Computational Drug Discovery : Methods and Applications

• Autore: Poongavanam Vasanthanathan
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (739 pages)
• Disciplina: 615.190015118
• Soggetto topico: Computational chemistry; Molecular dynamics; Quimioinformàtica; Dinàmica molecular
• Soggetto genere / forma: Llibres electrònics
• Altri autori: RamaswamyVijayan
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- About the Editors -- Part I Molecular Dynamics and Related Methods in Drug Discovery -- Chapter 1 Binding Free Energy Calculations in Drug Discovery -- 1.1 Introduction -- 1.1.1 Free Energy and Thermodynamic Cycles -- 1.2 Endpoint Methods -- 1.2.1 MM/PBSA and MM/GBSA -- 1.2.2 Linear Response Approximations -- 1.3 Alchemical Methods -- 1.3.1 Free Energy Perturbation -- 1.3.2 Thermodynamic Integration -- 1.3.3 Bennett's Acceptance Ratio -- 1.3.4 Nonequilibrium Methods -- 1.3.5 Multiple Compounds -- 1.3.6 One‐Step Perturbation Approaches -- 1.3.7 Challenges in Alchemical Free Energy Calculations -- 1.4 Pathway Methods -- 1.5 Final Thoughts -- References -- Chapter 2 Gaussian Accelerated Molecular Dynamics in Drug Discovery -- 2.1 Introduction -- 2.2 Methods -- 2.2.1 Gaussian Accelerated Molecular Dynamics -- 2.2.2 Ligand Gaussian Accelerated Molecular Dynamics -- 2.2.3 Energetic Reweighting of GaMD for Free Energy Calculations -- 2.2.4 GLOW: A Workflow Integrating Gaussian Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling -- 2.2.5 Binding Kinetics Obtained from Reweighting of GaMD Simulations -- 2.2.6 Gaussian Accelerated Molecular Dynamics Implementations and Software -- 2.3 Applications -- 2.3.1 G‐Protein‐Coupled Receptors -- 2.3.1.1 Characterizing the Binding and Unbinding of Caffeine in Human Adenosine A2A Receptor -- 2.3.1.2 Unraveling the Allosteric Modulation of Human A1 Adenosine Receptor -- 2.3.1.3 Ensemble Based Virtual Screening of Allosteric Modulators of Human A1 Adenosine Receptor -- 2.3.2 Nucleic Acids -- 2.3.2.1 Exploring the Binding of Risdiplam Splicing Drug Analog to Single‐Stranded RNA -- 2.3.2.2 Uncovering the Binding of RNA to a Musashi RNA‐Binding Protein -- 2.3.3 Human Angiotensin‐Converting Enzyme 2 Receptor. ; 2.3.4 Discovery of Novel Small‐Molecule Calcium Sensitizers for Cardiac Troponin C -- 2.3.5 Binding Kinetics Prediction from GaMD Simulations -- 2.4 Conclusions -- References -- Chapter 3 MD Simulations for Drug‐Target (Un)binding Kinetics -- 3.1 Introduction -- 3.1.1 Preface -- 3.1.2 Motivation for Predicting (Un)binding Kinetics -- 3.1.3 The Time Scale Problem of MD Simulations -- 3.2 Theory of Molecular Kinetics Calculation -- 3.2.1 Nonequilibrium Statistical Mechanics in a Nutshell -- 3.2.2 Kramers Rate Theory -- 3.2.3 Biased MD Methods -- 3.2.3.1 Temperature‐ and Barrier‐Scaling -- 3.2.3.2 Bias Potential‐Based Methods -- 3.2.3.3 Bias Force‐Based Methods -- 3.2.3.4 Knowledge‐Biased Methods -- 3.2.3.5 Coarse‐graining and Master Equation Approaches -- 3.3 Challenges and Caveats in Rate Prediction -- 3.3.1 Finding Reaction Coordinates and Pathways -- 3.3.2 Error Ranges of Estimates -- 3.3.3 A Need for Reliable Benchmarking Systems -- 3.3.4 Problems with Force Fields -- 3.4 Methods for Rate Prediction -- 3.4.1 Unbinding Rate Prediction -- 3.4.1.1 Empirical Predictions -- 3.4.1.2 Prediction of Absolute Unbinding Rates -- 3.4.2 Binding Rate Prediction -- 3.5 State‐of‐the‐Art in Understanding Kinetics -- 3.6 Conclusion -- References -- Chapter 4 Solvation Thermodynamics and its Applications in Drug Discovery -- 4.1 Introduction -- 4.1.1 Protein Folding -- 4.1.2 