Quantum Chemical Descriptors Research Articles

New iron(III), cobalt(II), nickel(II), copper(II), zinc(II) mixed-ligand complexes: Synthesis, structural, DFT, molecular docking and antimicrobial analysis

The present framework has as its goal the design and synthesis and characterization of new mononuclear 1:1:1 (M:L:Q) mixed-ligand complexes, including FeLQ, CoLQ, NiLQ, CuLQ, and ZnLQ. The "L," is the 4-[(4-oxo-4,5-dihydro-1,3-thiazol-2-yl)hydrazono]methylphenyl 4-methylbenzenesulfonate, while the "Q," is the 8-hydroxy quinoline. According to the findings, L and Q ligands each play the role of a neutral bi-dentate NN and a monobasic bi-dentate ON ligand, respectively. The findings demonstrated an octahedral shape. The density functional theory (DFT) technique was employed, and the quantum chemical descriptors were assessed, to optimize the molecular structure of the compounds. An in vitro investigation was carried out to investigate the antibacterial and antifungal activities of the compounds. According to the findings, the activity of metal complexes as potential candidates for use as antibiotics and antifungals is much greater than that of their free ligands. The in-silico inhibition of the 1fj4 protein was investigated using molecular docking. ZnLQ complex was the one that inhibited the 1fj4 protein with the greatest degree of success. The fact that this is the case lends credence to the notion that these compounds have the potential to function as launchpads for the development of new classes of antibiotics.
 KEY WORDS: Metal complexes, Schiff-base, Antimicrobial, DFT, Molecular docking
 Bull. Chem. Soc. Ethiop. 2024, 38(1), 147-166. DOI: https://dx.doi.org/10.4314/bcse.v38i1.12

Investigating haloacetic acids - human serum albumin interactions: A comprehensive approach using multi-spectroscopy, DFT calculations, and molecular docking

Haloacetic acid (HAAs) has been shown in studies to be teratogenic and mutagenic. The toxicity of HAAs to macromolecules and their mechanism of action are still unclear. To further explore the potential impact of HAAs on organisms, this study used multi-spectroscopy, density functional theory (DFT), and molecular docking to analyze the characteristics and mechanisms of five typical HAAs interactions with HSA.Multispectral analysis indicates that the binding energies of the interactions between the five HAAs and HSA are weaker than other HSA-ligand complex systems. The order of binding constants at 298 K was as follows: DBAA (458.14 L mol−1) > TCAA (374.71 L mol−1) > DCAA (242.89 L mol−1) > MBAA (140.73 L mol−1) > MCAA (102.05 L mol−1). The quenching processes of the interactions between the five HAAs and HSA belong to static quenching, an exothermic reaction process. Hydrogen bonds and van der Waals forces are the main driving forces in the formation process of the HSA-HAAs complex.Molecular docking results indicate that TCAA, DBAA, and DCAA are more likely to bind with HSA. Correlation analysis based on DFT calculations reveals the intrinsic relationship between the chemical structural features of HAAs and the binding performance of HSA-HAAs. For the first time, the research discovered that among the quantum chemical descriptors, the highest ALIE maximum, the highest ESP maximum, and the maximum local electrophilic index f−(r) all correlate well with the HSA-HAAs binding constants. Their correlation coefficients are 0.83, 0.94, and -0.56, respectively, which can effectively predict the binding performance of HAAs with HSA.In-vitro toxicity experiments on LO2 cells show that within the concentration range of 0.002–10 mmol·L−1, MBAA, MCAA, and DBAA exhibit higher toxicity to normal human liver cells. In contrast, DCAA and TCAA show low or no toxicity.Through comprehensive research combining experimental and theoretical analysis, the mechanism of HSA-HAAs has been deeply revealed. This holds significant scientific value for assessing the environmental risks of HAAs and understanding their impact on biological activities.

