Chemical Calculations Research Articles

Spontaneous Generation of -CH2CN from Acetonitrile at the Air-Water Interface.

Acetonitrile (CH3CN) is considered a very stable molecule in aqueous solutions, and its deprotonation to produce strongly basic -CH2CN requires harsh conditions. CH3CN is also present in the atmosphere, but its chemical transformation pathway at the air-water interface is unknown. In this study, we discovered and verified the unprecedented spontaneous generation of -CH2CN from the CH3CN-H2O solution at the air-water interface of microdroplets, and revealed the indirect deprotonation mechanism of CH3CN by synergistic redox of •OH and electrons in the microdroplets through the capture of key intermediates and computational chemistry. In addition, the dynamic process of indirect deprotonation-protonation was also observed. The high reactivity of -CH2CN in the droplets was revealed via nucleophilic addition to acetone, benzaldehyde, and the parent CH3CN molecule. Furthermore, the -CH2CN generated in the microdroplets underwent a barrier-free nucleophilic addition reaction with CO2 to produce 2-cyanoacetic acid for CO2 fixation. The synergistic redox reaction process revealed in this study provides new insights into microdroplet chemistry, and the distinctive CH3CN reactions identified may provide new clues to unravel the mystery of the CH3CN transformation in the atmospheric environment.

Battle of the CH motions: aliphatic vs. aromatic contributions to astronomical PAH emission and exploration of the aliphatic, aromatic, and ethynyl CH stretches

Abstract Strong anharmonic coupling between vibrational states in polycyclic aromatic hydrocarbons (PAH) produces highly mixed vibrational transitions that challenge the current understanding of the nature of the astronomical mid-infrared PAH emission bands. Traditionally, PAH emission bands have been characterized as either aromatic or aliphatic, and this assignment is used to determine the fraction of aliphatic carbon in astronomical sources. In reality, each of the transitions previously utilized for such an attribution is highly mixed with contributions from both aliphatic and aromatic CH motions as well as non-CH motions such as CC stretches. High-resolution gas-phase IR absorption measurements of the spectra of the aromatic molecules indene and 2-ethynyltoluene at the Canadian Light Source combined with high-level anharmonic quantum chemical computations reveal the complex nature of these transitions, implying that the use of these features as a marker for the aliphatic fraction in astronomical sources is not uniquely true or actually predictive. Further, the presence of aliphatic, aromatic, and ethynyl CH groups in 2-ethynyltoluene provides an internally consistent opportunity to simultaneously study the spectroscopy of all three astronomically important groups. Finally, this study makes an explicit connection between fundamental quantum mechanical principles and macroscopic astronomical chemical physics, an important link necessary to untangle the lifecycle of stellar and planetary systems.

Modification of cobalt bis(dicarbollide) ions with nitrile groups on carbon atoms: a unique low-temperature skeletal rearrangement due to the specific electron-donor character of CN substitution.

Herein, we report on the synthesis and stereochemistry of mono- and isomeric dinitrile derivatives of [(1,2-C2B9H11)2-3,3'-Co]- ions. The shape and electronic properties of CN groups can apparently surmount the strain associated with the substitution of two vicinal carbon positions. Owing to electron donation to the cage, this results in a substituent-induced rearrangement of one of the carbon atoms to the upper pentagonal ring. The molecular structure of this isomer was confirmed using sc-XRD and DFT chemical computations.

Computational Studies of Molybdenum-Containing Metal–Sulfur and Metal–Hydride Clusters

The development of transition metal clusters is an active area of research in inorganic chemistry, as they can be used as catalysts to perform chemically or biologically relevant reactions. Computational chemistry, employing density functional theory (DFT), plays a key role in rationalizing the electronic structure and properties of transition metal clusters. This article reviews recent quantum chemical studies of Mo3S4M clusters (M = Fe, Co, Ni), their CO- or N2-bound variants, and metal–hydride clusters. The ground state of the cluster systems was computed, and properties such as metal–metal bonding, orbital interactions, fluxional behavior of ligands, spectroscopy, and reaction mechanisms were rationalized and compared with available experimental results. Our research findings evidence that computational studies employing quantum chemical methods can guide experimental researchers to develop novel transition metal clusters for potential applications in catalysis.

Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models.

