Journal of Computational Chemistry最新文献

The orbital ordering (OO) resulting from the partial occupancy of the $��{�t�}_{�2�g�}�$ $��d�$ subshell of the transition metals in KBF$��{}_{�3�}�$ (B = Sc, Ti, Ffe, Co) perovskites, and the many possible patterns arising from the coupling between the B sites, have been investigated at the quantum mechanical level ($��\text{all electron}�$ Gaussian type basis set, B3LYP hybrid functional) in a 40 atoms supercell. The numerous patterns are distributed into 162 classes of equivalent configurations. For each fluoroperovskite, one representative per class has been calculated. The four compounds behave similarly: an identical dependence of the energy and volume (or cell parameters) on the OO pattern; the spanned energy interval is small (1 to 2 mE$��{}_{�h�}�$ per formula unit), suggesting that most of the configurations are occupied at room and even at low temperature. A linear model, taking into account the relative orbital order in contiguous sites, reproduces the energy order in the full set for each compound, suggesting that it could be used for studying OO in larger supercells.

The outcomes of DFT-based calculations are here reported to assess the applicability of two synthesized polypyridyl Ru(II) complexes, bearing ethynyl nile red (NR) on a bpy ligand, and two analogues, bearing modified-NR, in photodynamic therapy. The absorption spectra, together with the non-radiative rate constants for the S1 – Tn intersystem crossing transitions, have been computed for this purpose. Calculations evidence that the structural modification on the chromophore destabilizes the HOMO of the complexes thus reducing the H-L gap and, consequently, red shifting the maximum absorption wavelength within the therapeutic window, up to 620 nm. Moreover, the favored ISC process from the bright state involves the triplet state closest in energy, which is also characterized by the highest SOC value and by the involvement of the whole bpy ligand bearing the chromophore in delocalising the unpaired electrons. These outcomes show that the photophysical behavior of the complexes is dominated by the chromophore.

In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2 kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

Quantum computers are expected to outperform classical computers for specific problems in quantum chemistry. Such calculations remain expensive, but costs can be lowered through the partition of the molecular system. In the present study, partition was achieved with range-separated density functional theory (RS-DFT). The use of RS-DFT reduces both the basis set size and the active space size dependence of the ground state energy in comparison with the use of wave function theory (WFT) alone. The utilization of pair natural orbitals (PNOs) in place of canonical molecular orbitals (MOs) results in more compact qubit Hamiltonians. To test this strategy, a basis-set independent framework, known as multiresolution analysis (MRA), was employed to generate PNOs. Tests were conducted with the variational quantum eigensolver for a number of molecules. The results show that the proposed approach reduces the number of qubits needed to reach a target energy accuracy.

A computational methodology, founded on chemical concepts, is presented for interpreting the role of nuclear motion in the electron transport through single-molecule junctions (SMJ) using many-electron ab initio quantum chemical calculations. Within this approach the many-electron states of the system, computed at the SOS-ADC(2) level, are followed along the individual normal modes of the encapsulated molecules. The inspection of the changes in the partial charge distribution of the many-electron states allows the quantification of the electron transport and the estimation of transmission probabilities. This analysis improves the understanding of the relationship between internal motions and electron transport. Two SMJ model systems are studied for validation purposes, constructed from a conductor (BDA, benzene-1,4-diamine) and an insulator molecule (DABCO, 1,4-diazabicyclo[2.2.2]octane). The trends of the resulting transmission probabilities are in agreement with the experimental observations, demonstrating the capability of the approach to distinguish between conductor and insulator type systems, thereby offering a straightforward and cost-effective tool for such classifications via quantum chemical calculations.

In symmetry-adapted perturbation theory (SAPT), accurate calculations on non-covalent interaction (NCI) for large complexes with more than 50 atoms are time-consuming using large basis sets. More efficient ones with smaller basis sets usually result in poor prediction in terms of dispersion and overall energies. In this study, we propose two composite methods with baseline calculated at SAPT2/aug-cc-pVDZ and SAPT2/aug-cc-pVTZ with dispersion term corrected at SAPT2+ level using bond functions and smaller basis set with $��\delta �$MP2 corrections respectively. Benchmark results on representative NCI data sets, such as S22, S66, and so forth, show significant improvements on the accuracy compared to the original SAPT Silver standard and comparable to SAPT Gold standard in some cases with much less computational cost.

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

This article presents a personal view of selected Nobel Prizes in Chemistry. It is neither a comprehensive account of the science for which the Prizes were awarded, nor does it offer complete biographies of a remarkable group of scientists. It attempts to show the links between the prizes and chronicles the author's contacts with leading scientists over a span of almost six decades. I have used the official website of the Nobel Prizes¹ as my primary source of information and Wikipedia² as the secondary source. In a few cases, I have obtained information from reliable sources such as the Biographical Memoirs of the Fellows of the Royal Society.³ I cannot provide references for my personal reminiscences.

Some momentous discoveries and innovations can be connected to a particular moment in the history of humankind, whereas the emergence of other fields of human endeavor cannot be placed in time, nor associated with one individual or society. Computational chemistry is a prime example of the latter. It did not begin with one eureka moment, nor with a group of researchers, but rather evolved over several decades due to a myriad of factors, the two principal ones being scientific advances and technological innovations.

Experimental chemistry is primarily associated with the synthesis of molecules and materials or with reproducible measurements of observable properties, including the identification and quantification of chemical species. The fundamental basis of experimental chemistry was established in the 18th century by Antoine-Laurent de Lavoisier who was the first known person to record careful quantitative observations. The subsequent application of the scientific method over the next 250 years led to a remarkable list of achievements and established chemistry as a mature discipline. Given its relationship with the other natural sciences, chemistry is justifiably referred to as the central science.

Many chemical reactions have been known since antiquity; combustion and fermentation are classic examples. The earliest attempts to explain chemical phenomena lacked scientific rigor. A well-known example attributed to ancient Greek philosophers was the supposition that all substances are composed of four basic elements (fire, water, air, and earth). Attempts by philosophers in many societies to explain natural phenomena in terms of Empedocles' four-element theory eventually gave way to the atomic theory, introduced by John Dalton in 1808 and firmly established by the experiments of Ernest Rutherford in 1911.

The primary objective of theoretical and computational chemistry is to explain chemical phenomena involving atoms, molecules, and materials and to make predictions about the properties and transformations of matter. Theoretical and computational chemistry are inextricably linked, with the former providing a rigorous theoretical framework, while the la

A recent work (arXiv:2401.04685) has merged N-centered ensembles of neutral and charged electronic ground states with ensembles of neutral ground and excited states, thus providing a general and in-principle exact (so-called extended N-centered) ensemble density functional theory of neutral and charged electronic excitations. This formalism made it possible to revisit the concept of density-functional derivative discontinuity, in the particular case of single excitations from the highest occupied Kohn–Sham (KS) molecular orbital, without invoking the usual “asymptotic behavior of the density” argument. In this work, we address a broader class of excitations, with a particular focus on double excitations. An exact implementation of the theory is presented for the two-electron Hubbard dimer model. A thorough comparison of the true physical ground- and excited-state electronic structures with that of the fictitious ensemble density-functional KS system is also presented. Depending on the choice of the density-functional ensemble as well as the asymmetry of the dimer and the correlation strength, an inversion of states can be observed. In some other cases, the strong mixture of KS states within the true physical system makes the assignment “single excitation” or “double excitation” irrelevant.