New insight into biodegradation mechanism of phenylurea herbicides by cytochrome P450 enzymes: Successive N-demethylation mechanism

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure − activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

Potential energy surface, effect of solvents in molecular level, experimental spectra (FTIR, Raman, UV–visible & NMR), electronic, and dynamics simulation of isobavachalcone –Anti tuberculosis agent

The spectroscopic characteristics, molecular structure, and biological characteristics of the Isobavachalcone (IBC) structure have been studied using computational techniques utilizing the usual B3LYP/6-311++G (d,p). PED (Potential Energy Distribution) is used throughout process of assigning the fundamental wave-numbers of IBC. In this work, we have reported the correlation between experimental and computed examination of molecular configuration, FT:IR & Raman, UV–Visible absorption, and NMR (Nuclear Magnetic Resonance) spectrum of the title compound. Potential energy scan (PES) analysis of IBC was enhanced in the initial stage with same basis set. FMO (Frontier Molecular Orbital), MEP (Molecular Electrostatic Potential), local reactivity and quantum chemical descriptors has also been analyzed. Furthermore, drug-likeness and ADMET predictions of the present ligand shows good pharmacological activity. ELF (Electron Localised Function), LOL (Localised Orbital Locator) and RDG (Reduced Density Function) enacted for both the gas and solvents phases. A docking simulation performed with the 2FUM receptor using the Auto Dock software showed that the IBC compound has inhibitory activity against Mycobacterium tuberculosis disease. All our results suggest that the IBC molecule could have a great effect in the treatment of tuberculosis disease. MD simulations been executed over 100 ns to predict the RMSD, RMSF, and the radius-of gyration analyses.

One-dimensional polymer of copper with salicylic acid and pyridine linkers: Synthesis, characterizations, solid state assembly investigation by hirshfeld surface analysis, and computational studies

A new one-dimensional polymeric copper complex [Cu(DNSA)py] with salicylic acid and pyridine was synthesized and characterized by many techniques, including FT-IR, UV–Visible, elemental, TGA/DSC, and single crystal XRD analysis. In the crystal structure, two 3,5-dinitrosalicylic ligands were chelated by the copper cation, in which one ligand was coordinated by the carbonyl O-atom and one of the carboxylate O-atoms, while another symmetry-related ligand was coordinated by the O-atoms of the carboxylate group to form distorted square pyramidal geometry. Thus, a one-dimensional polymer structure is formed, extending along the b-axis. The solid-state assembly was stabilized by various intermolecular interactions, as examined through Hirshfeld surface analysis. Voids analysis was carried out to predict the response of the crystal under external pressure. The most stable structure of the [Cu(DNSA)py] monomer resulted from density functional theory (DFT) using the B3LYP/def2-TZVPP. The 6–311G(d,p) level of theory demonstrated that the Cu atom adapts distorted square pyramidal geometry. The MEP surface and Mulliken charge distribution identified the Cu monomer atoms suitable for electrophilic and nucleophilic attacks. Frontier molecular orbital (FMO) analyses were conducted to anticipate the stability and chemical reactivity of the [Cu(DNSA)py] monomer and its ligand (DNSA). Furthermore, the quantum chemical descriptors of the title compound were obtained from EHOMO and ELUMO energy values.

Machine Learning Prediction of the Redox Activity of Quinones.

The redox properties of quinones underlie their unique characteristics as organic battery components that outperform the conventional inorganic ones. Furthermore, these redox properties could be precisely tuned by using different substituent groups. Machine learning and statistics, on the other hand, have proven to be very powerful approaches for the efficient in silico design of novel materials. Herein, we demonstrated the machine learning approach for the prediction of the redox activity of quinones that potentially can serve as organic battery components. For the needs of the present study, a database of small quinone-derived molecules was created. A large number of quantum chemical and chemometric descriptors were generated for each molecule and, subsequently, different statistical approaches were applied to select the descriptors that most prominently characterized the relationship between the structure and the redox potential. Various machine learning methods for the screening of prospective organic battery electrode materials were deployed to select the most trustworthy strategy for the machine learning-aided design of organic redox materials. It was found that Ridge regression models perform better than Regression decision trees and Decision tree-based ensemble algorithms.

Experimental and Computational Study on Inhibitory Effect and Adsorption Properties of N-Acetylcysteine Amino Acid in Acid Environment.

Potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) were applied to study the inhibitory effect of N-acetylcysteine (NAC) on corrosion inhibition of carbon steel in hydrochloric acid solution. N-acetylcysteine influenced the iron dissolution to a greater extent than the hydrogen evolution reaction acting as a mixed inhibitor, predominantly anodic. The charge transfer resistance (Rct) gradually increased with the inhibitor concentration. From both methods, the inhibition efficiency (IE) reached a value of 89 ± 1% and NAC adsorption followed the Temkin isotherm. The value of adsorption Gibbs energy (ΔGadso), around -35 kJ mol-1, indicated a spontaneous adsorption and mixed action mechanism, with NAC chemical adsorption prevailing over physical one. New data will be reported by the computational study, that was performed using the density functional theory (DFT) method in aqueous phase. Quantum chemical descriptors were determined by B3LYP theory level with 6-31G+(d) basis set. Metropolis Monte Carlo atomistic simulation was used to reveal the adsorption configuration and interactions between acetylcysteine molecules and the carbon steel surface. Theoretical results were consistent with the experimental data, showing that the inhibitor action mechanism consisted of mainly chemisorption of its molecules on the carbon steel surface accompanied by van der Waals forces and electrostatic interactions.

Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations.

Non-small cell lung cancer (NSCLC) is a widespread and often aggressive form of cancer affecting people worldwide. PIK3CA missense mutations play a significant role in the progression of growth factor signaling in cancer, making PI3Kα an important biological target for inhibition against NSCLC. Natural product molecules with PI3Kα inhibitory activity are promising therapeutic agents for the treatment of NSCLC, owing to their selectivity and potentially lower toxicity compared to synthetic compounds. To discover new natural product molecules, we integrated ligand-based virtual screening with structure-based virtual screening. We developed a multi-ligand pharmacophore hypothesis, validated it with 3D Field-based QSAR, and screened a Natural-Product-Based Library (ChemDiv) containing 3601 molecules. After initial screening, 137 hit molecules were generated and further screened using the extra precision (XP) Glide docking protocol. The best ten molecules were selected for free binding energy (ΔG) analysis using MMGBSA and ADME predictions. For further optimization, the top four hits were subjected to induced fit docking (IFD), quantum chemical descriptors analysis by Frontier Molecular Orbital (FMO) studies, and a 100ns molecular dynamics (MD) simulation. The compounds-S721-1955, CM4579-5085, S721-1963, and S721-1999-exhibited better results than the PI3Kα selective inhibitor alpelisib. In silico prediction analysis of S721-1955 and alpelisib revealed that the former exhibited superior selectivity theoretically, as evidenced by its higher affinity for the target protein. The selective natural product molecule identified in this study holds promise as a potential anti-cancer drug against NSCLC in the near future, but further in vitro and in vivo studies are necessary to confirm its efficacy.

A DFT study on 2X- imidazole derivatives (X = OH, NH2, and SH) as corrosion inhibitors on Cu surfaces: Tautomerism effect

Adsorption of different tautomers of 2-hydroxy, 2-amino, and 2-mercapto imidazole as corrosion inhibitors on the (1 1 1), (1 1 0), and (1 0 0) copper surfaces in parallel and perpendicular modes are compared and evaluated by the DFT approach. Among different tautomers, imidazole with thiol, thione, and 1 H-imidazole-2-amine in imine form exhibited the best inhibition efficiency. Also, the quantum chemical descriptors and interaction energy magnitudes, as well as PDOSs, were calculated and used to evaluation of the interaction of organic molecules with the copper surfaces. In addition, Cu (1 1 0) showed the best protection in combination with imidazole derivatives.

