The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study

The present study applies the Hierarchical Technology for Quantitative Structure–Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD₅₀) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory (R ² = 0.96–0.98; Q ² = 0.91–0.93; R ² _test = 0.89–0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C–H fragments of the aromatic ring. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Prediction of drug solubility from molecular structure using a drug-like training set

Using a training set of 191 drug-like compounds extracted from the AQUASOL database a quantitative structure-property relationship (QSPR) study was conducted employing a set of simple structural and physicochemical properties to predict aqueous solubility. The resultant regression model comprised five parameters (ClogP, molecular weight, indicator variable for aliphatic amine groups, number of rotatable bonds and number of aromatic rings) and demonstrated acceptable statistics (r ² = 0.87, s = 0.51, F = 243.6, n = 191). The model was applied to two test sets consisting of a drug-like set of compounds (r ² = 0.80, s = 0.68, n = 174) and a set of agrochemicals (r ² = 0.88, s = 0.65, n = 200). Using the established general solubility equation (GSE) on the training and drug-like test set gave poorer results than the current study. The agrochemical test set was predicted with equal accuracy using the GSE and the QSPR equation. The results of this study suggest that increasing molecular size, rigidity and lipophilicity decrease solubility whereas increasing conformational flexibility and the presence of a non-conjugated amine group increase the solubility of drug-like compounds. Indeed, the proposed structural parameters make physical sense and provide simple guidelines for modifying solubility during lead optimisation.     

Compound set enrichment: a novel approach to analysis of primary HTS data

The main goal of high-throughput screening (HTS) is to identify active chemical series rather than just individual active compounds. In light of this goal, a new method (called compound set enrichment) to identify active chemical series from primary screening data is proposed. The method employs the scaffold tree compound classification in conjunction with the Kolmogorov-Smirnov statistic to assess the overall activity of a compound scaffold. The application of this method to seven PubChem data sets (containing between 9389 and 263679 molecules) is presented, and the ability of this method to identify compound classes with only weakly active compounds (potentially latent hits) is demonstrated. The analysis presented here shows how methods based on an activity cutoff can distort activity information, leading to the incorrect activity assignment of compound series. These results suggest that this method might have utility in the rational selection of active classes of compounds (and not just individual active compounds) for followup and validation.     

Identification of novel nt-MGAM inhibitors for potential treatment of type 2 diabetes: Virtual screening,atom based 3D-QSAR model,docking analysis and ADME study

In this study, a virtual screening procedure was applied to identify new potential nt-MGAM inhibitors as a possible medication for type 2 diabetes. To this aim, a series of salacinol analogues were first investigated by docking analysis for their binding to the X-ray structure of the biological target nt-MGAM. Key interactions for ligand binding into the receptor active site were identified which shared common features to those found for other known inhibitors, which strengthen the results of this study. 3D QSAR model was then built and showed to be statistically significant and with a good predictive power for the training (R² = 0.99, SD = 0.17, F = 555.3 and N = 27) and test set (Q² = 0.81, Pearson(r) = 0.92, RMSE = 0.52, N = 08). The model was then used to virtually screen the ZINC database with the aim of identifying novel chemical scaffolds as potential nt-MGAM inhibitors. Further, in silico predicted ADME properties were investigated for the most promising molecules. The outcome of this investigation sheds light on the molecular characteristics of the binding of salacinol analogues to nt-MGAM enzyme and identifies new possible inhibitors which have the potential to be developed into drugs, thus significantly contributing to the design and optimization of therapeutic strategies against type 2 diabetes.     

Modelling the viscosity of carbon-based nanomaterials dispersed in diesel oil: a machine learning approach

Journal of Thermal Analysis and Calorimetry - The viscosity of a nanofluid is one of its fundamental thermophysical properties, and it is an important consideration in heat transfer applications....     

Quantitative structure activity relationship (QSAR) studies on a series of imidazole derivatives as novel ORL1 receptor antagonists

QSAR studies were performed on a series of imidazole derivatives as novel Orl1 receptor antagonists. Imidazole derivatives have been analyzed in relation to their physicochemical and molecular properties. The activities of the compounds were found to be significantly correlated with the physicochemical parameters such as density (D), surface tension (St), index of refraction (Ior), balaban index (J) and partition coefficient (Log P). It was found that the presence of group at R₁ position was conducive for the inhibitory activity. The results are critically discussed on the basis of regression data and cross validation techniques. Poglani factor Q and the results of LOO (leave one out) method confirms the reliability and predictability of the proposed models.     

