Special Collection: Computational Chemistry

Chemists know well the value of an experimental or a theoretical result, but what is the value of a computational result? Simulation is neither theory nor experience, nor a mere calculation tool, but a genuine way of approaching reality that is transforming the scientific method. In some cases, it offers explanations to observations or experiments that seem incomprehensible because they are too complex. In this case, the computation serves as a relief. An experiment that converges with a certain computation has more scientific value than an experiment that does not converge with anything at all. In other cases, contribution of computational chemistry is essential because there is no experimental manner to determine what happens during a chemical process; for instance, in the path from reactants to products in (fast) reactions. Now, computational chemistry provides additional information that is not possible to obtain from experiments, so it is a valuable complement to them. Indeed, fruitful synergy between computation and experiment has led to the approach of theory-driven experimentation. Finally, computational chemistry helps to legitimize models or theories that have little opportunity to be contrasted with reality. In this situation, computational chemistry is not experience, but it does substitute it in relation to theory. In the present special collection, we have examples of the different ways computational chemistry helps chemists to interpret the electronic and molecular structure of molecules and their reactivity.     

Open Chemistry,JupyterLab, REST,and quantum chemistry

Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open‐source framework that offers an end‐to‐end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open‐source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state‐of‐the‐art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.     

Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.     

Chemoinformatics methods for systematic comparison of molecules from natural and synthetic sources and design of hybrid libraries

Until recently, the field of diversity and library design has more or less ignored natural products as a compound source. This is probably due to at least two reasons. First, combinatorial and reaction-based approaches have been major focal points in the early days of computational library design. In addition, a widespread view is that natural products are often highly complex and not amenable to medicinal chemistry efforts. This contribution introduces recent computational approaches to systematically analyze natural molecules and bridge the gap between natural products and synthetic chemistry programs. Large scale comparisons of natural and synthetic molecules are discussed as well as studies designed to identify `synthetic mimics' of natural products with specific activity. In addition, a concept for the design of natural/synthetic hybrid libraries is introduced. Although research in this area is still in its early stages, an important lesson to be learned from computational analyses is that there is no need to a priori `shy away' from natural products as a source for molecular design.     

计算化学实验的课程建设

With the fast development of theoretical chemistry methodologies and computer hardware and software technologies, computational chemistry has become more and more imperative in chemical science. Accordingly, we have initiated "Computational Chemistry Experiments" course based on "Introductory Computational Chemistry" course developed in the Department of Chemistry, Tsinghua University since 2013, to provide basic training and computational chemistry practices for undergraduate students. The popular quantum chemistry software Gaussian is used as the major tool in the course. We focus on the exploratory feature of computational chemistry, creating initial structures and optimizing geometries, to provide fundamental training for students to comprehend the difference of traditional experimental chemistry and modern computational chemistry. Systematic research training is offered to develop the creative thinking capacity of students in using computational chemistry methods in chemistry education and research.     

Integrating virtual screening and combinatorial chemistry for accelerated drug discovery

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high-throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.     

Estimation of Free Radical Polymerization Rate Coefficients Using Computational Chemistry

This study explores the application of computational chemistry to estimate free radical polymerization rate coefficients. The Evans-Polanyi relationship is combined with computed heats of polymerization to estimate copolymerization reactivity ratios for many vinyl monomer pairs, focusing on acrylates, methacrylates and styrene, with accuracy assessed by comparison to experimental values. The effect of different optimization approaches on the values of thermodynamic properties is explored, and it is concluded that a combination of conventional optimization and relaxed potential energy scans was most effective at identifying global minima. The difference between thermodynamic properties calculated using the harmonic oscillator treatment and a hindered rotor model is evaluated for methyl methacrylate polymerization.     

The Role of Mathematics in the Experimental/Theoretical/Computational Trichotomy of Chemistry