A significant challenge in applying machine learning to computational chemistry, particularly considering the growing complexity of contemporary machine learning models, is the scarcity of available experimental data. To address this issue, we introduce an approach that derives molecular features from an intricate neural network-based model and applies them to a simpler conventional machine learning model that is robust to overfitting. This method can be applied to predict various properties of a liquid system, including viscosity or surface tension, based on molecular features drawn from the ab initio calculated free energy of solvation. Furthermore, we propose a modified kernel model that includes Arrhenius temperature dependence to incorporate theoretical principles and diminish extreme nonlinearity in the model. The modified kernel model demonstrated significant improvements in certain scenarios and possible extensions to various theoretical concepts of molecular systems.

No Experience Required: Inclusive Undergraduate Research in Computational Biophysical Chemistry

ABSTRACT Undergraduate research is a key tool for recruitment and, more importantly, retention of science, technology, engineering, and math students. Unfortunately, many students are not aware of or do not take advantage of these opportunities and, at best, often wait until late in their college careers. For my undergraduate computational biophysical chemistry research lab I have established a no-experience-required policy that allows me to recruit and mentor more research students. It also allows me to be more inclusive and expand the pool of students who have access to research. This also results in a more diverse research group, which is particularly important at a primarily undergraduate institution that has a diverse population with a high percentage of first-generation students. Here, I lay out the procedures I use to recruit and train students of all levels for my research lab, as well as the research products produced by students and myself.

Searching for New Antibacterial Compounds Against Staphylococcus aureus: A Computational Study on the Binding Between FtsZ and FtsA

Background: Staphylococcus aureus is a pathogen that has become resistant to different antibiotics, which makes it a threat to human health. Although the first penicillin-resistant strain appeared in 1945, nowadays, there are just a few alternatives to fight it. To circumvent this issue, novel approaches to develop drugs to target proteins of the bacteria cytoskeleton, essential for bacteria’s binary fission, are being developed. FtsZ and FtsA are two proteins that are key for the initial stages of binary fission. On one side, FtsZ forms a polymeric circular structure called the Z ring; meanwhile, FtsA binds to the cell membrane and then anchors to the Z ring. According to the literature, this interaction occurs within the C-terminus domain of FtsZ, which is mainly disordered. Objective: In this work, we studied the binding of FtsZ to FtsA using computational chemistry tools to identify the interactions between the two proteins to further use this information for the search of potential protein-protein binding inhibitors (PPBIs). Methods: We made a bioinformatic analysis to obtain a representative sequence of FtsZ and FtsA of Staphylococcus aureus. With this information, we built homology models of the FtsZ to carry out the molecular docking with the FtsA. Furthermore, alanine scanning was conducted to identify the key residues forming the FtsZ–FtsA complex. Finally, we used this information to generate a pharmacophore model to carry out a virtual screening approach. Results: We identified the key residues forming the FtsZ-FtsA complex as well as five molecules with high potential as PPBIs.

Challenges in Mathematics Arising from Theoretical and Computational Chemistry

The combination of theoretical chemistry and mathematics represents a significant advancement in science since it has yielded insights into the enigmas surrounding atoms and molecules. The advancement of technology has greatly benefited drug development, leading to significant advancements in both medicine and materials research. This summary discusses innovative methodologies and computational challenges in the expansive domain of theoretical and computational chemistry. Despite the computational challenges associated with the Schrodinger equation and Density Functional Theory (DFT), quantum mechanics offers a basic understanding of the behaviour of molecules and atoms. While MD simulations may not handle long-range interactions and complex configuration spaces, they nonetheless excel at capturing various time scales of molecular behaviour. Nevertheless, the process of transforming abstract notions into computer models for the purpose of solving MD simulations continues to be difficult; statistical mechanics offers a theoretical framework for understanding MD simulations. Data-driven methodologies and machine learning have significantly transformed the perspective of computational chemistry by enabling the observation and comprehension of very complex chemical interactions. We must also address concerns about interpretability, reliability in limited data streams, and system transferability. These challenges expedite the advancement of novel materials, product designs, and pharmaceuticals by leveraging machine learning and integrating it with specialised domain expertise. We used available data from many reputable databases, spanning the time period from 2010 to 2024. It is imperative to prioritise the advancement of multiscale modelling techniques and hybrid quantum-classical tools in order to achieve sufficient mastery over chemical phenomena. Another potential benefit is the use of novel mathematical models to uncover previously unknown patterns and connections in genetic data. The most effective way to tackle mathematical problems is by employing a straightforward approach and engaging in collaboration with experts from other fields. By adopting this approach, we can effectively address significant societal issues and propel advancements in chemistry. Keywords: mathematics, computational, chemistry, theoretical, challenges, quantum mechanics

Molecular quantum chemical data sets and databases for machine learning potentials

Abstract The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials.
In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs. This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis.
Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

Difluorocarbene Generation via a Spin-Forbidden Excitation under Visible Light Irradiation.

