External Validation Parameters Research Articles

2D‐QSAR and molecular docking studies of carbamate derivatives to discover novel potent anti‐butyrylcholinesterase agents for Alzheimer's disease treatment

AbstractCurrently, anti‐butyrylcholinesterase (anti‐BuChE)is among the greatest therapeutic agents for the treatment of Alzheimer's disease. In this research, a series of 36 carbamate derivatives were subjected to a quantitative structure–activity relationships study using DFT and Lipinski's descriptors. Multiple linear regression (MLR) was used to explore the relationships between the structural features of these compounds and BuChE inhibitory activity. In order to generate results applicable in the experimental plan, the Organization of Economic Cooperation and Development guidelines were adopted. The quality of MLR model was evaluated by several statistical parameters including internal and external validation parameters (R2, R2adj, F, VIF, R2test, Q2CV, R2Rand, Q2CV [Rand], and cRp2). The built model displayed a high predictive power (R2test = 0.817). What is more, the internal validation parameters (Q2CV = 0.774; average R2Rand = 0.118; average Q2CV [Rand] = −0.438; cRp2 = 0.820) highlight the robustness of our built model. Besides, the obtained result revealed that anti‐BuChE activity is mainly attributed to the following molecular descriptors: octanol–water partition (log P), highest occupied molecular orbital energy (EHOMO), total energy (ET), and dipole moment (μ). Based on these findings, a series of newer synthesizable compounds with enhanced anti‐BuChE activities were designed and their ADMET and drug‐likeness properties were further predicted to filter out compounds likely to fail during drug development stages. Finally, molecular docking and molecular dynamics were performed to identify the binding types between the best designed compounds and BuChE enzyme.

Ecotoxicological QSTR and QSTTR Modeling for the Prediction of Acute Oral Toxicity of Pesticides against Multiple Avian Species.

The ever-increasing use of pesticides in response to the rising agricultural demand has threatened the existence of nontarget organisms like avian species, disrupting the global ecological integrity. Therefore, it is critical to protect and restore different endangered bird species from the perspective of ecosystem safety. In the present work, we have developed regression-based two-dimensional quantitative structure toxicity relationship (2D QSTR) and quantitative structure toxicity-toxicity relationship (QSTTR) models to estimate the toxicity of pesticides on five different avian species following the Organization for Economic Co-operation and Development (OECD) guidelines. Rigorous validation has been performed using different statistical internal and external validation parameters to ensure the robustness and interpretability of the developed models. From the developed models, it can be stated that the presence of electronegative and lipophilic features greatly enhance pesticide toxicity, whereas the hydrophilic characters are shown to have a detrimental impact on the toxicity of pesticides. Moreover, the developed QSTTR models have been employed to the in silico toxicity prediction of 124, 154, and 250 pesticides against bobwhite quail, ring-necked pheasant, and mallard duck species, respectively, extracted from the Office of Pesticides Program (OPP) Pesticide Ecotoxicity Database. The information obtained from the modeled descriptors might be used for pesticide risk assessment in the future, with the added benefit of providing an early caution of their possible negative impact on birds for regulatory purposes.

Elaboration of a Predictive Qsar Model of the Anti-Paludial Activity of a Series of Dihydrothiophenone Molecules at Theory Level B3LYP/ 6-31G (d, p)

The purpose of this study is to develop a QSAR model predictive of the antimalarial activity of a series of Dihydrothiophenone molecules using quantum chemical methods. The molecules were optimized from the B3LYP/6-31G (d, p) level of theory. The extracted descriptors are the vibrational frequency of the carbonyl group (Ѵ(C=O)), enthalpy of formation (Δ f H°), the valence angle between the carbon-nitrogen-carbon atoms α(C-N-C) and the ionization potential (I); The application of the RLM method of the XLSTAT program allowed us to develop a regression model. The statistical indicators (R²=93.50%, S=0.211, F=43.678) of the developed model attest to its robustness and reliability. Internal and external validation parameters (Q2loo et Q2ext) reveal that the established model performs well in predicting the antimalarial activity of the series of molecules studied. It can therefore be used to design new HD molecules belonging to its field of applicability at a 95% confidence level.