Protein-Ligand Interactions -- 4.2 Tools to Assess the Solvation Thermodynamics -- 4.2.1 Watermap -- 4.2.2 GIST -- 4.2.3 3D‐RISM -- 4.3 Case Studies -- 4.3.1 Watermap -- 4.3.1.1 Background and Approach -- 4.3.1.2 Results and Discussion -- 4.3.2 Grid Inhomogeneous Solvation Theory (GIST) -- 4.3.2.1 Objective and Approach -- 4.3.2.2 Results and Discussion -- 4.3.3 Three‐Dimensional Reference Interaction‐Site Model (3D‐RISM) -- 4.3.3.1 Objective and Background -- 4.3.3.2 Results and Discussion. ; 4.4 Conclusion -- References -- Chapter 5 Site‐Identification by Ligand Competitive Saturation as a Paradigm of Co‐solvent MD Methods -- 5.1 Introduction -- 5.2 SILCS: Site Identification by Ligand Competitive Saturation -- 5.3 SILCS Case Studies: Bovine Serum Albumin and Pembrolizumab -- 5.3.1 SILCS Simulations -- 5.3.2 FragMap Construction -- 5.3.3 SILCS‐MC -- 5.3.4 SILCS‐Hotspots -- 5.3.5 SILCS‐PPI -- 5.3.6 SILCS‐Biologics -- 5.4 Conclusion -- Conflict of Interest -- Acknowledgments -- References -- Part II Quantum Mechanics Application for Drug Discovery -- Chapter 6 QM/MM for Structure‐Based Drug Design: Techniques and Applications -- 6.1 Introduction -- 6.2 QM/MM Approaches -- 6.2.1 Combined Quantum Mechanical/Molecular Mechanical Energy Calculations -- 6.2.2 QM/MM Methods for the Evaluation of Non‐Covalent Inhibitor Binding -- 6.2.3 QM/MM Reaction Modeling -- 6.3 Applications of QM/MM for Covalent Drug Design and Evaluation -- 6.3.1 Covalent Tyrosine Kinase Inhibitors for Cancer Treatment -- 6.3.2 Evaluation of Antibiotic Resistance Conferred by β‐Lactamases -- 6.3.3 Covalent SARS‐CoV‐2 Inhibitors: Mechanism and Insights for Design -- 6.4 Conclusions and Outlook -- References -- Chapter 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 -- 7.1 Introduction -- 7.2 Feasibility of Routine and Fast QM‐Driven X‐Ray Refinement -- 7.3 Metrics to Measure Improvement -- 7.3.1 Ligand Strain Energy -- 7.3.2 ZDD of Difference Density -- 7.3.3 Overall Crystallographic Structure Quality Metrics: MolProbity Score and Clashscore -- 7.4 QM Region Refinement -- 7.5 ONIOM Refinement -- 7.6 XModeScore: Distinguish Protomers, Tautomers, Flip States, and Docked Ligand Poses. ; 7.7 Impact of the QM‐Driven Refinement on Protein-Ligand Affinity Prediction -- 7.7.1 Impact of Structure Inspection and Modification -- 7.7.2 Impact of Selecting Protomer States: Implications of XModeScore on SBDD -- 7.8 Conclusion -- Acknowledgments -- References -- Chapter 8 Quantum‐Chemical Analyses of Interactions for Biochemical Applications -- 8.1 Introduction -- 8.2 Introduction to FMO -- 8.3 Pair Energy Decomposition Analysis (PIEDA) -- 8.3.1 Formulation of PIEDA -- 8.3.2 Applications of PIEs and PIEDA -- 8.3.3 Example of PIEDA -- 8.4 Partition Analysis (PA) -- 8.4.1 Formulation of PA -- 8.4.2 Applications and an Example of PA -- 8.5 Partition Analysis of Vibrational Energy (PAVE) -- 8.5.1 Formulation of PAVE -- 8.5.2 Applications of PAVE -- 8.6 Subsystem Analysis (SA) -- 8.6.1 Formulation of SA -- 8.6.2 Examples of SA and PAVE -- 8.7 Fluctuation Analysis (FA) -- 8.8 Free Energy Decomposition Analysis (FEDA) -- 8.9 Other Analyses of Chemical Reactions -- 8.10 Conclusions -- References -- Part III Artificial Intelligence in Pre‐clinical Drug Discovery -- Chapter 9 The Role of Computer‐Aided Drug Design in Drug Discovery -- 9.1 Introduction to Drug-Target Interactions, Hit Identification -- 9.2 Lead Identification and Optimization: QSAR and Docking‐Based Approaches -- 9.3 DTI Machine Learning Methods -- 9.4 Supervised, Non‐supervised and Semi‐supervised