LEADER 10736nam 22004573 450 001 9910830522503321 005 20240123080213.0 010 $a3-527-84074-5 010 $a3-527-84072-9 010 $a3-527-84073-7 035 $a(MiAaPQ)EBC31075111 035 $a(Au-PeEL)EBL31075111 035 $a(EXLCZ)9930016796800041 100 $a20240123d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Drug Discovery $eMethods and Applications 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$dİ2024. 215 $a1 online resource (739 pages) 311 08$aPrint version: Poongavanam, Vasanthanathan Computational Drug Discovery Newark : John Wiley & Sons, Incorporated,c2024 9783527351664 327 $aCover -- 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. 327 $a2.3.4 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. 327 $a4.4 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 ??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. 327 $a7.7 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. 327 $a10.2 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. 327 $a11.5.2.2 Single GPU Optimizations for DL. 676 $a615.190015118 700 $aPoongavanam$b Vasanthanathan$01609900 701 $aRamaswamy$b Vijayan$01609901 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830522503321 996 $aComputational Drug Discovery$93937385 997 $aUNINA