Predictive Modeling of the Anti-Paludial Activity of a Series of Dihydrothiophenone Molecules at the Hartree-Fock (HF) / 6-31G (d, p) Level

To investigate the relationship between antimalarial activity and molecular structures, a QSAR study is applied to a set of 19 Dihydrothiophenone compounds. This study is performed using the linear multiple regression (MLR) method. Calculations at the HF/6-31G (d, p) level of theory have been performed to obtain structure information. The molecular descriptors used are: carbonyl group vibrational frequency (Ѵ(C=O)), nitrogen-hydrogen vibrational frequency(Ѵ(NH)), entropy of formation (ΔfS) and lowest occupied energy(Elumo). The obtained model gives statistically significant results and shows good predictability: R2 = 0.925, S = 0.230 et F = 22.257. Internal and external validation parameters (Q2loo =0.934et Q2ext=0.748) reveal that the established model performs well in predicting the antimalarial activity of the investigated series of molecules Vibrational frequency of the carbonyl group (Ѵ(C=O)), is the priority descriptor in predicting the antimalarial activity of the investigated series of molecules. The acceptance criteria of Eriksson et al. used for the test set are verified.

Intelligent Consensus Predictions of Biodegradation Half-Life of Petroleum Hydrocarbons (PHCs)

The present study explores the important chemical features of diverse petroleum hydrocarbons (PHCs) responsible for their biodegradation by developing partial least squares (PLS) regression-based quantitative structure-property relationship (QSPR) models. The biodegradability is estimated in terms of biodegradation half-life (Logt1/2). All the PLS models were extensively validated by different internationally acceptable internal (R2= 0.849–0.861; Q2 = 0.833–0.849; R2adj = 0.845–0.858) and external (Q2F1= 0.825-0.848; Q2F2 = 0.822–0.845) validation parameters. The consensus predictions were also performed by using the “intelligent consensus predictor” (ICP) tool, which improves the predictive ability of individual models based on mean absolute error (MAE)-based criteria. The models suggested that the biodegradation of PHCs is dependent on the presence of substituents on the aromatic ring, 12 atom containing ring system, thiophene moiety, electron rich chemicals, large molecular size, degree of unsaturation, degree of branching, cyclization, and hydrophobicity.

Identification of Potential C-kit Protein Kinase Inhibitors Associated with Human Liver Cancer: Atom-based 3D-QSAR Modeling, Pharmacophores-based Virtual Screening and Molecular Docking Studies

Rhodanine and its derivatives exhibit interesting biological activities as well as a wide range of biological applications. In this study, a dataset of seventy-four molecules with anticancer activities against human cancer cell line Huh-7D12, were chosen for the modeling of pharmacophores and Quantitative Structure Activity (3D-QSAR) relationship. Pharmacophoric models containing five sites were generated from three characteristics: hydrogen bond acceptor (A), hydrophobic (H) and aromatic ring (R). After the validation, eight hypotheses which presented a good power of selectivity of the active agents were selected (GH > 0.5). Internal and external validation parameters indicated that the generated 3D-QSAR model exhibits good predictive capabilities and significant statistical reliability (R2 = 0.9606, Q2 = 0.955, = 0.952). Pharmacophoric models and contour maps provided significant information on the main structural features of rhodanine derivatives. Twenty-one molecules were returned from the Enamine chemical database after molecular docking studies (HTVS, SP, XP, and IFD). These provided an estimate of ligand-protein binding interactions essential for anticancer activity. The ADMET prediction of these 21 compounds suggested that their pharmacophoric properties lie within an acceptable range. This result indicates that these new compounds provide an effective basis for the methodical development of potent inhibitors of the protein kinase C-kit.

First report on chemometric modeling of hydrolysis half-lives of organic chemicals.

