Antibacterial metal complexes of o ‐sulfamoylbenzoic acid: Synthesis, characterization, and DFT study

Computational investigation of pyrazinamide drugs and its transition metal complexes using a DFT approach

Pyrazinamide, an antituberculosis but documented toxic drug, is subjected to computational investigation along with the metal complexes via a DFT approach to predict the structure-activity and structure-toxicity relationship. 6-31G(d,p) basis set was used for Zn, Ni, Mn, Fe, and Co, while the SDD basis set was applied to Cu, Cr, Cd, and Hg. Several reactivity parameters and charge distribution were calculated and the reactivity profile was estimated. The complexes were found to be soft and polarizable which could be responsible for their binding with bacterial targets to inhibit their growth. In contrast, pyrazinamide which is found to be hard among all is susceptible to being toxic. Moreover, the electronegative nature of the complexes can endow them with a better antibacterial effect. Since metal complexes have been found to be less toxic and more biologically interactive by computational methods, they can be employed as potent drugs for the cure of tuberculosis.

Heterocyclic Donor Moiety Effect on Optical Nonlinearity Behavior of Chrysene-Based Chromophores with Push-Pull Configuration via the Quantum Chemical Approach

Organic-based nonlinear optical (NLO) materials may be used in many optical-electronic systems and other next-generation defense technologies. With the importance of NLO materials, a series of push-pull architecture (D-π-A) derivatives (DTMD2-DTMD6) were devised from DTMR1 through structural alteration of different efficient donor heterocyclic groups. Density functional theory-based computations were executed at the MPW1PW91/6-31G(d,p) level to explore the NLO behavior of the derivatives. To investigate the optoelectronic behavior of the said compounds, various analyses like the frontier molecular orbital (FMO), global reactivity parameters, density of state (DOS), absorption spectra (UV-vis), natural bond orbital, and transition density matrix (TDM) were performed. The derivatives have a smaller band gap (2.156-1.492 eV) and a larger bathochromic shift (λmax = 692.838-969.605 nm) as compared to the reference chromophore (ΔE = 2.306 eV and λmax = 677.949 nm). FMO analysis revealed substantial charge conduction out of the donor toward the acceptor via a spacer that was also shown by TDM and DOS analyses. All derivatives showed promising NLO results, with the maximum amplitude of linear polarizability ⟨α⟩ and first (βtotal) and second (γtotal) hyperpolarizabilities over their reference chromophore. DTMD2 contained the highest βtotal (7.220 × 10-27 esu) and γtotal (1.720 × 10-31 esu) values corresponding with the reduced band gap (1.492 eV), representing potential futures for a large NLO amplitude. This structural modification through the use of various donors has played a significant part in achieving promising NLO behavior in the modified compounds.

Efficient Synthesis of Imine-Carboxylic Acid Functionalized Compounds: Single Crystal, Hirshfeld Surface and Quantum Chemical Exploration

Two aminobenzoic acid based crystalline imines (HMBA and DHBA) were synthesized through a condensation reaction of 4-aminobenzoic acid and substituted benzaldehydes. Single-crystal X-ray diffraction was employed for the determination of structures of prepared Schiff bases. The stability of super molecular structures of both molecules was achieved by intramolecular H-bonding accompanied by strong, as well as comparatively weak, intermolecular attractive forces. The comparative analysis of the non-covalent forces in HMBA and DHBA was performed by Hirshfeld surface analysis and an interaction energy study between the molecular pairs. Along with the synthesis, quantum chemical calculations were also accomplished at M06/6-311G (d, p) functional of density functional theory (DFT). The frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP), natural bond orbitals (NBOs), global reactivity parameters (GRPs) and natural population (NPA) analyses were also carried out. The findings of FMOs found that Egap for HMBA was examined to be smaller (3.477 eV) than that of DHBA (3.7933 eV), which indicated a greater charge transference rate in HMBA. Further, the NBO analysis showed the efficient intramolecular charge transfer (ICT), as studied by Hirshfeld surface analysis.

