Computational Drug Discovery, 2 Volumes: Methods and Applications

ISBN-13: 9783527351664 / Angielski / Twarda / 2024 / 704 str.

V Poongavanam;Vasanthanathan Poongavanam;Vijayan Ramaswamy

Opis

Comprehensive resource explaining efficient and cost-effective computational technologies for drug optimizations in order to enable innovative drug exploration and design Computational Drug Discovery: Methods and Applications (2V set) covers a wide range of cutting-edge computational technology or computational chemistry method that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) applications in protein structure prediction, AI-enabled virtual screening, and generative modeling. Additionally, it covers key technological advancements in computing impacting drug discovery, such as quantum computing, and cloud computing. 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 targeting 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 utilizing 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 relevant 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 the utilization of 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.

Spis treści

PrefaceVolume 1:PART I. MOLECULAR DYNAMICS AND RELATED METHODS IN DRUG DISCOVERYBinding Free Energy Calculations in Drug DiscoveryGaussian Accelerated Molecular Dynamics in Drug DiscoveryMD Simulations for Drug-Target (Un)Binding KineticsSolvation Thermodynamics and its Competitive Saturation as a Paradigm of Co-Solvent MethodsPART II. QUANTUM MECHANICS APPLICATION FOR DRUG DISCOVERYQM/MM Approaches for Structure Based Drug Design: Techniques and ApplicationsRecent Advances in Practical Quantum Mechanics and Mixed-QM/MM Driven X-Ray Crystallography and Cryo-Electron Microscopy (Cryo-EM) and their Impact on Structure-Based Drug DiscoveryQuantum-Mechanical Analyses of Interactions for Biochemical ApplicationsPART III. ARTIFICIAL INTELLIGENCE IN PRE-CLINICAL DRUG DISCOVERYThe Role of Computer Aided Drug Design in Drug Discovery - An IntroductionAI-Based Protein Structure Predictions and their Implications in Drug DiscoveryDeep Learning for the Structure-Based Binding Free Energy Prediction of Small Molecule LigandsUsing Artificial Intelligence for the De Novo Drug Design and RetrosynthesisReliability and Applicability Assessment for Machine Learning ModelsVolume 2:PART IV. CHEMICAL SPACE AND KNOWLEDGE BASED DRUG DISCOVERYEnumerable Libraries and Accessible Chemical SpaceNavigating Chemical SpaceVisualization, Exploration, and Screening of Chemical Space in Drug DiscoverySAR Knowledge Based for Driving Drug DiscoveryCambridge Structural Database (CSD) - Drug Discovery through Data Mining and Knowledge Based ToolsPART V. STRUCTURE-BASED VIRTUAL SCREENING USING DOCKINGStructure-Based Ultra-Large Scale Virtual ScreeningsCommunity Benchmarking Exercises for Docking and ScoringPART VI. IN SILICO ADMET MODELLINGAdvances in the Application of In Silico ADMET Models - An Industry PerspectivePART VII. COMPUTATIONAL APPROACHES FOR NEW THERAPEUTIC MODALITIESModelling the Structures of Ternary Complexes Mediated by Molecular GluesFree Energy Calculations in Covalent Drug DesignPART VIII. COMPUTING TECHNOLOGIES DRIVING DRUG DISCOVERYOrion® A Cloud-Native Molecular Design PlatformCloud-Native Rendering Platform and GPUs Aid Drug DiscoveryThe Quantum Computing Paradigm

O autorze

Vasanthanathan Poongavanam is a senior researcher in the Department of Chemistry-BMC, Uppsala University, Sweden. Before starting at Uppsala University in a researcher position with Jan Kihlberg in 2016, he was a postdoctoral fellow at the University of Vienna, Austria, and at the University of Southern Denmark. He obtained his Ph.D. in medicinal chemistry asa Drug Research Academy Fellow at the University of Copenhagen, Denmark, on computational modeling of Cytochrome P450. His research interests focus on understanding the molecular properties that govern the pharmacokinetic profile of molecules beyond the Ro5 space, including macrocycles and PROTACs.Vijayan Ramaswamy is a research scientist with the Structural Chemistry group at the Institute for Applied Cancer Science, University of Texas MD Anderson Cancer, TX, USA. Before starting at the MD Anderson Cancer in 2016, he was a postdoctoral fellow at Rutgers University, NJ, USA, and at Temple University, PA, USA. He received his Ph.D as a CSIR senior research fellow from the Indian Institute of Chemical Biology, Kolkata, India. His research focuses on applying computational chemistry methods to drive small-molecule drug discovery programs, particularly in oncology and neurodegenerative diseases.