Computer - based modeling in extract sciences research -I. Chemical Sciences

How do you measure the mechanical forces generated by single cells? What are the cues that guide cell migration? What are the interactions between cells that account for cooperative behaviors among cells? To answer these and many other basic questions about biological function, we must have the

Digital data have become a torrent engulfing every area of business, science and engineering disciplines. In the age of Big Data, deriving values and insights from large amounts of data using rich analytics becomes an important differentiating capability for competitiveness, success and leadership in every field. Scientists and engineers of many different domains are increasingly clamouring for mechanisms to manage and analyse the massive quantities of information now available in order to obtain new answers and extract from it maximum value. Computational modelling and simulation is the central technology to numerous of these domains. Molecular Dynamics (MD) is a computational simulation technique that describes the physical forces and movements of interacting microscopic elements such atoms and molecules. MD has important applications in the fields of chemistry, biotechnology, pharmaceutical industry, energy, climate or materials science, among others. Advanced MD algorithms include not only Molecular Mechanics (MM), but also Quantum Mechanics (QM) approaches, raising important big data challenges still to be sorted out. MD simulations perform an iterative process generating large amounts of data in streaming. Current software technology is far from being able to manage, analyze and visualize the extremely large and complex data sets generated by important molecular processes. This paper analyzes the current big data limits in the Computational Chemistry field, especially in the MD processes. To overcome these challenging situations, this work provide guidance for future research including advances in scalable algorithms for data analysis, dynamic query technology, data models and storage strategies, parallel executions, I/O optimization, and interactive visual exploration and analysis of MD data.

Although atomistic explicit-solvent Molecular Dynamics (MD) is a popular tool to study protein-RNA recognition, satisfactory MD description of protein-RNA complexes is not always achieved. Unfortunately, it is often difficult to separate MD simulation instabilities primarily caused by the simple point-charge molecular mechanics (MM) force fields from problems related to the notorious uncertainties in the starting structures. Herein, we report a series of large-scale QM/MM calculations on the U1A protein-RNA complex. This experimentally well-characterized system has an intricate protein-RNA interface, which is very unstable in MD simulations. The QM/MM calculations identify several H-bonds poorly described by the MM method and thus indicate the sources of instabilities of the U1A interface in MD simulations. The results suggest that advanced QM/MM computations could be used to indirectly rationalize problems seen in MM-based MD simulations of protein-RNA complexes. As the most accurate QM method, we employ the computationally demanding meta-GGA density functional TPSS-D3(BJ)/def2-TZVP level of theory. Because considerably faster methods would be needed to extend sampling and to study even larger protein-RNA interfaces, a set of low-cost QM/MM methods is compared to the TPSS-D3(BJ)/def2-TZVP data. The PBEh-3c and B97-3c density functional composite methods appear to be suitable for protein-RNA interfaces. In contrast, HF-3c and the tight-binding Hamiltonians DFTB3-D3 and GFN-xTB perform unsatisfactorily and do not provide any advantage over the MM description. These conclusions are supported also by similar analysis of a simple HutP protein-RNA interface, which is well-described by MD with the exception of just one H-bond. Some other methodological aspects of QM/MM calculations on protein-RNA interfaces are discussed.

Molecular interactions such as those involved in hydrogen bonding of alkylalcohols, chiral recognition and hydrophobic interactions in liquid chromatography were analyzed by using energies calculated mainly by MM2. Properties of solutes were calculated by Molecular Mechanics, Molecular Dynamics, Extended Hückel and MOPAC-BlogP of CACheTM. Up to three methylene units affected the hydrogen bonding of alkylalcohols. The position of hydrogen bonding and the atomic distances were important for chiral recognition, and the molecular structure of the adsorbent in hydrophobic interactions demonstrated selectivity if the solutes were saturated hydrocarbons.

