Computational Chemistry Methods Research Articles

Working with benchmark datasets in the Cuby framework.

The development and benchmarking of computational chemistry methods rely on comparison with benchmark data. More and larger benchmark datasets are becoming available, and working efficiently with them is a necessity. The Cuby framework provides rich functionality for working with datasets, comes with many ready-to-use predefined benchmark sets, and interfaces with a wide range of computational chemistry software packages. Here, we review the tools Cuby provides for working with datasets and provide examples of more advanced workflows, such as handling large numbers of computations on high performance computing resources and reusing previously computed data. Cuby has also been extended recently to include two important benchmark databases, NCIAtlas and GMTKN55.

Mapping the application research on machine learning in the field of ionic liquids: A bibliometric analysis

The aim was to gain a deep understanding of the research status and application of machine learning in the field of ionic liquids, and to identify the research hotspots and frontiers. Co-occurrence analysis, co-citation analysis, key literature citation temporal analysis and emerging word analysis were used. The results show that the knowledge base of applying machine learning in the field of ionic liquids can be divided into three parts: fundamental properties and application research, thermodynamics and phase equilibrium research, and the combination of machine learning with computational chemistry methods. The research hotspots mainly include the prediction and optimization of properties, the prediction of phase behavior, and research on machine learning algorithms. The current research frontiers in applying machine learning in the field of ionic liquids include the prediction of ionic liquid performance, the structure-property relationships of ionic liquids, and the optimization and design of ionic liquid processes.

Discovery of an immunosuppressive functional metabolite from the insect-derived endophytic Aspergillus taichungensis SMU01

Three p-terphenyl metabolites (1–3), three indole-diterpenoids (4–6), an herbicide sesquiterpene (7), a flavonoid (8), and five other small molecules containing nitrogen (9–13) were isolated from the medicinal insect (Periplaneta americana)-derived endophytic Aspergillus taichungensis SMU01. Their chemical structures were elucidated on the basis of spectroscopic data and quantum chemical computational methods. Biological activity of these isolates in the differentiation of mouse CD4+ T cell subsets was evaluated. Importantly, metabolites 2 targeting JAK-STAT signaling pathway could hold potential benefits in maintaining peripheral immune homeostasis and alleviating the progression of autoimmune diseases.

Gold(Ⅰ) organometallics with alkynyl and N-heterocyclic carbene ligands and their medicinal and computational chemistry evaluation as prospective anticancer drugs

Gold(Ⅰ) organometallics of the type alkynylgold(Ⅰ)(NHC) (where NHC = N-heterocyclic carbene) 5a – 5f and 6a/b were prepared and evaluated as prospective anticancer agents by computational and medicinal chemistry methods. DFT calculations showed similar stability for the series, with differing dipole moments, depending on the substituents on the alkynyl and the NHC ligands. Studies in cancer cell lines indicated in general that the complexes with the largest dipole moments exhibited the highest antiproliferative activity in comparison with structurally closely related complexes. Thus, complexes 5f, (with 4-fluoroethynylbenzene as alkynyl precursor), and 6b, (with mestranol as alkynyl precursor) containing the same methoxy‑substituted NHC ligand showed in general the strongest biological activity. Protein binding studies confirmed a high affinity to albumin (around 90 %) and cellular uptake studies indicated an increased uptake of the most active complex 5f.

Understanding Defects in Amorphous Silicon with Million‐Atom Simulations and Machine Learning

AbstractThe structure of amorphous silicon (a‐Si) is widely thought of as a fourfold‐connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such “dangling‐bond” and “floating‐bond” defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a‐Si. We study an ultra‐large‐scale, quantum‐accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect‐related statistics to be obtained. We combine structural descriptors and machine‐learned atomic energies to develop a classification of the different types of defects in a‐Si. The results suggest a revision of the established floating‐bond model by showing that fivefold‐bonded atoms in a‐Si exhibit a wide range of local environments–analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

Exploring Structure–Toxicity Relationships in Nitrobenzene and Derivatives: A Multifaceted Biochemical Investigation Using 3D–QSPR, HQSPR, Molecular Docking, and MD Simulation

