Alexander Heifetz – författare

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 resourcefor structural and molecular biologists, computational and medicinal chemists, pharmacologists, and drug designers.

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 resourcefor structural and molecular biologists, computational and medicinal chemists, pharmacologists, and drug designers.

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 resourcefor 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.

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 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 inMolecular 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.

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 inMolecular 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.

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 inMolecular 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.

This volume explores the practical application of quantum mechanical (QM) methods in modern drug design. Its chapters cover methodological developments, integration with molecular dynamics and artificial intelligence, applications to diverse target classes and modalities, and perspectives on automation and high-throughput deployment. The book is intended for a broad readership, including computational and medicinal chemists, structural biologists, pharmacologists, and researchers working at the interface of physics, chemistry, and drug design. Written for the highly successful Methods in Molecular Biology series, the chapters provide the detailed guidance necessary for successful implementation of these techniques.Expansive and cutting-edge, Quantum Mechanics Applications in Drug Design serves as both an authoritative reference and a practical guide for scientists seeking to harness quantum mechanical methods to accelerate and improve the design of new therapeutics.

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.