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  • 1
    Online Resource
    Online Resource
    New York, NY : Springer US
    Subject(s): Pharmaceutical technology. ; Bioinformatics. ; Quantum computers. ; Pharmaceutical Sciences/Technology. ; Bioinformatics. ; Quantum Computing.
    In: Springer eBooks
    Description / Table of Contents: This volume looks at applications of quantum mechanical (QM) methods in drug discovery. The chapters in this book describe how QM approaches can be applied to address key drug discovery issues, such as characterizing protein-water-ligand and protein-protein interactions, providing estimates of binding affinities, determining ligand energies and bioactive conformations, refinement of molecular geometries, scoring docked protein–ligand poses, describing molecular similarity, structure–activity-relationship (SAR) analysis, and ADMET prediction. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Quantum Mechanics in Drug Discovery is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists, and drug designers.
    Type of Medium: Online Resource
    Pages: X, 360 p. 150 illus., 117 illus. in color. , online resource.
    Edition: 1st ed. 2020.
    ISBN: 9781071602829
    Series Statement: Methods in Molecular Biology, 2114
    Language: English
    Note: Current and Future Challenges in Modern Drug Discovery -- QM Implementation in Drug Design: Does It Really Help? -- Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method -- Analyzing Interactions with the Fragment Molecular Orbital Method -- Underappreciated Chemical Interactions in Protein-Ligand Complexes -- Geometry Optimization, Transition State Search, and Reaction Path Mapping Accomplished with the Fragment Molecular Orbital Method -- Taking Water into Account with the Fragment Molecular Orbital Method -- Computational Methods for Biochemical Simulations Implemented in GAMESS -- QM in Seconds with the Fragment Molecular Orbital and Density-Functional Tight-Binding Methods -- Protein Molecular Dynamics Simulations with Approximate QM: What Can We Learn? -- Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method -- Characterizing Rhodopsin-Arrestin Interactions with the Fragment Molecular Orbital (FMO) Method -- Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method -- Conformational Searching with Quantum Mechanics -- User-Friendly Quantum Mechanics: Applications for Drug Discovery -- Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization -- Molecular Docking Using Quantum Mechanical-Based Methods -- QM Calculations in ADMET Prediction -- Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations -- What’s Next for Quantum Mechanics in Structure-Based Drug Discovery?.
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  • 2
    Online Resource
    Online Resource
    New York, NY : Springer US
    Subject(s): Pharmacology. ; Artificial intelligence. ; Machine learning. ; Pharmacology/Toxicology. ; Artificial Intelligence. ; Machine Learning.
    In: Springer Nature eBook
    Description / Table of Contents: This volume looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step, readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and unique, Artificial Intelligence in Drug Design is a valuable resource for structural and molecular biologists, computational and medicinal chemists, pharmacologists and drug designers.
    Type of Medium: Online Resource
    Pages: XI, 529 p. 103 illus., 89 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9781071617878
    Series Statement: Methods in Molecular Biology, 2390
    Language: English
    Note: Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges -- Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints -- Fighting COVID-19 with Artificial Intelligence -- Application of Artificial Intelligence and Machine Learning in Drug Discovery -- Deep Learning and Computational Chemistry -- Has Drug Design Augmented by Artificial Intelligence Become a Reality? -- Network Driven Drug Discovery -- Predicting Residence Time of GPCR Ligands with Machine Learning -- De Novo Molecular Design with Chemical Language Models -- Deep Neural Networks for QSAR -- Deep Learning in Structure-Based Drug Design -- Deep Learning Applied to Ligand-Based De Novo Drug Design -- Ultra-High Throughput Protein-Ligand Docking with Deep Learning -- Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors -- Artificial Intelligence in Compound Design -- Artificial Intelligence, Machine Learning, and Deep Learning in Real Life Drug Design Cases -- Artificial Intelligence-Enabled De Novo Design of Novel Compounds that are Synthesizable -- Machine Learning from Omics Data -- Deep Learning in Therapeutic Antibody Development -- Machine Learning for In Silico ADMET Prediction -- Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction -- Artificial Intelligence in Drug Safety and Metabolism -- Molecule Ideation Using Matched Molecular Pairs.
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  • 3
    Online Resource
    Online Resource
    New York, NY : Springer US
    Subject(s): Bioinformatics. ; Pharmacology. ; Medicine Research. ; Biology Research. ; Computational and Systems Biology. ; Pharmacology. ; Biomedical Research.