The generation of difluorocarbene from difluoromethane bis(sulfonium ylide) 1 through spin-forbidden excitation under irradiation with 450 nm blue light was reported. The formation of difluorocarbene was confirmed by its reaction with styrene derivatives for the generation of difluorocyclopropanation and insertion into RX-H bonds (X = O, S) for the generation of RXCF2H. The spin-forbidden excitation mechanism for the formation of difluorocarbene from difluoromethane bis(sulfonium ylide) was supported by spectroscopic and kinetic studies as well as computational chemistry. The homolytic cleavage of two S-C bonds in compound 1 under irradiation was confirmed by time-resolved EPR spectroscopic studies of the precursor's free-radical-capturing reaction, as well as the isolation of the dimer of dimethyl (phenylthiol)malonyl radical. Further studies showed that the homolytic cleavage process occurred asynchronously in the solvent cage based on the isotope-labeled scrambling experiments and DFT calculations.

Screening and design of PARP12 inhibitors from traditional Chinese medicine small molecules using computational modeling and simulation

The poly (ADP-ribose) polymerase (PARP) family of enzymes plays a pivotal role in orchestrating a multitude of cellular processes, including DNA repair mechanisms, transcriptional regulation, and modulation of immune responses. Within this family, PARP12 emerges as a noteworthy candidate for targeted cancer therapeutics. Consequently, this investigation endeavors to screen and design potential PARP12 inhibitors derived from traditional Chinese medicinal compounds by employing sophisticated molecular modeling and computational medicinal chemistry approaches. The compound RBN2397 is utilized as a benchmark, and the binding efficacies of the newly identified small molecules are assessed against a spectrum of criteria, encompassing molecular interactions, binding free energy, and extensive post-simulation analyses. The outcomes demonstrated that the identified small molecules, specifically tcm8650 and its derivative XC-1, possess remarkable binding affinities and exhibit reduced binding free energies compared to RBN2397. The molecular docking and interaction profiles of these compounds were also comprehensively scrutinized. Moreover, ADMET profiling meticulously evaluated the pharmacokinetic profiles and physicochemical characteristics of these promising molecules and their projected human physiological impact. These computational studies indicated their potential therapeutic applicability and predicted acceptable safety profile, advocating their further exploration as viable candidates in cancer treatment.

Azole Derivatives: Cutting‐Edge Agents in Cancer Therapy

AbstractMonocyclic 5‐membered heterocycles including imidazoles, thiazoles, oxazoles, and their related compounds have gained significant attention in medicinal chemistry because of their potent anticancerous activity. These small heterocyclic molecules possess versatile properties, including biological activity, absorption, distribution, metabolism, excretion, and chemical diversity that give them immense potential as anticancer agents. It is also a fact that inherent characteristic of azoles to combine with many biological molecules through hydrogen bond, stacking, and hydrophobic interaction makes them effective against almost all cancer types. In the present paper the author discusses the way which is connected with chemical structure of monocyclic azoles and their anticancer activity namely the ability of these compounds to intercalate with DNA, to inhibit some enzymes and to interfere cellular signaling pathways. Interestingly, several azole derivatives have been seen to be effective in preclinical efficacy studies as well as in clinical trials and are considered to be potent in overcoming the problem of resistance and side effects of the common anticancer agents. As the synthetic chemistry progresses, the structural system of the azoles has diversified and development in the pharmacology has become more specific. This has helped in enhancing the formation of new molecules in the azole class with improved selectivity and efficacy. Furthermore, the comprehensive review explains how computational chemistry and structure‐activity relationship (SAR) approaches are applied to the design of future‐generation azole compounds. In light of these facts, this article is designed to give a broad overview of the current state of monocyclic azole‐based anticancer agents in an attempt to further assert its therapeutic promise and spur further attempts at infusing the said agents into the cancer therapeutics fray. The discoveries made in this study may allow the development the radical different therapeutic approaches, which could lead to improved and targeted treatment of cancer.

Physics Models and Machine Learning in Molecular Dynamics

Abstract. Tackling the complexity of particle interactions, this investigation introduces a unified computational methodology employing Moldy, Gnuplot, and Visual Molecular Dynamics (VMD). Inspired by the n-body problem's persistent intrigue, specifically the three-body dynamics within molecular systems, we leverage Molecular Dynamics (MD) simulations to forecast and scrutinize the nuanced behavior of atomic and molecular entities. Moldy, a flexible MD software, facilitated the simulation of particle trajectories and interactions across diverse scenarios, with meticulous documentation of the resulting data. For visual analytics and insight extraction, Gnuplot crafted detailed plots depicting kinetic and potential energies alongside other thermodynamic metrics over temporal scales. The integration of VMD culminated in our analysis by vividly portraying the molecular motions, enhancing comprehension of emergent patterns. This study not only endorses Moldy's precision in MD simulations but also highlights the potent alliance among simulation, analysis, and visualization in elucidating intricate particle interactions. In summary, our work underscores the efficacy of the Moldy-Gnuplot-VMD triad as a formidable resource for scientists engaged in molecular motion prediction and analysis, paving fresh paths in computational physics and chemistry research.