The purpose of this booklet is to help those interested in research supported by the Department of Energy's Division of Chemical Sciences, which is one of six Divisions of the Office of Basic Energy Sciences in the Office of Energy Research. Chemists, physicists, chemical engineers and others who are considering the possibility of proposing research for support by this Division will find the booklet useful for gauging the scope of the program in basic research, and the relationship of their interests to the overall program. These summaries are intended to provide a rapid means for becoming acquainted with the Chemical Sciences program to members of the scientific and technological public and interested persons in the Legislative and Executive Branches of the Government. Areas of research supported by the Division are to be seen in the section headings, the index and the summaries themselves. Energy technologies which may be advanced by use of the basic knowledge discovered in this program can be seen in the index and again (by reference) in the summaries. The contents are as follows: DOE laboratires; chemical physics; atomic physics; chemical energy; separations; analysis; chemical engineering sciences; offsite contracts; equipment funds; topical index; institutional index for offsite contracts; and investigator index.

Researchers studying biomolecules require easy-to-use, customizable tools allowing them to effectively use other molecular science packages written for molecular dynamics (MD), quantum chemistry, statistical mechanics, and molecular graphics. This paper presents a Scala tool for the computational science of biomolecules (STCSB) developed in Scala, which allows users to prepare and run MD simulations, as well as perform three-dimensional reference interaction site model (3D-RISM) calculations of biomolecules. Features of the STCSB include the following: (1) a cross-platform application running on a Java virtual machine; (2) handling hierarchical XML-based data formats such as the protein data bank markup language (PDBML); (3) prepared application programming interfaces (APIs) with both character user interface (CUI) and graphical user interface (GUI) options; (4) prepared APIs for molecular graphics; and (5) a scalable source code based on the Model-View-Controller (MVC) architectural pattern.

The theoretical pillars of theoretical chemistry are molecular quantum mechanics and classical and quantum statistical mechanics. Computational quantum chemistry has developed to the point where the electronic structure of many-atom molecules and nanomolecular assemblies can be readily and accurately computed. Computational chemistry is based also on the development and use of powerful computational tools such as molecular dynamics and Monte Carlo sampling to predict structural, thermodynamic, and dynamical properties of many-body ordered and disordered systems. These methods have been used for more than four decades to simulate the behavior of liquids, glasses, liquid crystals, simple solutions, and aqueous biomolecular systems, systems for which classical mechanics …

Molecular Dynamics (MD) simulation, supported by parallel software and special hardware, is widely used in materials, computational chemistry and biology science. With advances in FPGA capability and inclusion of embedded multipliers, lots of studies steer to focus on FPGA-accelerated MD simulations. In this paper we make an overview on the background of MD simulations and latest FPGA-based research programs. Characteristics, major approaches and problems are introduced, and possible solutions are also discussed. Finally, some future directions of research are given.

This chapter explores the diverse applications of molecular docking in computational chemistry, highlighting its central role in modern drug discovery and molecular research. Molecular docking predicts protein ligand interactions, providing insights into molecular recognition, binding affinity, and reaction mechanisms. By simulating how molecules fit and interact, docking aids in identifying potential drug candidates through virtual screening, significantly accelerating early-stage drug development. Beyond drug discovery, docking guides enzyme engineering by predicting modifications that enhance catalytic activity, enabling rational design of biocatalysts. It also supports studies of molecular recognition, helping researchers understand the structural and energetic factors underlying binding specificity and selectivity. By estimating binding energies, docking informs the prioritization of compounds for experimental testing, reducing time and costs. Recent advances have expanded docking’s capabilities. Hybrid quantum mechanics/molecular mechanics (QM/MM) approaches improve the accuracy of binding predictions by accounting for electronic effects, while integration with molecular dynamics simulations captures protein flexibility and dynamic interactions. These enhancements allow more realistic modeling of biological systems and complex chemical reactions. Overall, molecular docking serves as a versatile tool across pharmaceutical, biochemical, and chemical sciences. Its ability to efficiently model interactions in silico accelerates research, supports rational design, and guides experimental efforts, making it indispensable in contemporary molecular research.

