| Description |
1 online resource (739 pages) |
| Contents |
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 |
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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 |
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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 |
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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 |
| Notes |
11.5.2.2 Single GPU Optimizations for DL |
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Description based on publisher supplied metadata and other sources |
| Form |
Electronic book
|
| Author |
Ramaswamy, Vijayan
|
| ISBN |
3527840745 |
|
9783527840748 |
|
3527840729 |
|
9783527840724 |
|
3527840737 |
|
9783527840731 |
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