Verification of the interaction between human bitter taste receptor T2R46 and polyphenols; Computational chemistry approach

Recent studies have indicated that the activation of bitter taste receptors (T2R) expressed in gastrointestinal secretory cells has a regulatory effect on the secretion of gastrointestinal hormones. Polyphenols are known to be ingested at a daily intake of 5 g or more and commonly have a bitter taste. Consequently, the interaction between the bitter taste receptor T2R46 and 490 polyphenols was investigated using in silico simulation techniques. It was demonstrated that W883.32 and E2657.39 play a pivotal role in the recognition of polyphenols and known ligands by T2R46, with frequent interactions observed, particularly with flavonoids. The results of the quantitative structure-activity relationship (QSAR) analysis demonstrated a high degree of correlation (R2=0.9359) between polyphenols and T2R46 in a model that incorporated molecular interaction field regions and branching scales. Furthermore, known ligands were also found to fit this model (R2=0.9155). These findings suggest that polyphenols may act as T2R46 ligands.

Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning.

Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds provides a virtually unlimited design space; however, exploring this space through Edisonian trial-and-error approaches is costly and time-consuming. In this work, we develop a new framework, SPARKLE, that combines computational chemistry, molecular generation, and machine learning to achieve zero-shot predictions of OEMs that simultaneously balance reward (specific energy), risk (solubility), and cost (synthesizability). We demonstrate that SPARKLE significantly outperforms alternative black-box machine learning algorithms on interpolation and extrapolation tasks. By deploying SPARKLE over a design space of more than 670,000 organic compounds, we identified ≈5000 novel OEM candidates. Twenty-seven of them were synthesized and fabricated into coin-cell batteries for experimental testing. Among SPARKLE-discovered OEMs, 62.9% exceeded benchmark performance metrics, representing a 3-fold improvement over OEMs selected by human intuition alone (20.8% based on six years of prior lab experience). The top-performing OEMs among the 27 candidates exhibit specific energy and cycling stability that surpass the state-of-the-art while being synthesizable at a fraction of the cost.

Inhibitory Potential of the Truncated Isoforms on Glutamate Transporter Oligomerization Identified by Computational Analysis of Gene-Centric Isoform Maps.

Glutamate transporters play a key role in central nervous system physiology by maintaining excitatory neurotransmitter homeostasis. Biological assemblies of the transporters, consisting of cyclic homotrimers, emerge as a crucial aspect of glutamate transporter modulation. Hence targeting heteromerization promises an effective approach for modulator design. On the other hand, the dynamic nature of transcription allows for the generation of transporter isoforms in structurally distinct manners. The potential isoforms were identified through the analysis of computationally generated gene-centric isoform maps. The conserved features of isoform sequences were revealed by computational chemistry methods and subsequent structural analysis of AlphaFold2 predictions. Truncated isoforms were further subjected to a wide range of docking analyses, 50ns molecular dynamics simulations, and evolutionary coupling analyses. Energetic landscapes of isoform-canonical transporter complexes suggested an inhibitory potential of truncated isoforms on glutamate transporter bio-assembly. Moreover, isoforms that mimic the trimerization domain (in particular, TM2 helices) exhibited stronger interactions with canonical transporters, underscoring the role of transmembrane helices in isoform interactions. Additionally, self-assembly dynamics observed in truncated isoforms mimicking canonical TM5 helices indicate a potential protective role against unwanted interactions with canonical transporters. Our computational studies on glutamate transporters offer insights into the roles of alternative splicing on protein interactions and identifies potential drug targets for physiological or pathological processes.