Identification of glucosyl-3-phosphoglycerate phosphatase as a novel drug target against resistant strain of Mycobacterium tuberculosis (XDR1219) by using comparative metabolic pathway approach

Tuberculosis (TB) is a major global health challenge. It has been afflicting human for thousands of years and is still severely affecting a huge population. The etiological agent of the disease is Mycobacterium tuberculosis (MTB) that survives in the human host in latent, dormant, and non-replicative state by evading the immune system. It is one of the leading causes of infection related death worldwide. The situation is exacerbated by the massive increase in the resistant strains such as multi-drug resistant TB (MDR-TB) and extensive drug-resistant TB (XDR-TB). The resistance is as severe that it resulted in failure of the current chemotherapy regimens (i.e. anti-tubercular drugs). It is therefore imperative to discover the new anti-tuberculosis drug targets and their potential inhibitors. Current study has made the use of in silico approaches to perform the comparative metabolic pathway analysis of the MTBXDR1219 with the host i.e. H. sapiens. We identified several metabolic pathways which are unique to pathogen only. By performing subtractive genomic analysis 05 proteins as potential drug target are retrieved. This study suggested that the identified proteins are essential for the bacterial survival and non-homolog to the host proteins. Furthermore, we selected glucosyl-3-phosoglycerate phosphatase (GpgP, EC 5.4.2.1) out of the 05 proteins for molecular docking analysis and virtual screening. The protein is involved in the biosynthesis of methylglucose lipopolysaccharides (MGLPs) which regulate the biosynthesis of mycolic acid. Mycolic acid is the building block of the unique cell wall of the MTB which is responsible for the resistance and pathogenicity. A relatively larger library consisting of 10,431 compounds was screened using AutoDock Vina to predict the binding modes and to rank the potential inhibitors. No potent inhibitor against MTB GpgP has been reported yet, therefore ranking of compounds is performed by making a comparison with the substrate i.e. glucosyl-3-phosphoglycerate. The obtained results provide the understanding of underlying mechanism of interactions of ligands with protein. Follow up study will include the study of the Protein-Protein Interactions (PPIs), and to propose the potential inhibitors against them.     

Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach

Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the k-nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an α-Al₂O₃ crystal. The crystal structure of α-Al₂O₃ was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (110) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process. © 2019 Wiley Periodicals, Inc.     

Reexamination of the Rehm-Weller data set reveals electron transfer quenching that follows a Sandros-Boltzmann dependence on free energy

In a landmark publication over 40 years ago, Rehm and Weller (RW) showed that the electron transfer quenching constants for excited-state molecules in acetonitrile could be correlated with the excited-state energies and the redox potentials of the electron donors and acceptors. The correlation was interpreted in terms of electron transfer between the molecules in the encounter pair (A*/D ? A(?-)/D(?+) for acceptor A and donor D) and expressed by a semiempirical formula relating the quenching constant, k(q), to the free energy of reaction, ΔG. We have reinvestigated the mechanism for many Rehm and Weller reactions in the endergonic or weakly exergonic regions. We find they are not simple electron transfer processes. Rather, they involve exciplexes as the dominant, kinetically and spectroscopically observable intermediate. Thus, the Rehm-Weller formula rests on an incorrect mechanism. We have remeasured k(q) for many of these reactions and also reevaluated the ΔG values using accurately determined redox potentials and revised excitation energies. We found significant discrepancies in both ΔG and k(q), including A*/D pairs at high endergonicity that did not exhibit any quenching. The revised data were found to obey the Sandros-Boltzmann (SB) equation k(q) = k(lim)/[1 + exp[(ΔG + s)/RT]]. This behavior is attributed to rapid interconversion among the encounter pairs and the exciplex (A*/D ? exciplex ? A(?-)/D(?+)). The quantity k(lim) represents approximately the diffusion-limited rate constant, and s the free energy difference between the radical ion encounter pair and the free radical ions (A(?-)/D(?+) vs A(?-) + D(?+)). The shift relative to ΔG for the overall reaction is positive, s = 0.06 eV, rather than the negative value of -0.06 eV assumed by RW. The positive value of s involves the poorer solvation of A(?-)/D(?+) relative to the free A(?-) + D(?+), which opposes the Coulombic stabilization of A(?-)/D(?+). The SB equation does not involve the microscopic rate constants for interconversion among the encounter pairs and the exciplex. Data that fit this equation contain no information about such rate constants except that they are faster than dissociation of the encounter pairs to (re-)form the corresponding free species (A* + D or A(?-) + D(?+)). All of the present conclusions agree with our recent results for quenching of excited cyanoaromatic acceptors by aromatic donors, with the two data sets showing indistinguishable dependencies of k(q) on ΔG.     