QSPR models for solvation enthalpy based on quantum chemical descriptors

After generation of quantum chemical descriptors calculated with the GEDIIS/GDIIS optimizer in Gaussian 09, five quantum chemical descriptors related to molecular polarizability, atomic charges, charge product between solvent and solute, were selected as the optimal descriptor subset for developing quantitative structure–property relationship (QSPR) models of 7215 enthalpies of solvation and vaporization. The random forest (RF) algorithm was used to develop the RF Model Ⅰ whose dataset division for training and testing was mainly based on the solvent types. The RF Model Ⅰ has the number (n) of enthalpies of solvation and vaporization being 3633, coefficient of determination R2 being 0.986, root mean square (rms) error being 2.598 kJ/mol for the training set and n = 3582, R2 = 0.933, rms = 5.501 kJ/mol for the test set. The RF Model Ⅵ based on Kennard-Stone algorithm for dataset selection possesses n = 4810, R2 = 0.987, rms = 2.550 kJ/mol (training set), n = 2405, R2 = 0.940, rms = 4.659 kJ/mol (test set). These statistical results are very accurate, compared with other QSPR models on enthalpies of solvation reported in the literature. Furthermore, the RF Model Ⅰ and Ⅵ based on large data sets can be used for predicting both of the solvation enthalpies and vaporization enthalpies.

Crystal Structure, quantum chemical analysis and apoptotic propensity of diaryl substituted α-aminophosphonates as selected C[sbnd]P bonded systems

Three component Kabachnik-Fields reaction under sonochemical conditions was utilized for synthesizing library of electronically diverse aryl substituted α-aminophosphonates as representative carbon phosphorus bonded systems. Single crystal X-ray structures of four novel α-aminophosphonate derivatives with comprehensive Hirshfeld surface analysis for highlighting influence of different functionalities over α-aminophosphonate motif on the crystal packing pattern and formation of molecular assemblies via supramolecular aggregation have been presented. Solid state absorption and photoluminescence studies corroborated electronic and crystal packing influences. The computational methodologies for evaluating quantum chemical descriptors of electronic properties of α-aminophosphonates were bench marked. The bioactivity potential of synthesized library of α- aminophosphonates was evaluated from their cytotoxic propensity towards human cancer lines in NCI-60 panel via SRB assay. The screened derivatives showed a range of cytotoxicity against cancer cell lines in NCI-60 panel. Compound 3 exhibited highest cell growth inhibition in prostate cancer cells with IC50 value of 10.94 μM. For mechanistic insights apoptotic propensity was evaluated using caspase-3 enzyme assay in PC-3 cells. The onset of apoptosis via caspase-3 activation was evidenced as a major pathway from morphological changes under fluorescence microscope with and without DAPI staining. The triggering of apoptosis via caspase 3 activation by the lead compound 3 was also confirmed from western blot analysis of PARP-1 in PC-3 cancer cells. Furthermore, the anti-metastatic potential of compound 3 can be predicted from its efficacy in restricting the migration of PC-3 cancer cells under in vitro conditions observed from wound healing assay.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

New findings on ligand series used as SARS-CoV-2 virus inhibitors within the frameworks of molecular docking, molecular quantum similarity and chemical reactivity indices

Background: The severe acute respiratory syndrome coronavirus (SARS-CoV)-2 virus causes an infectious illness named coronavirus disease 2019 (COVID-19). SARS-CoV is a positive-sense single-stranded RNA virus from the Betacoronavirus genus. The SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) has an important role in the viral life cycle and its active site is a very accessible region, thus a potential therapeutic approach may be to target this region to study the inhibition of viral replication. Various preexisting drugs have been proposed for the treatment of COVID-19 and the use of existing antiviral agents may reduce the time and cost of new drug discoveries, but the efficacy of these drugs is limited. Therefore, the aim of the present study was to evaluate a number of ligands used as SARS-CoV-2 virus inhibitors to determine the suitability of them for potential COVID-19 treatment. Methods: In this study, we selected a series of ligands used as SARS-CoV-2 virus inhibitors such as: abacavir, acyclovir, amprenavir, ascorbic acid vitamin C, azithromycin, baloxavir, boceprevir, cholecalciferol vitamin D, cidofovir, edoxudine, emtricitabine, hydroxychloroquine and remdesivir. These ligands were analyzed using molecular docking, molecular quantum similarity, and chemical reactivity indices defined within a conceptual density functional theory framework. Results: The analysis of molecular quantum similarity indices on inhibitors showed a high number of differences from a structural point of view. However, they are quite similar in their electronic density, obtaining the highest values in the electronic similarity index. Global and local chemical reactivity indices were analyzed. Conclusions: These studies allowed for the identification of the main stabilizing interactions using the crystal structure of SARS-CoV-2 RdRp. The molecular quantum similarity and chemical reactivity descriptors provide novel insights into these ligands that can be used in the design of new COVID-19 treatments.

