Machine Learning in Biomolecular Simulations PDF Download
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Author: Gennady Verkhivker
Publisher: Frontiers Media SA
ISBN: 2889631362
Category :
Languages : en
Pages : 129
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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
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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: Sergio Decherchi
Publisher: Frontiers Media SA
ISBN: 2889668630
Category : Science
Languages : en
Pages : 119
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Book Description
Dr. Sergio Decherchi and Dr. Andrea Cavalli are co-founders of BiKi Technologies s.r.l. - a company that commercializes a Molecular Dynamics-based software suite for drug discovery. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Author: Kristof T. Schütt
Publisher: Springer Nature
ISBN: 3030402452
Category : Science
Languages : en
Pages : 473
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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
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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
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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: Mohammad Ashrafuzzaman
Publisher: CRC Press
ISBN: 1003821634
Category : Science
Languages : en
Pages : 435
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Book Description
This textbook provides an introduction to the fundamental and applied aspects of biophysics for advanced undergraduate and graduate students of physics, chemistry, and biology. The application of physics principles and techniques in exploring biological systems has long been a tradition in scientific research. Biological systems hold naturally inbuilt physical principles and processes which are popularly explored. Systematic discoveries help us understand the structures and functions of individual biomolecules, biomolecular systems, cells, organelles, tissues, and even the physiological systems of animals and plants. Utilizing a physics- based scientific understanding of biological systems to explore disease is at the forefront of applied scientific research. This textbook covers key breakthroughs in biophysics whilst looking ahead to future horizons and directions of research. It contains models based on both classical and quantum mechanical treatments of biological systems. It explores diseases related to physical alterations in biomolecular structures and organizations alongside drug discovery strategies. It also discusses the cutting- edge applications of nanotechnologies in manipulating nanoprocesses in biological systems. Key Features: • Presents an accessible introduction to how physics principles and techniques can be used to understand biological and biochemical systems. • Addresses natural processes, mutations, and their purposeful manipulation. • Lays the groundwork for vitally important natural scientific, technological, and medical advances. Mohammad Ashrafuzzaman, a biophysicist and condensed matter scientist, is passionate about investigating biological and biochemical processes utilizing physics principles and techniques. He is a professor of biophysics at King Saud University’s Biochemistry Department in the College of Science, Riyadh, Saudi Arabia; the co- founder of MDT Canada Inc., and the founder of Child Life Development Institute, Edmonton, Canada. He has authored Biophysics and Nanotechnology of Ion Channels, Nanoscale Biophysics of the Cell, and Membrane Biophysics. He has also published about 50 peer- reviewed articles and several patents, edited two books, and has been serving on the editorial boards of Elsevier and Bentham Science journals. Dr. Ashrafuzzaman has held research and academic ranks at Bangladesh University of Engineering & Technology, University of Neuchatel (Switzerland), Helsinki University of Technology (Finland), Weill Medical College of Cornell University (USA), and University of Alberta (Canada). During 2013– 2018 he also served as a Visiting Professor at the Departments of Oncology, and Medical Microbiology and Immunology, of the University of Alberta. Dr. Ashrafuzzaman earned his highest academic degree, Doctor of Science (D.Sc.) in condensed matter physics from the University of Neuchatel, Switzerland in 2004.
Author: Valerie Daggett
Publisher: Elsevier
ISBN: 0080493785
Category : Medical
Languages : en
Pages : 477
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Book Description
Protein Simulation focuses on predicting how protein will act in vivo. These studies use computer analysis, computer modeling, and statistical probability to predict protein function.* Force Fields* Ligand Binding* Protein Membrane Simulation* Enzyme Dynamics* Protein Folding and unfolding simulations
Author: Nedunchezhian Swaminathan
Publisher: Springer Nature
ISBN: 303116248X
Category : Technology & Engineering
Languages : en
Pages : 353
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Book Description
This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
Author: Guohui Li
Publisher: Elsevier
ISBN: 0323959180
Category : Science
Languages : en
Pages : 399
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Book Description
Chemical Theory and Multiscale Simulation in Biomolecules: From Principles to Case Studies helps readers understand what simulation is, what information modeling of biomolecules can provide, and how to compare this information with experiments. Beginning with an introduction to computational theory for modeling, the book goes on to describe how to control the conditions of modeling systems and possible strategies for time-cost savings in computation. Part Two further outlines key methods, with step-by-step guidance supporting readers in studying and practicing simulation processes. Part Three then shows how these theories are controlled and applied in practice, through examples and case studies on varied applications. This book is a practical guide for new learners, supporting them in learning and applying molecular modeling in practice, whilst also providing more experienced readers with the knowledge needed to gain a deep understanding of the theoretical background behind key methods. - Presents computational theory alongside case studies to help readers understand the use of simulation in practice - Includes extensive examples of different types of simulation methods and approaches to result analysis - Provides an overview of the current academic frontier and research challenges, encouraging creativity and directing attention to current problems
Author:
Publisher: Academic Press
ISBN: 0323986056
Category : Science
Languages : en
Pages : 266
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Book Description
Virtual Screening and Drug Docking, Volume 59 in the Annual Reports on Medicinal Chemistry series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of timely topics, including Can docking scoring functions guarantee success in virtual screening?, No dance, no partner! A tale of flexibility in docking and virtual screening, Handling Imbalance Data in Virtual Screening, Rational computational approaches to predict novel drug candidates against leishmaniasis, Virtual screening against Mtb DNA gyrase: Applications and success stories, Using Filters in Virtual Screening: A Brief Guide to Minimize Errors and Maximize Efficiency, and more. Additional chapters in the new release include Machine Learning and Deep Learning Strategies for Virtual Screening, Applications of the Virtual Screening to find the novel HIV-1 therapeutic agents, and Large-scale screening of small molecules with docking strategies and its impact on drug discovery. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Annual Reports on Medicinal Chemistry series - Updated release includes the latest information on Virtual Screening and Drug Docking
