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<p>Volume 1</p> <p>Preface xv</p> <p>Acknowledgments xix</p> <p>About the Editors xxi</p> <p><b>Part I Molecular Dynamics and Related Methods in Drug Discovery 1</b></p> <p>1 Binding Free Energy Calculations in Drug Discovery 3<br /><i>Anitade Ruiter and Chris Oostenbrink</i></p> <p>2 Gaussian Accelerated Molecular Dynamics in Drug Discovery 21<br /><i>Hung N. Do, Jinan Wang, Keya Joshi, Kushal Koirala, and Yinglong Miao</i></p> <p>3 MD Simulations for Drug-Target(Un)binding Kinetics 45<br /><i>Steffen Wolf</i></p> <p>4 Solvation Thermodynamics and its Applications in Drug Discovery 65<br /><i>Kuzhanthaivelan Saravanan and Ramesh K. Sistla</i></p> <p>5 Site-Identification by Ligand Competitive Saturation as a Paradigm of Co-solvent MD Methods 83<br /><i>Asuka A. Orr and Alexander D. MacKerell Jr.</i></p> <p><b>Part II Quantum Mechanics Application for Drug Discovery 119</b></p> <p>6 QM/MM for Structure-Based Drug Design: Techniques and Applications 121<br /><i>Marc W. van der Kamp and Jaida Begum</i></p> <p>7 Recent Advances in Practical Quantum Mechanics and Mixed-QM/MM-Driven X-Ray Crystallography and Cryogenic Electron Microscopy (Cryo-EM) and Their Impact on Structure-Based Drug Discovery 157<br /><i>Oleg Borbulevych and Lance M. Westerhoff</i></p> <p>8 Quantum-Chemical Analyses of Interactions for Biochemical Applications 183<br /><i>Dmitri G. Fedorov</i></p> <p><b>Part III Artificial Intelligence in Pre-clinical Drug Discovery 211</b></p> <p>9 The Role of Computer-Aided Drug Design in Drug Discovery 213<br /><i>Stormvander Voort, Andreas Bender, and Bart A. Westerman</i></p> <p>10 AI-Based Protein Structure Predictions and Their Implications in Drug Discovery 227<br /><i>Tahsin F. Kellici, Dimitar Hristozov, and Inaki Morao</i></p> <p>11 Deep Learning for the Structure-Based Binding Free Energy Prediction of Small Molecule Ligands 255<br /><i>Venkatesh Mysore, Nilkanth Patel, and Adegoke Ojewole</i></p> <p>12 Using Artificial Intelligence for de novo Drug Design and Retrosynthesis 275<br /><i>Rohit Arora, Nicolas Brosse, Clarisse Descamps, Nicolas Devaux, Nicolas Do Huu, Philippe Gendreau, Yann Gaston-Mathé, Maud Parrot, Quentin Perron, and Hamza Tajmouati</i></p> <p>13 Reliability and Applicability Assessment for Machine Learning Models 299<br /><i>Fabio Urbina and Sean Ekins</i></p> <p><b>Volume 2</b></p> <p>Preface xv</p> <p>Acknowledgments xix</p> <p>About the Editors xxi</p> <p><b>Part IV Chemical Space and Knowledge-Based Drug Discovery 315</b></p> <p>14 Enumerable Libraries and Accessible Chemical Space in Drug Discovery 317<br /><i>Tim Knehans, Nicholas A. Boyles, and Pieter H. Bos</i></p> <p>15 Navigating Chemical Space 337<br /><i>Akos Tarcsay, András Volford, Jonathan Buttrick, Jan-Constantin Christopherson, Máte Erdos, and Zoltán B. Szabó</i></p> <p>16 Visualization, Exploration, and Screening of Chemical Space in Drug Discovery 365<br /><i>José J. Naveja, Fernanda I. Saldívar-González, Diana L. Prado-Romero, Angel J.Ruiz-Moreno, Marco Velasco-Velázquez, Ramón Alain Miranda-Quintana, and José L. Medina-Franco</i></p> <p>17 SAR Knowledge Bases for Driving Drug Discovery 395<br /><i>Nishanth Kandepedu, Anil Kumar Manchala, and Norman Azoulay</i></p> <p>18 Cambridge Structural Database (CSD)–Drug Discovery Through Data Mining & Knowledge-Based Tools 419<br /><i>Francesca Stanzione, Rupesh Chikhale, and Laura Friggeri</i></p> <p><b>Part V Structure-Based Virtual Screening Using Docking 441</b></p> <p>19 Structure-Based Ultra-Large Virtual Screenings 443<br /><i>Christoph Gorgulla</i></p> <p>20 Community Benchmarking Exercises for Docking and Scoring 471<br /><i>Bharti Devi, Anurag TK Baidya, and Rajnish Kumar</i></p> <p><b>PartVI In Silico ADMET Modeling 495</b></p> <p>21 Advances in the Application of In Silico ADMET Models–An Industry Perspective 497<br /><i>Wenyi Wang, Fjodor Melnikov, Joe Napoli, and Prashant Desai</i></p> <p><b>Part VII Computational Approaches for New Therapeutic Modalities 537</b></p> <p>22 Modeling the Structures of Ternary Complexes Mediated by Molecular Glues 539<br /><i>Michael L. Drummond</i></p> <p>23 Free Energy Calculations in Covalent Drug Design 561<br /><i>Levente M. Mihalovits, György G. Ferenczy, and György M. Keseru</i></p> <p><b>Part VIII Computing Technologies Driving Drug Discovery 579</b></p> <p>24 Orion A Cloud-Native Molecular Design Platform 581<br /><i>Jesper Sorensen, Caitlin C. Bannan, Gaetano Calabrò, Varsha Jain, Grigory Ovanesyan, Addison Smith, She Zhang, Christopher I. Bayly, Tom A. Darden, Matthew T. Geballe, David N. LeBard, Mark McGann, Joseph B. Moon, Hari S. Muddana, Andrew Shewmaker, Jharrod LaFon, Robert W. Tolbert, A. Geoffrey Skillman, and Anthony Nicholls</i></p> <p>25 Cloud-Native Rendering Platform and GPUs Aid Drug Discovery 617<br /><i>Mark Ross, Michael Drummond, Lance Westerhoff, Xavier Barbeu, Essam Metwally, Sasha Banks-Louie, Kevin Jorissen, Anup Ojah, and Ruzhu Chen</i></p> <p>26 The Quantum Computing Paradigm 627<br /><i>Thomas Ehmer, Gopal Karemore, and Hans Melo</i></p> <p>Index 679</p>