The European Young Chemist Award 2012

Fundamental transformations in the basic logic of computing are few and far between. Since the invention of digital computers in the early 1940s, the logic underlying computation has remained the same, even as computing hardware evolved from vacuum tubes to silicon transistors. With the advent of quantum computation, a fundamental transformation is near. Quantum computation is based on a different type of logic: rather than being in one of the two states of a classical bit, a quantum bit or qubit can be in a superposition of two states simultaneously. Operations and measurements on these qubits obey the constraints of quantum mechanics. It is now understood that quantum computers have great power in principle to go beyond classical computers, but that not every application is well suited to implementation on quantum computers. For reasons explained in more detail in the Introduction, scientific problems in chemical and materials sciences are uniquely suited to take advantage of quantum computing in the relatively near future. Indeed, quantum computing offers the best hope to solve many of the most important and difficult problems in this field. For example, quantum materials, such as superconductors and complex magnetic materials, show novel kinds of ordered phases that are natural from the point of view of quantum mechanics but difficult to access via computation on classical computers. Quantum sensors based on solid materials are already widely used but could be greatly improved with insight from quantum computations, as could materials for information technologies. Quantum chemical dynamics is another example of a problem that is intrinsically well suited to studies on quantum computers. Applications of quantum chemical dynamics include catalysis, artificial photosynthesis, and other industrially important processes. Quantum computers exist in the laboratory and are beginning to exceed 50 qubits, which is roughly the size beyond which their behavior cannot be predicted or emulated on present-day classical supercomputers. While a quantum computer of 50 qubits is almost certainly not powerful enough to tackle the major scientific challenges in chemical and materials sciences, some of these major challenges start to become accessible with a few hundred qubits if error rates can be kept small. This roundtable was convened to ask how emerging quantum computers can be applied to major scientific problems in chemical and materials sciences, in light of Basic Energy Sciences’s leading role in these fields and the Department of Energy’s leading role in high-performance scientific computation more generally. The main outcome of the roundtable was a consensus that there are scientific problems of great importance on which emerging quantum computers have the potential for disruptive impact, and where comparable progress is unlikely to occur by other means.

The MoSGrid (Molecular Simulation Grid, http://www. mosgrid.de) project aims to provide remote computational chemistry services within the German Grid Initiative (D-Grid). Submission and monitoring of compute jobs, as well as the retrieval of postprocessed results are realized through a web based portal. The use of standardized portlets and a generally modular approach allows for the simultaneous and independent implementation of frontends for different molecular simulation codes. To date, functional prototypes of portlets for applications from the quantum chemical and the molecular dynamics domain are available, being represented by Gaussian and Gromacs, respectively. The implementation of other quantum chemical codes, as requested by the community, and of codes for docking simulations is in preparation. MoSGrid will furthermore foster efficient and collaborative work by providing secure but shareable repositories for validated data, as well as for reusable recipes and workflows [1-3].

Theoretical Study on Reaction Pathways of Methyl Radical with Ethylamine

BCST is concerned with areas in chemical science and technology that can contribute to the solution of important national problems: Nuclear and radiochemistry, atmospheric and chemical sciences, chemical handling in laboratories, radwaste disposal, environment, computation chemistry, curriculum, etc. Review panel member addresses are included in an appendix.

The field of Information Technology has developed vastly in a past few decades and is used as an integral part of advanced research in many areas. Information technology provided chemical science and technology with truly remarkable and revolutionary resources for computations, communications, and data management. Chemists use databases and data mining to suggest molecular structures, quantum chemistry to predict their stability, and statistical mechanics methods to calculate their properties and interactions with other species. This article aims to give an overview of role of information technology in chemical research. It describes how sophisticated simulation tools and techniques of informational technology helps in various aspects of research in chemistry. Description of one noteworthy application of IT is also given highlighting role of IT. It can be concluded that recent development in computers and software open many doors for new research in chemical sciences and providing opportunity in research and development. The chemical sciences in the twenty-first century will include information, computation, and communications capabilities as both assets and challenges. Keywords: computational chemistry, Chemical Sciences, green chemistry, simulation