A General Chemistry Experiment to Predict and Measure Functional-Group Stretching Frequencies

An experiment suitable for first-year students is reported. In this activity, students use a molecular modeling program to compute infrared spectra for a series of molecules. From the data obtained, students generate a group frequency chart and use it to identify unknowns. This provides students with an introduction to vibrational spectroscopy and the use of molecular modeling.     

Molecular Modeling and Nuclear Overhauser Enhancement Spectroscopy (NOESY): Tools for Studying the Regioselective Bromination of 3-Bromoanisole

The use of molecular modeling for predicting chemical reactivity has been highly successful in the industrial and academic research communities. For this reason, increased emphasis has been placed on molecular modeling in the undergraduate curriculum. In the described experiment, the bromination of 3-bromoanisole, students are encouraged to use molecular modeling software as a tool for predicting chemical reactivity. Besides introducing students to molecular modeling, this experiment incorporates the use of nontraditional, less hazardous reagents and solvents for electrophilic aromatic bromination reactions. Lastly, nuclear Overhauser enhancement spectroscopy (NOESY) is introduced as a tool for structural elucidation. Although there are a number of aspects to this experiment, two 3-hour laboratory periods are sufficient because the results from semiempirical (AMI) geometry optimizations, which are complete in seconds, were almost identical to the higher order, more time-intensive ab initio (3-21G*) calculations. In addition, the experimental time was greatly shortened by the discovery that catalytic HCl(aq) reduces the reaction time from 5 hours to 18 minutes.     

Chemical technologies for probing embryonic development

Embryogenesis is a remarkable program of cell proliferation, migration, and differentiation that transforms a single fertilized egg into a complex multicellular organism. Understanding this process at the molecular and systems levels will require an interdisciplinary approach, including the concepts and technologies of chemical biology. This tutorial review provides an overview of chemical tools that have been used in developmental biology research, focusing on methods that enable spatiotemporal control of gene function and the visualization of embryonic patterning. Limitations of current approaches and future challenges are also discussed.     

Harnessing the Combined Power of Theoretical and Experimental Data through Multiscale Informatics

Monumental, recent and rapidly continuing, improvements in the capabilities of ab initio theoretical kinetics calculations provides reason to believe that progress in the field of chemical kinetics can be accelerated through a corresponding evolution of the role of theory in kinetic modeling and its relationship with experiment. The present article reviews and provides additional demonstrations of the unique advantages that arise when theoretical and experimental data across multiple scales are considered on equal footing, including the relevant uncertainties of both, within a single mathematical framework. Namely, the multiscale informatics framework simultaneously integrates information from a wide variety of sources and scales: ab initio electronic structure calculations of molecular properties, rate constant determinations for individual reactions, and measured global observables of multireaction systems. The resulting model representation consists of a set of theoretical kinetics parameters (with constrained uncertainties) that are related through elementary kinetics models to rate constants (with propagated uncertainties) that in turn are related through physical models to global observables (with propagated uncertainties). An overview of the approach and typical implementation is provided along with a brief discussion of the major uncertainties (parametric and structural) in theoretical kinetics calculations, kinetic models for complex chemical mechanisms, and physical models for experiments. Higher levels of automation in all aspects, including closed‐loop autonomous mixed‐experimental‐and‐computational model improvement, are advocated for facilitating scalability of the approach to larger systems with reasonable human effort and computational cost. The unique advantages of combining theoretical and experimental data across multiple scales are illustrated through a series of examples. Previous results demonstrating the utility of simultaneous interpretation of theoretical and experimental data for assessing consistency in complex systems and for reliable, physics‐based extrapolation of limited data are briefly summarized. New results are presented to demonstrate the high predictive accuracy of multiscale informed models for both small (molecular properties) and large (global observables) scales. These new results provide examples where the optimization yields physically realistic parameter adjustments and where physical model uncertainties in experiments are larger than kinetic model uncertainties. New results are also presented to demonstrate the utility of the multiscale informatics approach for design of experiments and theoretical calculations, accounting for both theoretical and experimental existing knowledge as well as relevant parametric and structural uncertainties in interpreting potential new data. These new results provide examples where neglecting structural uncertainties in design of experiments results in failure to identify the most worthwhile experiment. Further progress in the chemical kinetics field (particularly at the intersection of theory, kinetic modeling, and experiment) would benefit from increased attention to understanding parametric and structural uncertainties for all three—the uncertainty magnitude and cross‐correlations among model parameters as well as limitations of the model structures themselves.     