Synthesis, antioxidant activity, antimicrobial efficacy and molecular docking studies of 4-chloro-2-(1-(4-methoxyphenyl)-4,5-diphenyl-1H-imidazol-2-yl)phenol and its transition metal complexes

Herein, a one-pot synthesis of tetra-substituted imidazole, 4-chloro-2-(1-(4-methoxyphenyl)-4,5-diphenyl-1H-imidazol-2-yl)phenol (HL), is reported by the reaction of benzil, 5-bromosalicylaldehyde, ammonium acetate and anisidine. The synthesized imidazole was reacted with salts of 1st row transition metals (Co(ii), Ni(ii), Cu(ii), Mn(ii) and Zn(ii)) to obtain metal complexes. The structure of the compounds was confirmed using various spectroscopic and analytical techniques. HL, which is crystalline, was characterized by SC-XRD. Subsequently, the synthesized compounds were evaluated for their antioxidant and antimicrobial activities. Antimicrobial studies revealed the more noxious nature of metal complexes compared to ligand against various strains of bacteria and fungi. Molecular docking results based on the binding energy values also supported the experimental results of the antioxidant activities of the compounds. HL was found to be a better antioxidant than metal complexes. For a better insight into the structure, computational studies of the compounds were also carried out. A clear intra-molecular charge transfer was perceived in the ligand and its metal complexes. The transfer integral values for holes (36.48 meV) were found to be higher than the electron transfer integrals (24.76 meV), which indicated that the ligand would be a better hole transporter. According to the frontier molecular orbitals of the dimer, the charge transfer within the molecule is found from monomer 1 to 2.

Energy Level Prediction of Organic Semiconductors for Photodetectors and Mining of a Photovoltaic Database to Search for New Building Units

Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and artificial intelligence with conventional methods in optimization and material synthesis can guide experimental researchers to develop, design, predict and discover high-performance materials for photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is crucial to establish a relationship between photovoltaic properties and chemical structures before performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is desirable for designing better organic semiconductor photodetectors. Herein, we first collected large sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in the literature. In addition, molecular descriptors that make it easy and fast to predict the required properties were used to train machine learning models. Power conversion efficiency and energy levels were also predicted. Multiple models were trained using experimental data. The light gradient boosting machine (LGBM) regression model and Hist gradient booting regression model are the best models. The best models were further tuned to achieve better prediction ability. The reliability of our designed approach was further verified by mining the photovoltaic database to search for new building units. The results revealed that good consistency is obtained between experimental outcomes and model predictions, indicating that machine learning is a powerful approach to predict the properties of photodetectors, which can facilitate their rapid development in various fields.

Effect of different end-capped donor moieties on non-fullerenes based non-covalently fused-ring derivatives for achieving high-performance NLO properties

A series of derivatives (DOCD2-DOCD6) with D-π-A configuration was designed by substituting various efficient donor moieties via the structural tailoring of o-DOC6-2F. Quantum-chemical approaches were used to analyze the optoelectronic properties of the designed chromophores. Particularly, M06/6-311G(d,p) functional was employed to investigate the non-linear optical (NLO) response (linear polarizability ⟨α⟩, first (β_tot) and second ([Formula: see text]_tot) order hyperpolarizabilities) of the designed derivatives. A variety of analyses such as frontier molecular orbital (FMO), absorption spectra, transition density matrix (TDMs), density of states (DOS), natural bond orbital (NBO) and global reactivity parameters (GRPs) were employed to explore the optoelectronic response of aforementioned chromophores. FMO investigation revealed that DOCD2 showed the least energy gap (1.657 eV) among all the compounds with an excellent transference of charge towards the acceptor from the donor. Further, DOS pictographs and TDMs heat maps also supported FMO results, corroborating the presence of charge separation states along with efficient charge transitions. NBO analysis showed that π-linker and donors possessed positive charges while acceptors retained negative charges confirming the D-π-A architecture of the studied compounds. The λ_max values of designed chromophores (659.070-717.875 nm) were found to have broader spectra. The GRPs were also examined utilizing energy band gaps of E_HOMO and E_LUMO for the entitled compounds. Among all the derivatives, DOCD2 showed the highest values of β_tot (7.184 × 10^-27 esu) and [Formula: see text]_tot (1.676 × 10^-31 esu), in coherence with the reduced band gap (1.657 eV), indicating future potentiality for NLO materials.

Machine Learning-Assisted Prediction of the Biological Activity of Aromatase Inhibitors and Data Mining to Explore Similar Compounds

Designing molecules for drugs has been a hot topic for many decades. However, it is hard and expensive to find a new molecule. Thus, the cost of the final drug is also increased. Machine learning can provide the fastest way to predict the biological activity of druglike molecules. In the present work, machine learning models are trained for the prediction of the biological activity of aromatase inhibitors. Data was collected from the literature. Molecular descriptors are calculated to be used as independent features for model training. The results showed that the R ² values for linear regression, random forest regression, gradient boosting regression, and bagging regression are 0.58, 0.84, 0.77, and 0.80, respectively. Using these models, it is possible to predict the activity of new molecules in a short period of time and at a reasonable cost. Furthermore, Tanimoto similarity is used for similarity analysis, as well as a chemical database is mined to search for similar molecules. Nonetheless, this study provides a framework for repurposing other effective drug molecules to prevent cancer.

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis

Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models.