Hydrolysis is one of the most important processes of transformation of organic chemicals in water. The rates of reactions, final chemical entities of these processes, and half-lives of organic chemicals are of considerable interest to environmental chemists as well as authorities involved in the controlling the processing and disposal of such organic chemicals. In this study, we have proposed QSPR models for the prediction of hydrolysis half-life of organic chemicals as a function of different pH and temperature conditions using only two-dimensional molecular descriptors with definite physicochemical significance. For each model, suitable subsets of variables were elected using a genetic algorithm method; next, the elected subsets of variables were subjected to the best subset selection with a key objective to determine the best combination of descriptors for model generation. Finally, QSPR models were constructed using the best combination of variables employing the partial least squares (PLS) regression technique. Next, every final model was subjected for strict validation employing the internationally accepted internal and external validation parameters. The proposed models could be applicable for data gap filling to determine hydrolysis half-lives of organic chemicals at different environmental conditions. Generally, presence of aliphatic ether and ether functional groups, high percentage of oxygen content in the molecule and presence of O-Si pairs of atoms at topological distance one, results in a shorter hydrolysis half-life of organic chemicals. On the other hand, higher unsaturation content and high percentage of nitrogen content in molecules lead to higher hydrolysis half-life. It is also found that branched and compact molecules will have a lower half-life while straight chain analogues will have a higher half-life. To the best of our knowledge, the presented models are the first reported QSPR models for hydrolysis half-lives of organic chemicals at different pH values.

A Multi-layered Variable Selection Strategy for QSAR Modeling of Butyrylcholinesterase Inhibitors.

Alzheimer's disease (AD), a neurological disorder, is the most common cause of senile dementia. Butyrylcholinesterase (BuChE) enzyme plays a vital role in regulating the brain acetylcholine (ACh) neurotransmitter, but in the case of Alzheimer's disease (AD), BuChE activity gradually increases in patients with a decrease in the acetylcholine (ACh) concentration via hydrolysis. ACh plays an essential role in regulating learning and memory as the cortex originates from the basal forebrain, and thus, is involved in memory consolidation in these sites. In this work, we have developed a partial least squares (PLS)-regression based two dimensional quantitative structure-activity relationship (2D-QSAR) model using 1130 diverse chemical classes of compounds with defined activity against the BuChE enzyme. Keeping in mind the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have tried to select significant descriptors from the large initial pool of descriptors using multi-layered variable selection strategy using stepwise regression followed by genetic algorithm (GA) followed by again stepwise regression technique and at the end best subset selection prior to development of final model thus reducing noise in the input. Partial least squares (PLS) regression technique was employed for the development of the final model while model validation was performed using various stringent validation criteria. The results obtained from the QSAR model suggested that the quality of the model is acceptable in terms of both internal (R2= 0.664, Q2= 0.650) and external (R2 Pred= 0.657) validation parameters. The QSAR studies were analyzed, and the structural features (hydrophobic, ring aromatic and hydrogen bond acceptor/donor) responsible for enhancement of the activity were identified. The developed model further suggests that the presence of hydrophobic features like long carbon chain would increase the BuChE inhibitory activity and presence of amino group and hydrazine fragment promoting the hydrogen bond interactions would be important for increasing the inhibitory activity against BuChE enzyme. Furthermore, molecular docking studies have been carried out to understand the molecular interactions between the ligand and receptor, and the results are then correlated with the structural features obtained from the QSAR models. The information obtained from the QSAR models are well corroborated with the results of the docking study.

In silico evaluation of inhibitory potential of novel triazole derivatives against therapeutic target myristoyl-CoA: protein N-myristoyltransferase (NMT) of Candida albicans