An effective continuum approach for modeling non-equilibrium structural evolution of protein nanofiber networks

We quantify the formation and evolution of protein nanofibers using a new phase field modeling framework and compare the results to transmission electron microscopy measurements (TEM) and time-dependent growth measurements given in the literature. The modeling framework employs a set of effective continuum equations combined with underlying nanoscale forces and chemical potential relations governing protein nanofiber formation in solution. Calculations based on the theoretical framework are implemented numerically using a nonlinear finite element phase field modeling approach that couples homogenized protein molecular structure via a vector order parameter with chemical potential relations that describe interactions between the nanofibers and the surrounding solution. Homogenized, anisotropic molecular and chemical flux relations are found to be critical in obtaining nanofiber growth from seed particles or a random monomer bath. In addition, the model predicts both sigmoidal and first-order growth kinetics for protein nanofibers for unseeded and seeded models, respectively. These simulations include quantitative predictions on time scales of typical protein self-assembly behavior which qualitatively match TEM measurements of the RADA16-I protein and growth rate measurements for amyloid nanofibers from the literature. For comparisons with experiments, the numerical model performs multiple nanofiber protein evolution simulations with a characteristic length scale of ～2.4 nm and characteristic time scale of ～9.1 h. These results provide a new modeling tool that couples underlying monomer structure with self-assembling nanofiber behavior that is compatible with various external loadings and chemical environments.     

复杂化学体系的分子模拟研究——2013年诺贝尔化学奖浅析

随着化学科学研究水平的日益提高,人们对物质世界的分子水平的理解也在逐步加深。化学研究中对原理性理解的要求越来越高,也要求越来越准确的定量研究。化学传统上以实验为主,而现代化学对理论具有了强烈依赖性。理论化学的发展取得了巨大进步,成为化学学科的一个重要组成部分。理论与计算化学不但帮助解释实验结果,更提供对实验观察的预言和对材料药物等的合理设计。通过与统计力学和量子力学的紧密结合,在现代计算机技术的支持下分子模拟在化学、材料、生物等学科中得到广泛应用,处理多尺度实际体系的能力越来越强,具有非常广阔的发展前景。但分子模拟作为一个研究手段还需要大力发展,以提高其普适性,使之具有更坚实的理论基础,同时更加系统化和实用化。     

Modeling robust QSAR

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.     

Reassignments and Corroborations of Oxo‐Bridged Natural Products Directed by OSE and DU8+ NMR Computation

Structural misassignments of natural products are prevalent in the literature. Developing methods and theoretical concepts to assist those undertaking structural elucidation is therefore of paramount importance, such that biologists and synthetic chemists avoid pursuing phantom chemical entities. Herein described is a strategy for predicting the isolabilities of oxygen‐substituted bridgehead natural products based on calculations of olefin strain energies, NMR chemical shifts and coupling constants (DU8+). This approach provides corroborating evidence for the structures of certain bridgehead alkene natural products while leading to the reassignment of several other structures.     

Quantum chemical methods for the investigation of photoinitiated processes in biological systems: theory and applications.

