Computational Drug Discovery
Methods and Applications
1. Auflage Februar 2024
736 Seiten, Hardcover
27 Tabellen
Handbuch/Nachschlagewerk
ISBN:
978-3-527-35166-4
Wiley-VCH, Weinheim
Kurzbeschreibung
Provide readers with an overview of modern technologies, emphasizing AI for drug discovery.
Weitere Versionen
Preface
Volume 1:
PART I. MOLECULAR DYNAMICS AND RELATED METHODS 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 Competitive Saturation as a Paradigm of Co-Solvent Methods
PART II. QUANTUM MECHANICS APPLICATION FOR DRUG DISCOVERY
QM/MM Approaches for Structure Based Drug Design: Techniques and Applications
Recent 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 Discovery
Quantum-Mechanical Analyses of Interactions for Biochemical Applications
PART III. ARTIFICIAL INTELLIGENCE IN PRE-CLINICAL DRUG DISCOVERY
The Role of Computer Aided Drug Design in Drug Discovery - An Introduction
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 the De Novo Drug Design and Retrosynthesis
Reliability and Applicability Assessment for Machine Learning Models
Volume 2:
PART IV. CHEMICAL SPACE AND KNOWLEDGE BASED DRUG DISCOVERY
Enumerable Libraries and Accessible Chemical Space
Navigating Chemical Space
Visualization, Exploration, and Screening of Chemical Space in Drug Discovery
SAR Knowledge Based for Driving Drug Discovery
Cambridge Structural Database (CSD) - Drug Discovery through Data Mining and Knowledge Based Tools
PART V. STRUCTURE-BASED VIRTUAL SCREENING USING DOCKING
Structure-Based Ultra-Large Scale Virtual Screenings
Community Benchmarking Exercises for Docking and Scoring
PART VI. IN SILICO ADMET MODELLING
Advances in the Application of In Silico ADMET Models - An Industry Perspective
PART VII. COMPUTATIONAL APPROACHES FOR NEW THERAPEUTIC MODALITIES
Modelling the Structures of Ternary Complexes Mediated by Molecular Glues
Free Energy Calculations in Covalent Drug Design
PART VIII. COMPUTING TECHNOLOGIES DRIVING DRUG DISCOVERY
Orion® A Cloud-Native Molecular Design Platform
Cloud-Native Rendering Platform and GPUs Aid Drug Discovery
The Quantum Computing Paradigm
Volume 1:
PART I. MOLECULAR DYNAMICS AND RELATED METHODS 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 Competitive Saturation as a Paradigm of Co-Solvent Methods
PART II. QUANTUM MECHANICS APPLICATION FOR DRUG DISCOVERY
QM/MM Approaches for Structure Based Drug Design: Techniques and Applications
Recent 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 Discovery
Quantum-Mechanical Analyses of Interactions for Biochemical Applications
PART III. ARTIFICIAL INTELLIGENCE IN PRE-CLINICAL DRUG DISCOVERY
The Role of Computer Aided Drug Design in Drug Discovery - An Introduction
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 the De Novo Drug Design and Retrosynthesis
Reliability and Applicability Assessment for Machine Learning Models
Volume 2:
PART IV. CHEMICAL SPACE AND KNOWLEDGE BASED DRUG DISCOVERY
Enumerable Libraries and Accessible Chemical Space
Navigating Chemical Space
Visualization, Exploration, and Screening of Chemical Space in Drug Discovery
SAR Knowledge Based for Driving Drug Discovery
Cambridge Structural Database (CSD) - Drug Discovery through Data Mining and Knowledge Based Tools
PART V. STRUCTURE-BASED VIRTUAL SCREENING USING DOCKING
Structure-Based Ultra-Large Scale Virtual Screenings
Community Benchmarking Exercises for Docking and Scoring
PART VI. IN SILICO ADMET MODELLING
Advances in the Application of In Silico ADMET Models - An Industry Perspective
PART VII. COMPUTATIONAL APPROACHES FOR NEW THERAPEUTIC MODALITIES
Modelling the Structures of Ternary Complexes Mediated by Molecular Glues
Free Energy Calculations in Covalent Drug Design
PART VIII. COMPUTING TECHNOLOGIES DRIVING DRUG DISCOVERY
Orion® A Cloud-Native Molecular Design Platform
Cloud-Native Rendering Platform and GPUs Aid Drug Discovery
The Quantum Computing Paradigm
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 as
a 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.
a 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.
