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.

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