The drastically increasing availability ofmodern computers coupled with the equally drasticallylower cost of a given amount of computer power inrecent years has resulted in the evolution of thetraditional experimental/theoretical dichotomy inchemistry into anexperimental/theoretical/computational trichotomy. This trichotomy can be schematically represented by atriangle (the ETC triangle) with experimental,theoretical, and computational chemistry at the threevertices. The ET and EC edges of the ETC triangledepict the uses of theoretical and computationalchemistry, respectively, to predict and interpretexperimental results. The TC edge depicts therelationship between theoretical and computationalchemistry. Mathematics plays an increasing role in allaspects of chemistry, particularly theoreticalchemistry, and has led to the evolution of thediscipline of mathematical chemistry. Research inmathematical chemistry can be considered to lie on achemistry-mathematics continuum depending on therelative depths of the underlying chemistry andmathematics. Examples of the author's own researchlying near each end of the chemistry-mathematicscontinuum include his work on applications of graphtheory and topology in inorganic coordination andcluster chemistry lying near the chemistry end and hiswork on chirality algebra lying near the mathematicsend. The general points in this essay are illustratedby an analysis of the roles of computational andtheoretical chemistry in developing an understandingof structure and bonding in deltahedral boranes andrelated carboranes. This work has allowed extensionof the concept of aromaticity from two dimensions asin benzene and other planar hydrocarbons to the thirddimension in deltahedral boranes. This revised version was published online in August 2006 with corrections to the Cover Date.     

A Combined Theoretical and Experimental Approach to the Study of the Structural and Electronic Properties of Curcumin as a Function of the Solvent

Curcumin, a chemical compound present in the well-known Indian spice turmeric, has uses in many different fields ranging from medicinal chemistry to the dye industry. Its poor water solubility, though, makes Curcumin difficult to handle, making it less appealing for potential uses. The principal aim of this work is to perform a computational study of the structural and electronic properties of Curcumin {IUPAC name: 1,7-bis(4-hydroxy-3-methoxyphenyl)-1,6-heptadiene-3,5-dione} in several solvents, and a comparison with experimental data. Rotameric equilibria, vibrational and thermochemical analysis, and electronic absorption spectra (with ab initio and semi-empirical methodologies) have been studied, both in vacuum and in three selected solvents. Different computational techniques have been applied and the results compared. Combined approaches resulted in very satisfactory results. Interesting results have emerged, which suggest subsequent investigations about the nature of the excited states and potential derivatives of Curcumin that possibly have non-linear optical applications, as a π-core for innovative materials in laser engineering and photonics.     

计算化学在手性化合物结构分析中的应用

对有机化学研究中的手性化合物, 尤其是天然产物分子的立体构型的鉴定, 日益受到研究人员的重视. 然而, 在化合物不能结晶或其它条件影响到不能用实验来直接解析其立体构型的情况下, 计算化学是一个比较有效的手段. 目前可供利用的计算方法有旋光计算, 如利用量子力学方法或矩阵模型及¹³C NMR计算等. 本文对这些不同的计算方法进行介绍.     

A Classical and Quantum Chemical Analysis of Gaseous Heat Capacity

Physical chemistry is considered to be a scientifically abstract and mathematically intensive course in the undergraduate chemistry curriculum. To most students, the physical chemistry course involves a semester that deals with macroscopic properties and another that deals with microscopic evaluations of chemical systems. They often fail to see the importance of statistical mechanics in making the connection between the content of the two semesters. In this paper, we propose a computational exercise that complements a simple physical chemistry experiment that can be used to understand the chemical basis of a macroscopic property such as the heat capacity of gases using microscopic (classical and quantum) mechanics. Students are given the opportunity to use (1) computational chemistry software to calculate the contributions of translational, rotational, and vibrational motion to the energy of molecules; (2) a graphing program to study the linear and nonlinear dependence of energy on temperature; (3) classical, quantum, and statistical mechanical theory to verify experimental data; (4) regression analysis to approximate the heat capacity constant of simple gases from energy calculations.     

Engaging undergraduate students in computational chemistry research: A tutorial for new assistant professors

In this article, we provide advice and insights, based on our own experiences, for computational chemists who are beginning new tenure-track positions at primarily undergraduate institutions. Each of us followed different routes to obtain our tenure-track positions, but we all experienced similar challenges when getting started in our new position. In this article, we discuss our approaches to seven areas that we all found important for engaging undergraduate students in our computational chemistry research, including setting up computational resources, recruiting research students, training research students, designing student projects, managing the lab, mentoring students, and student conference participation.     

Computational methods for the identification and optimisation of high quality leads

Lead identification and optimisation have evolved into multidimensional, multidisciplinary and information-driven processes. Herein, we review the contribution of computational chemistry to these processes. We focus on computational approaches developed for modelling biopharmaceutical properties, including in vitro activity, selectivity, absorption, distribution, metabolism, excretion and toxicity. Whenever possible, successful applications are mentioned.     

Advance of computational technology for simulating solvent extraction.