Design, microwave synthesis, characterization and antimicrobial activity of imidazolone derivatives

Overuse and misuse of antibiotics create superbugs as drug-resistant bacteria. The vital strategy is developing new antibiotics, and one of the vital compounds that proved activity as antimicrobials is imidazoles. So, we designed and synthesized a series of imidazolones via microwave radiation in excellent yield and evaluated them as antimicrobial agents with docking studies to promote and boost their activity. The results obtained from spectroscopic analyses approved the chemical structures of new compounds. The biological antimicrobial activity results was found that these compounds exert an excellent broad spectrum antimicrobial activity against many Gram-positive and Gram-negative bacteria, as well as different fungal strain including some highly human pathogenic microorganisms.Compounds 5a-5c are active against Gram-positive bacteria and compounds 5d-5f against Gram-negative bacteria, together with Candida albicans as fungi. Molecular Docking study on the active compounds 5e, 5f showed ring-stacking interactions between them as ligands and related proteins, which are compatible with other results. Gaussian 09 program is used as computational chemistry software to confirm the obtained experimental results.

MOFSynth: A Computational Tool toward Synthetic Likelihood Predictions of MOFs.

In the past decade, high-throughput computational studies of materials have increased significantly mainly due to advances in computer capabilities and have attracted a great deal of interest. In the field of metal-organic frameworks (MOFs), over a million hypothetical MOFs have been designed in silico, yet only a small fraction of these have been synthesized. For validating the computational-hypothetical results and accelerating the progress in the field, there is a pressing need for distinguishing MOFs that are more likely to be synthesized for real-life applications. This study presents a comprehensive investigation into the synthesizability likelihood of MOFs, utilizing a novel computational approach based on the disparities in energy and geometry between the linker conformation within the MOF structure and its isolated, free-gas state since both of these have been proven to be critical factors influencing MOF synthesis. Our user-friendly tool streamlines synthesizability evaluation, requiring minimal expertise in computational chemistry. By deconstructing over 40,000 MOFs from databases, including QMOF, CoRE MOF, and ToBaCCo, we analyze key parameters defining the linker strain within the MOF unit cell. Our results indicate that QMOF and CoRE MOF contain more promising candidates for synthesis, while ToBaCCo exhibits a relatively poor synthesizability likelihood due to unoptimized materials. Through extensive analysis, we identify optimal linker candidates for highly synthesizable MOFs. Consistent trends in energy distribution across databases that are confirmed by high Pearson and Spearman coefficients suggest the potential for omitting optimization calculations, significantly reducing computational costs. This study underscores the importance of linker deformation and energy disparities and enhances our understanding of synthetic accessibility in MOF research, offering valuable insights for future advancements in the field.

The Paradox of Global Multireference Diagnostics.

Modern computational chemistry methods are a useful tool for modeling many chemical systems, but they are challenged by multireference species (e.g., transition metals). A variety of diagnostics have been formulated to identify such cases. They are typically developed by analyzing multireference characters of small molecules, and many provide an average picture of the entire system. We caution the use of such diagnostics for large systems because large systems may include parts with varying degrees of multireference characters. Specifically, a small but highly multireference component may yield a large error in absolute terms, which may be masked in an average value over the entire molecule. As the calculation of molecular relative energies often concerns errors in absolute terms, such a false sense of safety may be detrimental. A prospective means to tackle this challenge is to use fractional occupation density to identify potentially problematic components in a system and then examine this moiety with higher-level computations on appropriately constructed smaller models.

OpenQP: A Quantum Chemical Platform Featuring MRSF-TDDFT with an Emphasis on Open-Source Ecosystem.

The OpenQP (Open Quantum Platform) is a new open-source quantum chemistry library developed to tackle sustainability and interoperability challenges in the field of computational chemistry. OpenQP provides various popular quantum chemical theories as autonomous modules such as energy and gradient calculations of HF, DFT, TDDFT, SF-TDDFT, and MRSF-TDDFT, thereby allowing easy interconnection with third-party software. A scientifically notable feature is the innovative mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) and its customized exchange-correlation functionals such as the DTCAM series of VAEE, XI, XIV, AEE, and VEE, which significantly expand the applicability scope of DFT and TDDFT. OpenQP also supports parallel execution and is optimized with BLAS and LAPACK for high performance. Future enhancements such as extended Koopman's theorem (EKT)-MRSF-TDDFT and spin-orbit coupling (SOC)-MRSF-TDDFT will further expand OpenQP's capabilities. Additionally, a Python wrapper PyOQP is provided that performs tasks such as geometry optimization, conical intersection searches, and nonadiabatic coupling calculations, among others, by prototyping the modules of the OpenQP library in combination with third-party libraries. Overall, OpenQP aligns with modern trends in high-performance scientific software development by offering flexible prototyping and operation while retaining the performance benefits of compiled languages like Fortran and C. They enhance the sustainability and interoperability of quantum chemical software, making OpenQP a crucial platform for accelerating the development of advanced quantum theories like MRSF-TDDFT.