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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.Â
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.
This volume looks at modern computational strategies and techniques
used in GPCR drug discovery including structure and ligand-based
approaches and cheminformatics. The chapters in this book describe
how these approaches can be applied to address key drug discovery
issues, such as receptor structure modelling, function and
dynamics, prediction of protein-water-ligand interactions and
binding kinetics, free energy of binding, interconversion between
agonists and antagonists, deorphanization of GPCRs, and the
discovery of biased and allosteric modulators. 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 modelling protocols, and tips on troubleshooting and
avoiding known pitfalls. Cutting-edge and unique,Computational
Methods for GPCR Drug Discovery is a valuable resource for
structural and molecular biologists, computational and medicinal
chemists, pharmacologists, and drug designers.
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.
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.
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