Due to recent significant enhancement of computer performance as well as computational techniques, molecular modeling and molecular simulations using computational chemistry can be achieved at the level of practical applications. Even in solvent extraction, the application of computational chemistry to simulations of extraction processes and the molecular design of high-performance extracting agents have gradually been increasing during the last decade. With combining the quantitative structure-property relationship between the molecule properties calculated by the computational chemistry methods and the thermodynamic properties obtained from experiments, researchers can precisely predict the next-generation of extracting agents and novel extraction processes. In this review, the concept of computational chemistry, such as molecular mechanics, molecular orbitals and molecular dynamics calculations, frequently used in the filed of solvent extraction, are outlined. Our systematic research on the solvent-extraction process utilizing MM, MO and MD calculations is also presented.     

Enzyme mechanisms: interplay of theory and experiment

An overview is given on experimental and theoretical methods applied to the study of enzyme mechanisms. While experiments provide more or less precise data on realistic systems, the obtained information often overlap and a mixture of observations has to be resolved in order to appropriately understand enzymatic processes at the molecular level. On the other hand, computations to be done on adequate models reflecting all important properties of the system, may not provide results that are accurate enough to be compared with experiments. It is stressed that in a computational study the level of sophistication of the model and the method applied to it should be about the same in order to obtain sound results. A case study, the catalytic mechanism of serine proteases, is discussed in detail calling attention to the problems where the interplay between computations and experimental studies was necessary to understand mechanistic details. It is computational chemistry that lead to the discovery of the crucial role of the protein electrostatic field in the acceleration of the enzyme reaction as well as protonation state of the side chains of the active site. We stress that computational studies are especially important in the right interpretation of experimental observations and should replace speculations based on textbook chemistry.     

Assignment of molecular structures to the electrochemical reduction products of diiron compounds related to [Fe-Fe] hydrogenase: a combined experimental and density functional theory study

The reduction chemistry of (mu-bridge)[Fe(CO)3]2 [bridge = propane-1,3-dithiolate (1) and ethane-1,2-dithiolate (2)] is punctuated by the formation of distinct products, resulting in a marked difference in CO inhibition of electrocatalytic proton reduction. The products formed following reduction of 2 have been examined by a range of electrochemical, spectroelectrochemical, and spectroscopic approaches. Density functional theory has allowed assessment of the relative energies of the structures proposed for the reduction products and agreement between the calculated spectra (IR and NMR) and bond distances with the experimental spectra and EXAFS-derived structural parameters. For 1 and 2, one-electron reduction is accompanied by dimerization, but the structure, stability, and reaction with CO of the dimer is different in the two cases, and this is responsible for the different CO inhibition response for electrocatalytic proton reduction. Calculations of the alternate structures of the two-electron, one-proton reduced forms of 2 show that the isomers with terminally bound hydrides are unlikely to play a significant role in the chemistry of these species. The hydride-transfer chemistry of the 1B species is more reasonably attributed to a hydride-bridged form. The combination of experimental and computational results provides a solid foundation for the interpretation of the reduction chemistry of dithiolate-bridged diiron compounds, and this will underpin translation of the diiron subsite of the [FeFe] hydrogenase H cluster into an abiological context.     

Enabling drug discovery project decisions with integrated computational chemistry and informatics

Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.     

An evaluation of experimental design in QSAR modelling utilizing the k‐medoid clustering

A reliable selection of a representative subset of chemical compounds has been reported to be crucial for numerous tasks in computational chemistry and chemoinformatics. We investigated the usability of an approach on the basis of the k‐medoid algorithm for this task and in particular for experimental design and the split between training and validation set. We therefore compared the performance of models derived from such a selection to that of models derived using several other approaches, such as space‐filling design and D‐optimal design. We validated the performance on four datasets with different endpoints, representing toxicity, physicochemical properties and others. Compared with the models derived from the compounds selected by the other examined approaches, those derived with the k‐medoid selection show a high reliability for experimental design, as their performance was constantly among the best for all examined datasets. Of all the models derived with all examined approaches, those derived with the k‐medoid approach were the only ones that showed a significantly improved performance compared with a random selection, for all datasets, the whole examined range of selected compounds and for each dimensionality of the search space. Copyright © 2012 John Wiley & Sons, Ltd.     

激酶抑制剂的计算机辅助设计

激酶是当今的第二大靶标,其高选择性、强活性抑制剂的开发是医药化学的前沿领域。本文介绍了生物信息学——多序列比对、QSAR、药效团方法、同源模建、高通量虚拟筛选、分子动力学与自由能计算、QM-MM和计算系统生物学等计算机辅助设计方法在激酶抑制剂设计中的重要应用。