AbstractNitrobenzene derivatives present an environmental concern to aquatic organisms, as well as a potential adverse effects on human health due to their toxicity. Therefore, monitoring nitrobenzene contamination levels is critical to protect both the ecosystem and the public. This study aims to quantitatively correlate the toxicity of nitrobenzene derivatives to their chemical structures using computational chemistry methods such as 3D‐QSPR, HQSPR, Molecular Docking and MD simulation. Additionally, to investigate the type of interactions and stability of nitrobenzene derivatives with the PP2 protein involved in the Phloem protein 2 (PP2). From each computational method a prediction model was extracted HQSPR (Atomic, Bonds, Donor/Acceptor, 3D‐QSPR (Steric, Electrostatic, Hydrophobic, Acceptor), and used for the design of X1 and X2 new nitrobenzene derivatives, which showed remarkable stability and better interactions. This study contributes to the understanding of the nitrobenzene derivatives’ toxicity and offers materials towards the understanding of the mechanism of action of these substances in protein–ligand interactions.

A theoretical study on toluene oxidization by OH radical

Toluene, a prominent member of volatile organic compounds (VOCs), exerts a substantial adverse influence on both human life and the environment. In the context of advanced oxidation processes, the ·OH radical emerges as a highly efficient oxidant, pivotal in the elimination of VOCs. This study employs computational quantum chemistry methods (G4MP2//B3LYP/6-311++G(d,p)) to systematically investigate the degradation of toluene by ·OH radicals in an implicit solvent model, and validates the rationale of choosing a single-reference method using T1 diagnostics. Our results suggest three possible reaction mechanisms for the oxidation of toluene by ·OH: firstly, the phenyl ring undergoes a hydrogen abstraction reaction followed by direct combination with ·OH to form cresol; secondly, ·OH directly adds to the phenyl ring, leading to ring opening; thirdly, oxidation of sidechain to benzoic acid followed by further addition and ring opening. The last two oxidation pathways involve the ring opening of toluene via the addition of ·OH, significantly facilitating the process. Therefore, both pathways are considered feasible for the degradation of toluene. Subsequently, the UV-H2O2 system was designed to induce the formation of ·OH for toluene degradation and to identify the optimal reaction conditions. It was demonstrated that ·OH and 1O2 are the primary active species for degrading toluene, with their contribution ranking as ·OH > 1O2. The intermediates in the mixture solution after reactions were characterized using GC–MS, demonstrating the validity of theoretical predictions. A comparative study of the toluene consumption rate revealed an experimental comprehensive activation energy of 10.33 kJ/mol, which is consistent with the preliminary activation energies obtained via theoretical analysis of these three mechanisms (0.56 kJ/mol to 13.66 kJ/mol), indicating that this theoretical method can provide a theoretical basis for experimental studies on the oxidation of toluene by ·OH.

Exploring structure-property landscape of non-fullerene acceptors for organic solar cells.

We present a comprehensive analysis of the structure-property relationship in small molecule non-fullerene acceptors (NFAs) featuring an acceptor-donor-acceptor configuration employing state-of-the-art quantum chemical computational methods. Our focus lies in the strategic functionalization of halogen groups at the terminal positions of NFAs as an effective means to mitigate non-radiative voltage losses and augment photovoltaic and photophysical properties relevant to organic solar cells. Through photophysical studies, we observe a bathochromic shift in the visible region for all halogen-functionalized NFAs, except type-2, compared to the unmodified compound. Most of these functionalized compounds exhibit exciton binding energies below 0.3eV and ΔLUMO less than 0.3eV, indicating their potential as promising candidates for organic solar cells. Selected candidate structures undergo an analysis of charge transport properties using the semi-classical Marcus theory based on hopping transport formalism. Molecular dynamics simulations followed by charge transport simulations reveal an ambipolar nature of charge transport in the investigated NFAs, with equivalent hole and electron mobilities compared to the parent compound. Our findings underscore the crucial role of end-group functionalization in enhancing the photovoltaic and photophysical characteristics of NFAs, ultimately improving the overall performance of organic solar cells. This study advances our understanding of the structure-property relationships in NFAs and provides valuable insights into the design and optimization of organic solar cell materials.