<p><b>Vasanthanathan Poongavanam</b> is a senior scientist in the Department of Chemistry-BMC, Uppsala University, Sweden. Before starting at Uppsala University 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. degree in Computational Medicinal Chemistry as a Drug Research Academy (DRA) Fellow at the University of Copenhagen, Denmark, on computational modeling of cytochrome P450. He has published more than 65 scientific articles, including reviews and book chapters. His scientific interests focus on in silico ADMET modeling including cell permeability and solubility, and he has worked extensively on understanding the molecular properties that govern the pharmacokinetic profile of molecules bRo5 property space, including macrocycles and PROTACs.</p> <p><b>Vijayan Ramaswamy</b> (R.S.K. Vijayan) is a senior research scientist affiliated with the Structural Chemistry division at the Institute for Applied Cancer Science, University of Texas MD Anderson Cancer, TX, USA. In 2016, he joined MD Anderson Cancer after a brief tenure as a scientist, at PMC Advanced Technologies, New Jersey, USA. He undertook postdoctoral training at Rutgers University in New Jersey, USA, and Temple University in Pennsylvania, USA. He received his Ph.D. in Pharmacy as a CSIR senior research fellow from the Indian Institute of Chemical Biology, Kolkata, India. He is a named co-inventor on 7 issued US patents, including an ATR kinase inhibitor that has advanced to Phase 2 clinical trials. He has published more than 20 scientific articles and authored one book chapter. His research focuses on applying computational chemistry methods to drive small molecule drug discovery programs, particularly for oncology and neurodegenerative diseases.</p>

<p><b>A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery</b> <p><i>Computational Drug Discovery: </i> 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. <p>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. <p>To offer the most up-to-date information on computational methods utilized in <i>computational drug discovery, </i> 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. <p>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 <i>computational drug discovery</i>. <p>Key topics covered in the book include: <ul><li>Application of molecular dynamics simulations and related approaches in drug discovery</li> <li>The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions</li> <li>Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for <i>de novo</i> design, and virtual screening. </li> <li>Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. </li> <li>Methods for performing ultra-large-scale virtual screening for hit identification. </li> <li>Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. </li> <li>In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. </li> <li>The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery</li></ul> <p>This book will provide readers an overview of the latest advancements in <i>computational drug discovery</i> and serve as a valuable resource for professionals engaged in drug discovery.

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