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Author: Gennady Verkhivker Publisher: Frontiers Media SA ISBN: 2889631362 Category : Languages : en Pages : 129
Book Description
Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.
Author: Gennady Verkhivker Publisher: Frontiers Media SA ISBN: 2889631362 Category : Languages : en Pages : 129
Book Description
Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.
Author: Kristof T. Schütt Publisher: Springer Nature ISBN: 3030402452 Category : Science Languages : en Pages : 473
Book Description
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Author: Tamar Schlick Publisher: Springer Science & Business Media ISBN: 0387224645 Category : Science Languages : en Pages : 669
Book Description
Very broad overview of the field intended for an interdisciplinary audience; Lively discussion of current challenges written in a colloquial style; Author is a rising star in this discipline; Suitably accessible for beginners and suitably rigorous for experts; Features extensive four-color illustrations; Appendices featuring homework assignments and reading lists complement the material in the main text
Author: Elif Ozkirimli Publisher: Frontiers Media SA ISBN: 2889741214 Category : Science Languages : en Pages : 147
Book Description
Dr. Elif Ozkirimli is a full time employee of F. Hoffmann-La Roche AG, Switzerland and Dr. Artur Yakimovich is a full time employee of Roche Products Limited, UK. All other Topic Editors declare no competing interests with regards to the Research Topic.
Author: Domitilla Del Vecchio Publisher: Princeton University Press ISBN: 1400850509 Category : Science Languages : en Pages : 287
Book Description
This book provides an accessible introduction to the principles and tools for modeling, analyzing, and synthesizing biomolecular systems. It begins with modeling tools such as reaction-rate equations, reduced-order models, stochastic models, and specific models of important core processes. It then describes in detail the control and dynamical systems tools used to analyze these models. These include tools for analyzing stability of equilibria, limit cycles, robustness, and parameter uncertainty. Modeling and analysis techniques are then applied to design examples from both natural systems and synthetic biomolecular circuits. In addition, this comprehensive book addresses the problem of modular composition of synthetic circuits, the tools for analyzing the extent of modularity, and the design techniques for ensuring modular behavior. It also looks at design trade-offs, focusing on perturbations due to noise and competition for shared cellular resources. Featuring numerous exercises and illustrations throughout, Biomolecular Feedback Systems is the ideal textbook for advanced undergraduates and graduate students. For researchers, it can also serve as a self-contained reference on the feedback control techniques that can be applied to biomolecular systems. Provides a user-friendly introduction to essential concepts, tools, and applications Covers the most commonly used modeling methods Addresses the modular design problem for biomolecular systems Uses design examples from both natural systems and synthetic circuits Solutions manual (available only to professors at press.princeton.edu) An online illustration package is available to professors at press.princeton.edu
Author: Martin J. Field Publisher: Cambridge University Press ISBN: 1139465813 Category : Science Languages : en Pages : 294
Book Description
Molecular simulation is a powerful tool in materials science, physics, chemistry and biomolecular fields. This updated edition provides a pragmatic introduction to a wide range of techniques for the simulation of molecular systems at the atomic level. The first part concentrates on methods for calculating the potential energy of a molecular system, with new chapters on quantum chemical, molecular mechanical and hybrid potential techniques. The second part describes methods examining conformational, dynamical and thermodynamical properties of systems, covering techniques including geometry-optimization, normal-mode analysis, molecular dynamics, and Monte Carlo simulation. Using Python, the second edition includes numerous examples and program modules for each simulation technique, allowing the reader to perform the calculations and appreciate the inherent difficulties involved in each. This is a valuable resource for researchers and graduate students wanting to know how to use atomic-scale molecular simulations. Supplementary material, including the program library and technical information, available through www.cambridge.org/9780521852524.