With the advent of modern computers and advances in the development of efficient quantum chemical computer codes, the meaningful computation of large molecular systems at a quantum mechanical level became feasible. Recent experimental effort to understand photoinitiated processes in biological systems, for instance photosynthesis or vision, at a molecular level also triggered theoretical investigations in this field. In this Minireview, standard quantum chemical methods are presented that are applicable and recently used for the calculation of excited states of photoinitiated processes in biological molecular systems. These methods comprise configuration interaction singles, the complete active space self-consistent field method, and time-dependent density functional theory and its variants. Semiempirical approaches are also covered. Their basic theoretical concepts and mathematical equations are briefly outlined, and their properties and limitations are discussed. Recent successful applications of the methods to photoinitiated processes in biological systems are described and theoretical tools for the analysis of excited states are presented.     

A Cognitive Modeling Tutor Supporting Student Inquiry for Balancing Chemical Equations

Improved interactive tutoring capabilities in educational software for chemistry problem solving is an important need that has been clearly articulated by teachers and students. The purpose of this work is to examine the incorporation of new concepts from the field of artificial intelligence (AI) as a route to meaningful individualized tutoring. The basic shift is to replace specific foreknowledge of problems with a direct representation of chemical and pedagogical principles and then simulate reasoning using these principles to tutor students. To assess the potential of an AI-based approach, we have developed a prototype tutorial program for balancing chemical equations that contains two important advances. First, the system can create a worked-out solution with detailed explanations for any equation entered by the student or teacher. Unlike a conventional tutorial, this is done dynamically, without the equation being stored ahead of time. Second, the program can interactively answer a variety of detailed questions about its work at each step. Studying worked-out examples plays an important role in learning, and this approach to supporting interactive student inquiry is being investigated as a method of cognitive modeling and apprenticeship intended to foster the students own self-explanation and question-asking abilities.     

Development of photoresponsive supramolecular machines inspired by biological molecular systems

In the growing research area on molecular machinery, light is one of the attractive and useful stimuli source to operate synthetic molecular machines, since light allows selective operation of photoresponsive moieties without additives. We have proposed a new approach to design of photoresponsive molecular machines by interlocking mechanical motions between photoresponsive and movable units through covalent and non-covalent bonds. This approach is inspired by biological molecular machines consisting of multiple protein subunits, and potentially useful for construction of giant mechanical systems. In this review, we will introduce our concepts of the molecular design with several successful examples as well as their applications for controlling chemical events, and also glance at a semi-biological molecular machine controllable by light, which reveals a potential of biological systems for development of elaborate molecular devices.     

Testing the limits of sensitivity in a solid-state structural investigation by combined X-ray powder diffraction, solid-state NMR, and molecular modelling

A solid state structural investigation of ethoxzolamide is performed on microcrystalline powder by using a multi-technique approach that combines X-ray powder diffraction (XRPD) data analysis based on direct space methods with information from (13)C((15)N) solid-state Nuclear Magnetic Resonance (SS-NMR) and molecular modeling. Quantum chemical computations of the crystal were employed for geometry optimization and chemical shift calculations based on the Gauge Including Projector Augmented-Wave (GIPAW) method, whereas a systematic search in the conformational space was performed on the isolated molecule using a molecular mechanics (MM) approach. The applied methodology proved useful for: (i) removing ambiguities in the XRPD crystal structure determination process and further refining the derived structure solutions, and (ii) getting important insights into the relationship between the complex network of non-covalent interactions and the induced supra-molecular architectures/crystal packing patterns. It was found that ethoxzolamide provides an ideal case study for testing the accuracy with which this methodology allows to distinguish between various structural features emerging from the analysis of the powder diffraction data.     

Computer-aided drug design at Boehringer Ingelheim

Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.     

Chemical Concepts from Density Functional TheorySCI北大核心CSCD

Chemical concepts such as structure,bonding,reactivity,etc.have been widely used in the literature and text books to appreciate molecular properties and chemical transformations.Even though modern theoretical and computational chemistry is well established from the perspective of accuracy and complexity,how to quantify these concepts is a still unresolved task.Conceptual density functional theory and its related recent developments provide unique opportunities to tackle this problem.In this Special Issue,27 contributions from top investigators over the world are collected to highlight the state-of-art research on this topic,which not only showcases the status of where we are now but also unveils a number to possible future directions to be pursued.