Learning Methods -- 9.5 Graph‐Based Methods to Label Data for DTI Prediction -- 9.6 The Importance of Explainable ML Methods: Linking Molecular Properties to Effects -- 9.7 Predicting Therapeutic Responses -- 9.8 ADMET‐tox Prediction -- 9.9 Challenging Aspects of Using Computational Methods in Drug Discovery -- 9.9.1 What are Those Limitations? -- References -- Chapter 10 AI‐Based Protein Structure Predictions and Their Implications in Drug Discovery -- 10.1 Introduction. ; 10.2 Impact of AI‐Based Protein Models in Structural Biology -- 10.2.1 Combination of AI‐Based Predictions with Cryo‐EM and X‐Ray Crystallography -- 10.2.2 Combination of AI‐Based Predictions with NMR Structures -- 10.2.3 Combination of AI‐Based Predictions with Other Experimental Restraints -- 10.2.4 Impact of Deep Learning Models in Other Areas of Structural Biology -- 10.3 Combination of AI‐Based Methods with Computational Approaches -- 10.3.1 Combination of Structure Prediction with Other Computational Approaches -- 10.4 Current Challenges and Opportunities -- 10.5 Conclusions -- References -- Chapter 11 Deep Learning for the Structure‐Based Binding Free Energy Prediction of Small Molecule Ligands -- 11.1 Introduction -- 11.2 Deep Learning Models for Reasoning About Protein-Ligand Complexes -- 11.2.1 Datasets -- 11.2.2 Convolutional Neural Networks -- 11.2.2.1 Background -- 11.2.2.2 Voxelized Grid Representation -- 11.2.2.3 Descriptors -- 11.2.2.4 Applications -- 11.2.3 Graph Neural Networks -- 11.2.3.1 Background -- 11.2.3.2 Graph Representation -- 11.2.3.3 Descriptors -- 11.2.3.4 Applications -- 11.2.3.5 Extension to Attention Based Models -- 11.2.3.6 Geometric Deep Learning and Other Approaches -- 11.3 Deep Learning Approaches Around Molecular Dynamics Simulations -- 11.3.1 Enhanced Sampling -- 11.3.2 Physics‐inspired Neural Networks -- 11.3.3 Modeling Dynamics -- 11.3.3.1 Applications -- 11.4 Modifying AlphaFold2 for Binding Affinity Prediction -- 11.4.1 Modifying AlphaFold2 Input Protein Database for Accurate Free Energy Predictions -- 11.4.2 Modifying Multiple Sequence Alignment for AlphaFold2‐Based Docking -- 11.5 Conclusion -- 11.5.1 New Models for Binding Affinity Prediction -- 11.5.2 Retrospective from the Compute Industry -- 11.5.2.1 Future DL‐Based Binding Affinity Computation will Require Massive Scalability. ; 11.5.2.2 Single GPU Optimizations for DL.
• Sommario/riassunto: The book 'Computational Drug Discovery Methods and Applications' edited by Poongavanam and Vijayan Ramaswamy, provides a comprehensive overview of cutting-edge computational techniques in drug discovery. It covers a wide range of topics, including molecular dynamics, quantum mechanics, artificial intelligence, and more. The book is structured into thematic sections focusing on methods such as binding free energy calculations, solvation models, AI-based protein structure predictions, and virtual screening techniques. It aims to equip medicinal and computational chemists, as well as drug discovery professionals, with advanced knowledge and practical insights into the integration of computational tools in drug discovery processes. With contributions from experts in academia and industry, this volume serves as a valuable resource for understanding both theoretical and practical aspects of computational drug design.
• Titolo autorizzato: Computational Drug Discovery
• ISBN: 9783527840748; 3527840745; 9783527840724; 3527840729; 9783527840731; 3527840737
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910877239103321
• Lo trovi qui: Univ. Federico II