysical and bioinformatics tools/techniques and their practical applications in modern drug design and discovery paradigm. It also encompasses fundamental principles, advanced methodologies and applications of various CADD approaches including several cutting-edge areas; presenting recent developments covering ongoing trends in the field of computer-aided drug discovery. Having contributions by a global team of experts, the book is expected to be an ideal resource for drug discovery scientists, medicinal chemists, pharmacologists, toxicologists, phytochemists, biochemists, biologists, R&D personnel, researchers, students, teachers and those working in the field of drug discovery. It will fill the knowledge gaps that exist in the current CADD approaches and methodologies/ protocols being widely used in both academic and research practices. Further, a special focus on current status of various computational drug design approaches (SBDD, LBDD, de novo drug design, pharmacophore-based search), bioinformatics tools and databases, computational screening and modeling of phytochemicals/natural products, artificial intelligence and machine learning, and network pharmacology and systems biology would certainly guide researchers, students or readers to conduct their research in the emerging area(s) of interest. It is also expected to be highly beneficial to various stakeholders working in the pharmaceutical and biotechnology industries (R&D), the academic as well as research sectors.

Download or read book Artificial Intelligence Platform For Molecular Targeted Therapy A Translational Science Approach written by Ariel Fernandez and published by World Scientific. This book was released on 2021-03-12 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the era of big biomedical data, there are many ways in which artificial intelligence (AI) is likely to broaden the technological base of the pharmaceutical industry. Cheminformatic applications of AI involving the parsing of chemical space are already being implemented to infer compound properties and activity. By contrast, dynamic aspects of the design of drug/target interfaces have received little attention due to the inherent difficulties in dealing with physical phenomena that often do not conform to simplifying views.This book focuses precisely on dynamic drug/target interfaces and argues that the true game change in pharmaceutical discovery will come as AI is enabled to solve core problems in molecular biophysics that are intimately related to rational drug design and drug discovery.Here are a few examples to convey the flavor of our quest: How do we therapeutically impair a dysfunctional protein with unknown structure or regulation but known to be a culprit of disease? In regards to SARS-CoV-2, what is the structural impact of a dominant mutation?, how does the structure change translate into a fitness advantage?, what new therapeutic opportunity arises? How do we extend molecular dynamics simulations to realistic timescales, to capture the rare events associated with drug targeting in vivo? How do we control specificity in drug design to selectively remove side effects? This is the type of problems, directly related to the understanding of drug/target interfaces, that the book squarely addresses by leveraging a comprehensive AI-empowered approach.

Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.

Download or read book Computational Approaches in Drug Discovery Development and Systems Pharmacology written by Rupesh Kumar Gautam and published by Elsevier. This book was released on 2023-02-15 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Approaches in Drug Discovery, Development and Systems Pharmacology provides detailed information on the use of computers in advancing pharmacology. Drug discovery and development is an expensive and time-consuming practice, and computer-assisted drug design (CADD) approaches are increasing in popularity in the pharmaceutical industry to accelerate the process. With the help of CADD, scientists can focus on the most capable compounds so that they can minimize the synthetic and biological testing pains. This book examines success stories of CADD in drug discovery, drug development and role of CADD in system pharmacology, additionally including a focus on the role of artificial intelligence (AI) and deep machine learning in pharmacology. Computational Approaches in Drug Discovery, Development and Systems Pharmacology will be useful to researchers and academics working in the area of CADD, pharmacology and Bioinformatics. Explains computer use in pharmacology using real-life case studies Provides information about biological activities using computer technology, thus allowing for the possible reduction of the number of animals used for research Describes the role of AI in pharmacology and applications of CADD in various diseases

Download or read book Applied Computer Aided Drug Design Models and Methods written by Igor José dos Santos Nascimento and published by Bentham Science Publishers. This book was released on 2023-12-08 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume. Key Features · Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format. · Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs. · Explains theoretical fundamentals and applications of computer-aided drug design. · Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP). · Includes scientific reference for advanced readers Readership Students, teachers and early career researchers.

Download or read book Advances in Computational Toxicology written by Huixiao Hong and published by Springer. This book was released on 2019-05-21 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive review of both traditional and cutting-edge methodologies that are currently used in computational toxicology and specifically features its application in regulatory decision making. The authors from various government agencies such as FDA, NCATS and NIEHS industry, and academic institutes share their real-world experience and discuss most current practices in computational toxicology and potential applications in regulatory science. Among the topics covered are molecular modeling and molecular dynamics simulations, machine learning methods for toxicity analysis, network-based approaches for the assessment of drug toxicity and toxicogenomic analyses. Offering a valuable reference guide to computational toxicology and potential applications in regulatory science, this book will appeal to chemists, toxicologists, drug discovery and development researchers as well as to regulatory scientists, government reviewers and graduate students interested in this field.