Effect of ionic liquid as corrosion inhibitor for 6061 aluminium alloy-(electrochemical and quantum chemical approaches)

ABSTRACT Ionic liquid 1,3-dimethylimidazolium dimethyl phosphate (DIDP) is used as a possible green inhibitor for the corrosion control of 6061 aluminium alloy in 0.25 mol/L HCl is described in the study. Study involved electrochemical methods carried out at various temperatures by changing the concentrations of DIDP. Kinetic and thermodynamic parameters were determined using the Arrhenius rate law and transition state equations, respectively. Physisorption of the inhibitor takes place and the adsorption follows Freundlich isotherm. Surface morphology was studied by scanning electron microscopy (SEM), atomic force microscopy (AFM), and energy-dispersive X-ray analysis (EDAX) techniques. Quantum chemical studies were done by the density functional theory (DFT). The maximum inhibition efficiency of DIDP on 6061 aluminium alloy was about 78% for the concentration of 1000 ppm at 303 K. The mechanistic aspects of DIDP adsorption onto the metal surface were supported by quantum chemical studies. HOMO and LUMO of the optimized structure and quantum chemical descriptors confirmed the adsorption of the inhibitor on the metal surface. Mulliken charge population was used to identify the DIDP molecule’s high electron density region, and Fukui indices confirmed the interaction between metal and inhibitor.

The role of quantum-chemical descriptors and sigma-profile overlapping of different co-solvents on tuning ethanolic extraction of soybean oil

Co-solvents can significantly improve ethanol efficiency in vegetable oil extraction, making their use a viable, cost-effective, and sustainable alternative to conventional solvents. The challenge, however, is to understand the complex interactions between the solvents and how this can affect the yield and quality of the extracted oil. In this study, the COnductor-like Screening Model with Segment Activity Coefficient and Hansen solubility parameters were combined and used to optimize soybean oil extraction using ethanol along with seven different co-solvents. In parallel, an experimental approach was carried out to verify the accuracy of the models. Both theoretical and empirical results indicated that the oil extraction yield decreased as the co-solvent polarity increased. The best performance in solubilizing non-polar lipids was observed for the mixture of 10 wt% of p-cymene, a non-polar solvent, with 90 wt% ethanol, reaching a yield of 24.35 g/100 g. According to the thermodynamic analysis, the effects of polarity weigh heavily on the standard entropy, which rules the driving force of mass transfer during the extraction. The interaction mechanisms were carefully investigated employing quantum mechanical calculations, revealing how the differences in chemical potential (μ), hardness (η), and electrophilicity (ω) underlie the macroscopic results and rule the efficiency obtained for different solvent ratios and combinations.

Cyclocondensation of 3,4-diaminobenzophenone with glyoxal: Synthesis, X-ray structure, density functional theory calculation and molecular docking studies

7-benzoyl quinoxaline (BQ) was synthesized by the cyclocondensation of 3,4-diaminobenzophenone with glyoxal. BQ was characterized using elemental analysis (C, H, N), FT‐IR, 1H NMR and UV–Visible spectral studies. The crystal structure of BQ was solved by single crystal X-ray diffraction method. Chemical reactivity analysis of the newly synthesized molecule was made with the help of well-known quantum chemical descriptors like hardness, chemical potential, first and second electrophilicity indexes and electronic structure rules like Maximum Hardness Principle of Conceptual Density Functional Theory (CDFT). The results of CDFT based calculations showed that this new molecule is stable and can act a good electron acceptor. Molecular docking studies, providing valuable insights into the biological activity of the novel molecule, highlighted its potential to showcase promising therapeutic characteristics against commonly occurring cancer types. This encouraging behavior will be further validated through upcoming anti-proliferative studies, aiming to confirm its efficacy in combating cancerous cell growth and proliferation.

