Poongavanam V., Ramaswamy V. (ed.) Computational Drug Discovery, 2 Volumes: Methods and Applications

Wiley-VCH GmbH, 2024. — 730 p. — ISBN: 978-3-527-35374-3, 978-3-527-35375-0.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 address the recent trends in the field of computer-aided drug design, including ultra-large-scale virtual screening for hit identification, and 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, the 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 with an overview of the latest advancements in Computational Drug Discovery and serve as a valuable resource for professionals engaged in drug discovery.Binding Free Energy Calculations in Drug Discovery.
Gaussian Accelerated Molecular Dynamics in Drug Discovery.
MD Simulations for Drug-Target (Un)binding Kinetics.
Solvation Thermodynamics and its Applications in Drug Discovery.
Site-Identification by Ligand Competitive Saturation as a Paradigm of Co-solvent MD Methods.
QM/MM for Structure-Based Drug Design: Techniques and Applications.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.Quantum-Chemical Analyzes of Interactions for Biochemical Applications.
The Role of Computer-Aided Drug Design in Drug Discovery.
AI-Based Protein Structure Predictions and Their Implications in Drug Discovery.
Deep Learning for the Structure-Based Binding Free Energy Prediction of Small Molecule Ligands.
Using Artificial Intelligence for de novo Drug Design and Retrosynthesis.
Reliability and Applicability Assessment for Machine Learning Models.
Enumerable Libraries and Accessible Chemical Space in Drug Discovery.
Navigating Chemical Space.
Visualization, Exploration, and Screening of Chemical Space in Drug Discovery.
SAR Knowledge Bases for Driving Drug Discovery.
Cambridge Structural Database (CSD) – Drug Discovery Through Data Mining & Knowledge-Based Tools.
Structure-Based Ultra-Large Virtual Screenings.
Community Benchmarking Exercises for Docking and Scoring.
Advances in the Application of In Silico ADMET Models – An Industry Perspective.
Modeling the Structures of Ternary Complexes Mediated by Molecular Glues.
Free Energy Calculations in Covalent Drug Design.
Orion A Cloud-Native Molecular Design Platform.
Cloud-Native Rendering Platform and GPUs Aid Drug Discovery.
The Quantum Computing Paradigm.