Author: Francesco L. Gervasio Publisher: John Wiley & Sons ISBN: 3527342656 Category : Medical Languages : en Pages : 368
Book Description
A guide to applying the power of modern simulation tools to better drug design Biomolecular Simulations in Structure-based Drug Discovery offers an up-to-date and comprehensive review of modern simulation tools and their applications in real-life drug discovery, for better and quicker results in structure-based drug design. The authors describe common tools used in the biomolecular simulation of drugs and their targets and offer an analysis of the accuracy of the predictions. They also show how to integrate modeling with other experimental data. Filled with numerous case studies from different therapeutic fields, the book helps professionals to quickly adopt these new methods for their current projects. Experts from the pharmaceutical industry and academic institutions present real-life examples for important target classes such as GPCRs, ion channels and amyloids as well as for common challenges in structure-based drug discovery. Biomolecular Simulations in Structure-based Drug Discovery is an important resource that: -Contains a review of the current generation of biomolecular simulation tools that have the robustness and speed that allows them to be used as routine tools by non-specialists -Includes information on the novel methods and strategies for the modeling of drug-target interactions within the framework of real-life drug discovery and development -Offers numerous illustrative case studies from a wide-range of therapeutic fields -Presents an application-oriented reference that is ideal for those working in the various fields Written for medicinal chemists, professionals in the pharmaceutical industry, and pharmaceutical chemists, Biomolecular Simulations in Structure-based Drug Discovery is a comprehensive resource to modern simulation tools that complement and have the potential to complement or replace laboratory assays for better results in drug design.
Author: Gregory R. Bowman Publisher: Springer Science & Business Media ISBN: 9400776063 Category : Science Languages : en Pages : 148
Book Description
The aim of this book volume is to explain the importance of Markov state models to molecular simulation, how they work, and how they can be applied to a range of problems. The Markov state model (MSM) approach aims to address two key challenges of molecular simulation: 1) How to reach long timescales using short simulations of detailed molecular models. 2) How to systematically gain insight from the resulting sea of data. MSMs do this by providing a compact representation of the vast conformational space available to biomolecules by decomposing it into states sets of rapidly interconverting conformations and the rates of transitioning between states. This kinetic definition allows one to easily vary the temporal and spatial resolution of an MSM from high-resolution models capable of quantitative agreement with (or prediction of) experiment to low-resolution models that facilitate understanding. Additionally, MSMs facilitate the calculation of quantities that are difficult to obtain from more direct MD analyses, such as the ensemble of transition pathways. This book introduces the mathematical foundations of Markov models, how they can be used to analyze simulations and drive efficient simulations, and some of the insights these models have yielded in a variety of applications of molecular simulation.
Author: Vijai Singh Publisher: Springer Nature ISBN: 9811979111 Category : Science Languages : en Pages : 368
Book Description
This book elucidates the sustainable production of commercially important biomolecules in medicines, food, and beverage processing, through biological systems, including microorganisms, animal cells, plant cells, tissues, enzymes, and in vitro. It discusses promising technologies for the manipulation of cells including, genetic engineering, synthetic biology, genome editing, and metabolic engineering. The initial chapters of the book introduce topics on biomanufacturing, circular economy, strain design and improvement, upstream and downstream processing. The subsequent chapters cover artificial intelligence-assisted production, designer cell factories, biosensors for monitoring biomolecules, different cells factories, biosynthetic pathways, and genome editing approaches for scale-up biomanufacturing. Lastly, the book discusses the opportunities and challenges of implementing biological systems for the production of biomolecules. This book is a valuable source for students, researchers, scientists, clinicians, stakeholders, policymakers, and practitioners to understand biomanufacturing for the sustainable production of biomolecules.
Author: Gennady Verkhivker
Author: Gennady Verkhivker
Author: Kristof T. Schütt
Author: Tamar Schlick
Author: Elif Ozkirimli
Author: Domitilla Del Vecchio
Author: Martin J. Field
Author: Francesco L. Gervasio
Author: Gregory R. Bowman
Author: Zhongjie Liang
Author: Vijai Singh