Electrochemistry evaluation and quantum corroboration with surface analysis of potential anticorrosive of two new pyridazine derivatives for mild steel in 1 M HCl solution

Two new structurally related pyridazine derivatives, Ortho (PO) and Para (PP), were synthesized by greener chemical pathway, and were spectroscopically specified by 1H NMR and 13C NMR. Their application in corrosion inhibition of mild steel (MS) in 1.0 M HCl was explored by experimental electrochemical tests of Potentiodynamic Polarization (PDP), Electrochemical Impedance Spectroscopy (EIS), with a surface and corrosive solution analyses by means of Scanning Electron Microscopy (SEM), Energy Dispersive X˗ray (EDX) analysis, and UV˗vis spectroscopy, as well as by Density functional theory (DFT) calculations for corroboration and development of an interpretation of the experimentally obtained anticorrosive activity by referring to the particular effects of molecular structure. Electrochemical experiments tests provided that the two compounds act by physisorption and chemisorption adsorption modes, with a maximum efficiency of 94.4% and 95.4% for PO and PP, respectively at 10˗3 mol L˗1, and obey of Langmuir’s theory of monolayer adsorption. SEM/EDX and UV˗vis analysis confirmed this state of adsorption by an inhibitor film. DFT quantum chemical descriptors and molecular dynamics simulations showed a reasonable correlation with experimentally corrosion inhibition performance.

Computational insights into E/Z isomerism of fluoxastrobin, an antifungal agent: A DFT/TD-DFT study

Herein, inspired by the success of strobilurins in fungicidal activity bioassays, DFT (Density Functional Theory)-based quantum chemical computations were performed on fluoxastrobin, a fluorinated strobilurin fungicide. Its formulation has E/Z isomerism around the C=N bond. It is highly advantageous to utilize quantum chemical methods to acquire more insights into E/Z isomerism and to gain supplemental confirmations in terms of experimental results. In this respect, the geometries of the E and Z isomers of fluoxastrobin were optimized both in the vacuum and in 1-octanol, acetonitrile, DMSO, and water phases using the DFT/B3LYP/6-311++G (d, p) methodology. Both experimental and theoretical FTIR and UV-vis evaluations were performed for the isomers. TD-DFT/B3LYP/6-311++G (d, p) theory level computations were determined that the observed peaks were mostly caused from the π→π∗ and n→π∗ transitions. Also, quantum chemical reactivity descriptors and physicochemical parameters were calculated for both vacuum and solvent phases. Accordingly, computations in all studied phases estimate the E isomer to be preferred with energy values in the range of 2.51-3.77 kcal/mol. Natural bond orbital (NBO) analysis was also carried out to determine the intermolecular interactions and their corresponding stabilization energies. Last, with the help of Gibbs solvation free energy values, 1-octanol/water partition coefficients were calculated and lipophilicity evaluations of both isomers were performed.

Density functional studies of antimony complexes derived from carboxamide ligands

The anti-leishmanial activity of antimony (III) complexes [1a-3a] bearing carboxamide ligands has been reported in the literature. In this study, a complete assignment of the vibrational spectra of these complexes was performed using the Scaled Quantum Mechanics Force Field (SQMFF) methodology in the solid phase at the DFT/ B3LYP/ LanL2DZ level. Quantum chemical descriptors (FMO, hardness, softness, etc) of these complexes were calculated to investigate the relationship between the calculated descriptors and their anti-leishmanial activities. As the softness of the complexes increased, the anti-leishmanial activity also increased. No correlation was found between the vibrational frequencies of the complexes and anti-leishmania activity.

An efficient method of searching for correlations between unlimited datasets to provide forecasting models

This work presents a highly powerful and very simple statistical method for creating predictive models based on structure–property relationships. The algorithm was implemented in the software CORRELATO, which demo version is presented in Online Supplementary Materials. This algorithm was tested on a small series of phthalocyanine dyes to search for the relationship between optical limiting effect and quantum chemical descriptors responsible for the nonlinear optical properties of these absorbers