Development and experimental validation of 3D QSAR models for the screening of thyroid peroxidase inhibitors using integrated methods of computational chemistry

The intricate network of glands and organs that makes up the endocrine system. Hormones are used to regulate and synchronize the nervous and physiological systems. The agents which perturbate an endocrine system are called endocrine disruptors and they can eventually affect cellular proliferation and differentiation in target tissues. A subclass of endocrine disruptors known as thyroid disruptors (TDs) or thyroid disrupting chemicals (TDCs) influence the hypothalamo-pituitary-thyroid axis or directly interfere with thyroid function by binding to thyroid hormone receptors. Thyroid hormone levels in circulation are now included in more test guidelines (OECD TG 441, 407, 408, 414, 421/422, 443/416). Although these might be adequate to recognize thyroid adversity, they are unable to explain the underlying mechanism of action. Thyroid peroxidase (TPO) and sodium iodide symporter (NIS), two proteins essential in the biosynthesis of thyroid hormones, are well-accepted molecular targets for inhibition. The screening of a large number of molecules using high throughput screening (HTS) requires a minimum quantity of sample, cost, and time consuming. Whereas 3-dimensional quantitative structure-activity relationship (3D-QSAR) analysis can screen the TDCs before synthesizing a compound. In the present study, the human TPO (hTPO) and NIS (hNIS) structures were modelled using homology modeling and the quality of the structures was validated satisfactorily using MD simulation for 100ns. Further, 190 human TPO inhibitors with IC50 were curated from Comptox and docked with the modelled structure of TPO using D238, H239 and D240 centric grid. The binding conformation of a molecule with low binding energy was used as a reference and the rest other molecules were aligned after generating the possible conformers. The activity-stratified partition was performed for aligned molecules and training set (139), test set (51) were defined. The machine learning models such as k Nearest Neighbor (kNN) and Random Forest (RF) models were built and validated using external experimental dataset containing 10 molecules. Among the 10 molecules, all 10 molecules were identified as TPO inhibitors and demonstrated 100 % accuracy qualitatively. To confirm the selective TPO inhibition all 10 molecules were docked with the modelled structure of hNIS and the results have demonstrated the selective TPO inhibition.

Exploring Geometrical, Electronic and Spectroscopic Properties of 2-Nitroimidazole-Based Radiopharmaceuticals via Computational Chemistry Methods.

Tumor hypoxia plays an important role in the clinical management and treatment planning of various cancers. The use of 2-nitroimidazole-based radiopharmaceuticals has been the most successful for positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging probes, offering noninvasive means to assess tumor hypoxia. In this study we performed detailed computational investigations of the most used compounds for PET imaging, focusing on those derived from 2-nitroimidazole: fluoromisonidazole (FMISO), fluoroazomycin arabinoside (FAZA), fluoroetanidazole (FETA), fluoroerythronitroimidazole (FETNIM) and 2-(2-nitroimidazol-1-yl)-N-(2,2,3,3,3-pentafluoropropyl)acetamide (EF5). Conformational analysis, structural parameters, vibrational IR and Raman properties (within both harmonic and anharmonic approximations), as well as the NMR shielding tensors and spin-spin coupling constants were obtained by density functional theory (DFT) calculations and then correlated with experimental findings, where available. Furthermore, time-dependent DFT computations reveal insight into the excited states of the compounds. Our results predict a significant change in the conformational landscape of most of the investigated compounds when transitioning from the gas phase to aqueous solution. According to computational data, the 2-nitroimidazole moiety determines to a large extent the spectroscopic properties of its derivatives. Due to the limited structural information available in the current literature for the investigated compounds, the findings presented herein deepen the current understanding of the electronic structures of these five radiopharmaceuticals.

Improved Approach for ab Initio Calculations of Rate Coefficients for Secondary Reactions in Acrylate Free-Radical Polymerization.

Secondary reactions in radical polymerization pose a challenge when creating kinetic models for predicting polymer structures. Despite the high impact of these reactions in the polymer structure, their effects are difficult to isolate and measure to produce kinetic data. To this end, we used solvation-corrected M06-2X/6-311+G(d,p) ab initio calculations to predict a complete and consistent data set of intrinsic rate coefficients of the secondary reactions in acrylate radical polymerization, including backbiting, β-scission, radical migration, macromonomer propagation, mid-chain radical propagation, chain transfer to monomer and chain transfer to polymer. Two new approaches towards computationally predicting rate coefficients for secondary reactions are proposed: (i) explicit accounting for all possible enantiomers for reactions involving optically active centers; (ii) imposing reduced flexibility if the reaction center is in the middle of the polymer chain. The accuracy and reliability of the ab initio predictions were benchmarked against experimental data via kinetic Monte Carlo simulations under three sufficiently different experimental conditions: a high-frequency modulated polymerization process in the transient regime, a low-frequency modulated process in the sliding regime at both low and high temperatures and a degradation process in the absence of free monomers. The complete and consistent ab initio data set compiled in this work predicts a good agreement when benchmarked via kMC simulations against experimental data, which is a technique never used before for computational chemistry. The simulation results show that these two newly proposed approaches are promising for bridging the gap between experimental and computational chemistry methods in polymer reaction engineering.

Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.

The structure of amorphous silicon (a-Si) is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a-Si. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned atomic energies to develop a classification of the different types of defects in a-Si. The results suggest a revision of the established floating-bond model by showing that fivefold-bonded atoms in a-Si exhibit a wide range of local environments-analogous to fivefold centers in coordination chemistry. Furthermore, it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for understanding defects in disordered materials beyond silicon alone.

DFT investigation of coupling constant anomalies in substituted β-lactams.

β-lactams are a chemically diverse group of molecules with a wide range of biological activities. Having recently observed curious trends in 2JHH coupling values in studies on this structural class, we sought to obtain a more comprehensive understanding of these diagnostic NMR parameters, specifically interrogating 1JCH, 2JCH, and 2JHH, to differentiate 3- and 4-monosubstituted β-lactams. Further investigation using computational chemistry methods was employed to explore the geometric and electronic origins for the observed and calculated differences between the two substitution patterns.

A Novel One-Pot Three-Component Approach to Orthoaminocarbonitrile Tetrahydronaphthalenes Using Triethylamine (Et_3N) as a Highly Efficient and Homogeneous Catalyst Under Mild Conditions and Investigating Its Anti-cancer Properties Through Molecular Docking Studies and Calculations

An efficient and environmentally friendly method for the one-pot synthesis of ortho-aminocarbonitrile tetrahydronaphthalenes has been developed in the presence of triethylamine (Et3N) as a homogeneous catalyst. The multicomponent reactions of benzaldehydes, cyclohexanone and malononitrile were carried out under mild conditions to obtain some ortho-aminocarbonitrile tetrahydronaphthalene derivatives. A broad range of structurally diverse benzaldehydes were applied successfully, and corresponding products (4A-L) were obtained in good to excellent yields (87-98%) in very short times (10-25 minutes). The present approach provides several advantages including simple workup, high yields, very mild reaction conditions, short reaction times, little catalyst loading and not requiring specialized equipment. Furthermore, with the help of computational chemistry and drug design methods, the anti-cancer properties of these compounds were studied and investigated. All the synthesized compounds bind to an agonist at the active site of the 3A8P protein, which leads to the inactivation of this protein and produces beneficial effects during cancer treatment. In synthesized compounds, the ligands establish hydrogen bonds with leucine A:728 residues through nitrogen, which has a very special and vital role in biological sciences and pharmaceutical connections. In this study, it was found that these compounds have the potential to become an oral anti-cancer drug.

Synthesis of 1, 2-Disubstituted Benzimidazoles at Ambient Temperature Catalyzed by 1-Methylimidazolium Tetraflouroborate ([Hmim] BF_4) and Investigating Their Anti-ovarian Cancer Properties Through Molecular Docking Studies and Calculations

An environmentally benign method for the synthesis of 1, 2-disubstituted benzimidazoles by the reaction of aromatic aldehydes and o-phenylenediamines (OPD) in the presence of 1-methylimidazolium tetraflouroborate ([Hmim] BF4) at ambient temperature under green conditions is described. A broad range of structurally diverse benzaldehydes were applied successfully, and corresponding products were obtained in good to excellent yields in very short times. All products were identified by the melting points, 1H and 13C NMR techniques. Furthermore, with the help of computational chemistry and drug design methods, the anti-ovarian cancer properties of these compounds were studied and investigated. All the synthesized compounds bind to an agonist at the active site of the 6LAD protein, which leads to the inactivation of this protein and produces beneficial effects during ovarian cancer treatment. In this study, it was found that these compounds have the potential to become an oral anti-cancer drug.

Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6.

Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC90), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.

Non-Hermitian molecular dynamics simulations of exciton-polaritons in lossy cavities.