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.     

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.     

The interstitial atom of the nitrogenase FeMo-cofactor: ENDOR and ESEEM evidence that it is not a nitrogen

X-ray crystallographic study of the nitrogenase MoFe protein revealed electron density from an atom (denoted X) inside the active-site metal cluster, the [MoFe7S9:homocitrate] FeMo-cofactor. The electron density associated with X is consistent with a single N, O, or C atom. We now have tested whether X is an N or not by comparing the Q-band ENDOR and ESEEM signals from resting-state (S = 3/2) MoFe protein and NMF-extracted FeMo-co from bacteria grown with either 14N or 15N as the exclusive N source. All of the 14N or 15N signals associated with the protein are lost upon extraction of the FeMo-co. We interpret this as strong evidence that X is not an N.     

Molecular modeling on pyruvate phosphate dikinase of Entamoeba histolytica and in silico virtual screening for novel inhibitors

Pyruvate phosphate dikinase (PPDK) is the key enzyme essential for the glycolytic pathway in most common and perilous parasite Entamoeba histolytica. Inhibiting the function of this enzyme could control the wide spread of intestinal infections caused by Entamoeba histolytica in humans. With this objective, we modeled the three dimensional structure of the PPDK protein. We used templates with 51% identity and 67% similarity to employ homology-modeling approach. Stereo chemical quality of protein structure was validated by protein structure validation program PROCHECK and VERIFY3D. Experimental proof available in literature along with the in silico studies indicated Lys21, Arg91, Asp323, Glu325 and Gln337 to be the probable active sites in the target protein. Virtual screening was carried out using the genetic docking algorithm GOLD and a consensus scoring function X-Score to substantiate the prediction. The small molecule libraries (ChemDivision database, Diversity dataset, Kinase inhibitor database) were used for screening process. Along with the high scoring results, the interaction studies provided promising ligands for future experimental screening to inhibit the function of PPDK in Entamoeba histolytica. Further, the phylogeny study was carried out to assess the possibility of using the proposed ligands as inhibitors in related pathogens.     

Discovery of potential inhibitors targeting the kinase domain of polynucleotide kinase/phosphatase (PNKP): Homology modeling,virtual screening based on multiple conformations,and molecular dynamics simulation

In recent years, the level of interest has been increased in developing the DNA-repair inhibitors, to enhance the cytotoxic effects in the treatment of cancers. Polynucleotide kinase/phosphatase (PNKP) is a critical human DNA repair enzyme that repairs DNA strand breaks by catalyzing the restoration of 5’-phosphate and 3’-hydroxyl termini that are required for subsequent processing by DNA ligases and polymerases. PNKP is the only protein that repairs the 3′-hydroxyl group and 5′-phosphate group, which depicts PNKP as a potential therapeutic target. Besides, PNKP is the only DNA-repair enzyme that contains the 5′-kinase activity, therefore, targeting this kinase domain would motivate the development of novel PNKP-specific inhibitors. However, there are neither crystal structures of human PNKP nor the kinase inhibitors reported so far. Thus, in this present study, a sequential molecular docking-based virtual screening with multiple PNKP conformations integrating homology modeling, molecular dynamics simulation, and binding free energy calculation was developed to discover novel PNKP kinase inhibitors, and the top-scored molecule was finally submitted to molecular dynamics simulation to reveal the binding mechanism between the inhibitor and PNKP. Taken together, the current study could provide some guidance for the molecular docking based-virtual screening of novel PNKP kinase inhibitors.