    In: Springer Nature eBook
    Description / Table of Contents: This volume explores the application of high-performance computing (HPC) technologies to computational drug discovery (CDD) and biomedicine. The first section collects CDD approaches that, together with HPC, can revolutionize and automate drug discovery process, such as knowledge graphs, natural language processing (NLP), Bayesian optimization, automated virtual screening platforms, alchemical free energy workflows, fragment-molecular orbitals (FMO), HPC-adapted molecular dynamic simulation (MD-HPC), and the potential of cloud computing for drug discovery. The second section delves into computational algorithms and workflows for biomedicine, featuring an HPC framework to assess drug-induced arrhythmic risk, digital patient applications relevant to the clinic, virtual human simulations, cellular and whole-body blood flow modeling for stroke treatments, prediction of the femoral bone strength from CT data, and many more subjects. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary software and tools, step-by-step and readily reproducible modeling protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, High Performance Computing for Drug Discovery and Biomedicine allows a diverse audience, including computer scientists, computational and medicinal chemists, biologists, clinicians, pharmacologists and drug designers, to navigate the complex landscape of what is currently possible and to understand the challenges and future directions of HPC-based technologies.
    Type of Medium: Online Resource
    Pages: XIII, 429 p. , online resource.
    Edition: 1st ed. 2024.
    ISBN: 9781071634493
    Series Statement: Methods in Molecular Biology, 2716
    Language: English
    Note: Introduction to Computational Biomedicine -- Introduction to High Performance Computing -- Computational Biomedicine (CompBioMed) Centre of Excellence: Selected Key Achievements -- In Silico Clinical Trials: Is It Possible? -- Bayesian Optimization in Drug Discovery -- Automated Virtual Screening -- The Future of Drug Development with Quantum Computing -- Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery -- Knowledge Graphs and Their Applications in Drug Discovery -- Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls -- Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities -- Molecular Dynamics and Other HPC Simulations for Drug Discovery -- High Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques -- HPC Framework for Performing In Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk -- Effect of Muscle Forces on Femur during Level Walking Using a Virtual Population of Older Women -- Cellular Blood Flow Modeling with HemoCell -- A Blood Flow Modeling Framework for Stroke Treatments -- Efficient and Reliable Data Management for Biomedical Applications -- Accelerating COVID-19 Drug Discovery with High-Performance Computing -- Teaching Medical Students to Use Supercomputers: A Personal Reflection.
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  • 4
    Online Resource
    Online Resource
    New York, NY : Springer New York | New York, NY : Springer New York
    Subject(s): Medicine. ; Pharmacology ; Biomedicine ; Pharmacology/Toxicology
    In: Springer eBooks
    Type of Medium: Online Resource
    Pages: XI, 436 p. 111 illus., 102 illus. in color. , online resource.
    ISBN: 9781493974658
    Series Statement: Methods in Molecular Biology, 1705
    Language: English
    Note: Current and Future Challenges in GPCR Drug Discovery.-� Characterization of Ligand Binding to GPCRs through Computational Methods.-� Breakthrough in GPCR Crystallography and its Impact on Computer-Aided Drug Design.-� A Structural Framework for GPCR Chemogenomics: What���s in a Residue Number?.-� GPCR Homology Model Generation for Lead Optimization.-� GPCRs: What Can We Learn from Molecular Dynamics Simulations?.-� Methods of Exploring Protein-Ligand Interactions to Guide Medicinal Chemistry Efforts.-� Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method.-� Molecular Basis of Ligand Dissociation from G Protein-Coupled Receptors and Predicting Residence Time.-� Methodologies for the Examination of Water in GPCRs.-� Methods for Virtual Screening of GPCR Targets: Approaches and Challenges.-� Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists.-� Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs -- Challenges and Opportunities in Drug Discovery of Biased Ligands.-� Synergistic Use of GPCR Modeling and SDM Experiments to Understand Ligand Binding.-� Computational Support of Medicinal Chemistry Effort in Industrial Setting.-� Investigating Small-Molecule Ligand Binding to G Protein-Coupled Receptors with Biased or Unbiased Molecular Dynamics Simulations -- Ligand-Based Methods in GPCR Computer-Aided Drug Design.-� Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery -- Cheminformatics in the Service of GPCR Drug Discovery.-� Modeling and Deorphanization of Orphan GPCRs.
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