The observation that materials can change their properties when placed inside or near an optical resonator has sparked a fervid interest in understanding the effects of strong light-matter coupling on molecular dynamics, and several approaches have been proposed to extend the methods of computational chemistry into this regime. Whereas the majority of these approaches have focused on modeling a single molecule coupled to a single cavity mode, changes to chemistry have so far only been observed experimentally when very many molecules are coupled collectively to multiple modes with short lifetimes. While atomistic simulations of many molecules coupled to multiple cavity modes have been performed with semi-classical molecular dynamics, an explicit description of cavity losses has so far been restricted to simulations in which only a very few molecular degrees of freedom were considered. Here, we have implemented an effective non-Hermitian Hamiltonian to explicitly treat cavity losses in large-scale semi-classical molecular dynamics simulations of organic polaritons and used it to perform both mean-field and surface hopping simulations of polariton relaxation, propagation, and energy transfer.

Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.

Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature. These high-throughput electronic structure calculations also incur a substantial computational cost. With the emergence of big-data initiatives, cost-effective, data-driven methods have gained traction, although their usability is highly contingent on the degree of data quality and sparsity. In this study, we present a workflow that employs deep residual convolutional neural networks (DR-CNN) and gradient boosting feature selection to predict peak optical absorption wavelengths (λmax) exclusively from SMILES representations of dye molecules and solvents; one would normally measure λmax using UV-vis absorption spectroscopy. We use a multifidelity modeling approach, integrating 34,893 DFT calculations and 26,395 experimentally derived λmax data, to deliver more accurate predictions via a Bayesian-optimized gradient boosting machine. Our approach is benchmarked against the state of the art that is reported in the scientific literature; results demonstrate that learnt representations via a DR-CNN workflow that is integrated with other machine learning methods can accelerate the design of molecules for specific optical characteristics.

THE ROLE OF QUANTUM CHEMICAL CALCULATIONS IN CATALYSIS: A REVIEW

The article talks about the high role of quantum chemical research methods in such a promising direction as the catalytic oxidation of saturated hydrocarbons, especially in the study of transition states of molecules. Theoretical analysis is based on research in catalysis and computational chemistry. Computational chemistry is an effective tool that allows one to calculate the structure and physical properties of catalysts, as well as the structure and energy of the activated complex. In this review, special attention is paid to identifying a sufficiently accurate and, at the same time, less energy-consuming method for determining the structure of the transition state. The most popular methods of quantum chemical calculations are presented, among which a special role is given to density functional theory (DFT), functionals B3LYP, OLYP, PBE and basis sets 6-31G, LANL2DZ. The use of machine learning by some authors, along with quantum chemical computing, to identify catalysts for activation and functionalization of C-H bonds has been mentioned. The advantages and disadvantages of computational chemistry for heterogeneous catalysis are discussed. The results of studies of catalysts predicated on transition metal complexes are summarized. It is underlined that nanocomposites based on transition metals attract the attention of researchers due to their d-orbitals, which are responsible for the reactivity when activating C-H bonds. The review also indicates plans for conducting quantum chemical calculations to study the catalytic processes of selective oxidation of n-alkanes using tran-sition metal nanocomposites as catalysts. This review will be useful for future applications of computational chemistry methods for the development of new catalysts and the study of chemical reaction mechanisms

Comprehensive analysis of DFT-3C methods with B3LYP and experimental data to model optoelectronic properties of tetracene

Modern computational chemistry methods aid experimentalists in scrutinizing molecular systems for organic optoelectronic device fabrication. They are also helpful for device engineering, such as choosing metals for contact fabrication without sophisticated instrumentation. The B3LYP functional with 6-31G(d,p) or 6-311G(d,p) basis set is a widely used method in this regard. Recently, many upgraded methods, such as the three-fold corrected composite scheme (3C), have been reported to offer cost-effectiveness and better accuracy than the B3LYP. This is particularly interesting from a materials engineering point of view as these methods offer the possibility of rapidly screening the materials prior to the experimental analysis. In order to evaluate their suitability towards organic optoelectronic systems, in this work, we have used three composite schemes, namely B97-3C, PBEh-3C and HF-3C, along with B3LYP and the experimental data. The well-studied tetracene system is considered for the analysis, and different optoelectronic properties such as bandgap, reorganization energy, and absorption maximum are calculated theoretically and compared with the experimental data. The results indicate that though slightly expensive, B3LYP performs better than the 3C methods. Of the three, only PBEh-3C performs on par with B3LYP in accuracy and time. A note of caution is that these results should be inferred as this level of theory is adequate for the screening and modeling optoelectronic systems rather than a potential computational chemistry method.