High Performance Computing for Drug Discovery and Biomedicine [electronic resource] / edited by Alexander Heifetz.

Format
Book
Language
English
Εdition
1st ed. 2024.
Published/​Created
New York, NY : Springer US : Imprint: Humana, 2024.
Description
1 online resource (XIII, 429 p.)

Details

Subject(s)
Editor
Series
Summary note
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.
Contents
  • 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.
ISBN
1-0716-3449-6
Doi
  • 10.1007/978-1-0716-3449-3
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