Pharmacokinetics and drug-likeness of antidiabetic flavonoids: Molecular docking and DFT study

Computer aided toxicity and pharmacokinetic prediction studies attracted the attention of pharmaceutical industries as an alternative means to predict potential drug candidates. In the present study, in-silico pharmacokinetic properties (ADME), drug-likeness, toxicity profiles of sixteen antidiabetic flavonoids that have ideal bidentate chelating sites for metal ion coordination were examined using SwissADME, Pro Tox II, vNN and ADMETlab web tools. Density functional theory (DFT) calculations were also employed to calculate quantum chemical descriptors of the compounds. Molecular docking studies against human alpha amylase were also conducted. The results were compared with the control drugs, metformin and acarbose. The drug-likeness prediction results showed that all flavonoids, except myricetin, were found to obey Lipinski’s rule of five for their drug like molecular nature. Pharmacokinetically, chrysin, wogonin, genistein, baicalein, and apigenin showed best absorption profile with human intestinal absorption (HIA) value of ≥ 30%, compared to the other flavonoids. Baicalein, butein, ellagic acid, eriodyctiol, Fisetin and quercetin were predicted to show carcinogenicity. The flavonoid derivatives considered in this study are predicted to be suitable molecules for CYP3A probes, except eriodyctiol which interacts with P-glycoprotein (p-gp). The toxicological endpoints prediction analysis showed that the median lethal dose (LD50) values range from 159–3919 mg/Kg, of which baicalein and quercetin are found to be mutagenic whereas butein is found to be the only immunotoxin. Molecular docking studies showed that the significant interaction (-7.5 to -8.3 kcal/mol) of the studied molecules in the binding pocket of the α-amylase protein relative to the control metformin with the crucial amino acids Asp 197, Glu 233, Asp 197, Glu 233, Trp 59, Tyr 62, His 101, Leu 162, Arg 195, His 299 and Leu 165. Chrysin was predicted to be a ligand with high absorption and lipophilicity with 84.6% absorption compared to metformin (78.3%). Moreover, quantum chemical, ADMET, drug-likeness and molecular docking profiles predicted that chrysin is a good bidentate ligand.

Diaryl Sulfide Derivatives as Potential Iron Corrosion Inhibitors: A Computational Study

The present work aimed to assess six diaryl sulfide derivatives as potential corrosion inhibitors. These derivatives were compared with dapsone (4,4′-diaminodiphenyl sulfone), a common leprosy antibiotic that has been shown to resist the corrosion of mild steel in acidic media with a corrosion efficiency exceeding 90%. Since all the studied compounds possess a common molecular backbone (diphenyl sulfide), dapsone was taken as the reference compound to evaluate the efficiency of the remainder. In this respect, two structural factors were examined, namely, (i) the effect of replacement of the S-atom of diaryl sulfide by SO or SO2 group, (ii) the effect of the introduction of an electron-withdrawing or an electron-donating group in the aryl moiety. Two computational chemical approaches were used to achieve the objectives: the density functional theory (DFT) and the Monto Carlo (MC) simulation. First, B3LYP/6-311+G(d,p) model chemistry was employed to calculate quantum chemical descriptors of the studied molecules and their geometric and electronic structures. Additionally, the mode of adsorption of the tested molecules was investigated using MC simulation. In general, the adsorption process was favorable for molecules with a lower dipole moment. Based on the adsorption energy results, five diaryl sulfide derivatives are expected to act as better corrosion inhibitors than dapsone.

Spectroscopic and Quantum Chemical Investigations of Hypothetical m-Diacetylaminoazopyrimidine and Its Photovoltaic Properties

Here, an attempt is made to theoretically study and predict the electronic and spectroscopic (UV-Vis and IR) and structural properties, quantum chemical descriptors and subsequent application of diacetylaminoazopyrimidine in dye-sensitized solar cells (DSSCs). Ground- and excited-state time-dependent density functional theory (TD-DFT) calculations were carried out using material studio and ORCA software, respectively. The computed ground-state energy gap, chemical hardness, chemical softness, chemical potential, electronegativity and electrophilicity index are: 3.60 eV, 1.80 eV, 0.56 eV, 4.49 eV, -4.49 eV and 5.68, respectively. Conversely, the DFT-predicted excited-state quantum chemical descriptors are: 1.67 eV, 0.83 eV, 1.20 eV, 4.71 eV and -4.71 eV, corresponding to the energy gap, chemical hardness, chemical softness, chemical potential and electronegativity, respectively. Furthermore, vibrational frequency calculations confirm the presence of some key functional groups (N=N, C=O, C-H) present in the dye molecules. The computed optoelectronic parameters, such as light-harvesting efficiency, electron injection and open-circuit voltage are 0.06 eV, -8.59 eV and -5.75 eV, respectively. Overall, the dye possesses a relatively good current conversion efficiency as compared to other dyes studied in the literature; hence, it could be used as a novel material for photovoltaic technological applications. Keywords: Diacetylaminoazopyrimidine, DFT, Excited state, Spectroscopy, DSSCs.

Kernel Methods for Predicting Yields of Chemical Reactions

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.

Kernel Methods for Predicting Yields of Chemical Reactions

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalisability of the models, with particular interest along the aryl halide dimension.

Quantitative Structure–property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers

Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. Herein, we analyzed the available data on singlet oxygen generation quantum yields (F?) for a dataset containing > 70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran). In order to build reliable QSPR model, we synthesized a series of new BODIPYs containing different electron donating aryl groups in the meso position, studied their optical and structural properties along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using Density Functional Theory (DFT), namely M06-2X functional. QSPR models predicting F? values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R = 0.88 ? 0.91 and q2 = 0.62 ? 0.69) for all three solvents. A small root mean squared error of 8.2% was obtained for F? values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting F? values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.<br>

Quantitative Structure–property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers

Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. Herein, we analyzed the available data on singlet oxygen generation quantum yields (F?) for a dataset containing > 70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran). In order to build reliable QSPR model, we synthesized a series of new BODIPYs containing different electron donating aryl groups in the meso position, studied their optical and structural properties along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using Density Functional Theory (DFT), namely M06-2X functional. QSPR models predicting F? values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R = 0.88 ? 0.91 and q2 = 0.62 ? 0.69) for all three solvents. A small root mean squared error of 8.2% was obtained for F? values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting F? values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.<br>