This paper explores the confluence of genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure–activity relationship (QSAR) modeling and molecular docking simulation studies relevant to the novel and effective triazole derivatives as NMT inhibitors in an attempt to develop superior antifungal activity coupled with less susceptibility to develop resistance. Initially, thirteen penta-variant models were generated through hybrid GA-MLR based QSAR approach. Eventually, a general pooled model was derived through the collective consideration of all the descriptors incorporated in the stated thirteen models. The model was found to be predictive model as per traditional internal and external validation parameters (R2 = 0.829, Q2 = 0.759, $${R}_{\mathrm{adj}}^{2}\hspace{0.17em}$$ = 0.796, $${R}_{\mathrm{Pred}}^{2}$$ = 0.613) along with Y-randomization test and recently proposed various $$r_{m}^{2}$$ metrics. The proposed model is in agreement with Golbraikh and Tropsha recommendations supplemented by non-existence of multicollinearity via ridge trace, VIF trace and tolerance data analysis. The modeled descriptors signify the role of atomic van der Waals volume, nitrogen-containing fragment, number of hydrogen atoms and hydrogen bonding in modulating antifungal activity. Further, the molecular docking simulation studies reinforce the role of hydrogen bonding interactions in modulating the activity; as reflected by QSAR analysis; docking poses have revealed that four triazole derivatives were profusely bonded through hydrophobic, electrostatic and hydrogen bond interactions and stabilize into the active site of target protein NMT. Asn392 and His227 are key amino acid residues liable for generation of hydrogen bond interactions whereas Leu350 and Tyr354 amino acid residues were accountable for steric interactions. Based on the studies the two molecules are hypothesized and proposed with optimized biological function.

In silico modeling for dual inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes in Alzheimer’s disease

In this research, we have implemented two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling using two different datasets, namely, acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzyme inhibitors. A third dataset has been derived based on their selectivity and used for the development of partial least squares (PLS) based regression models. The developed models were extensively validated using various internal and external validation parameters. The features appearing in the model against AChE enzyme suggest that a small ring size, higher number of −CH2- groups, higher number of secondary aromatic amines and higher number of aromatic ketone groups may contribute to the inhibitory activity. The features obtained from the model against BuChE enzyme suggest that the sum of topological distances between two nitrogen atoms, higher number of fragments X-C(=X)-X, higher number of secondary aromatic amides, fragment R--CR-X may be more favorable for inhibition. The features obtained from selectivity based model suggest that the number of aromatic ethers, unsaturation content relative to the molecular size and molecular shape may be more specific for the inhibition of the AChE enzyme in comparison to the BuChE enzyme. Moreover, we have implemented the molecular docking studies using the most and least active molecules from the datasets in order to identify the binding pattern between ligand and target enzyme. The obtained information is then correlated with the essential structural features associated with the 2D-QSAR models.

Theoretical investigation and design of some indole derivatives as potent \u03b2-glucuronidase inhibitors

Backgroundβ-glucuronidase enzyme is mostly found in plants and animals. It plays a vital role in detoxification of reactive metabolites that are interrelated to several illnesses and the growth of colon cancer. It speeds up the breaking down of β-glucuronosyl-O-bonds. Lack of β-glucuronidase enzyme leads to Sly syndrome in humans, and overexpression of this enzyme leads to many diseases. Therefore, it becomes necessary to mediate the effect of this enzyme.ResultTheoretical investigation via QSAR modeling on 30 indole derivatives was performed to build a model which could be used to predict the activity of the indole derivatives. QSAR was carried out using multi-linear regression (MLR) method utilizing genetic function approximation (GFA) to develop the QSAR models. A very high predictive QSAR model was reported based on its statistical fitness with good internal and external validation parameters: R2trng = 0.954942, Qcv2 = 0.925462, R2test = 0.855393, and LOF = 0.042924. Molecular docking on the 30 indole derivatives was also performed to screen and identify the lead compound that would be used as template for designing new indole compounds. The docking investigation reveals that ligand 10 binds very tight in the binding pocket of β-glucuronidase enzyme with binding energy of − 9.5 kcal/mol. The ligand (10) was chosen as a template for designing new β-glucuronidase inhibitors. The four design compounds were found to be better than the template and the standard drug (D-saccharic acid 1, 4-lactone) with binding energies of − 9.6, − 9.7, − 9.8, and − 9.9 kcal/mol.ConclusionA very high predictive QSAR model with good internal and external validation parameters: R2trng = 0.954942, Qcv2 = 0.925462, R2test = 0.855393, and LOF = 0.042924, was built and reported in this study. Molecular docking investigation reveals that the most potent compound among all the data set was compound 10 with binding energy of − 9.5 kcal/mole. It bound to the binding pocket of β-glucuronidase enzyme via hydrophobic, electrostatic, and hydrogen bond, and it was retained as template for designing new indole compounds. The design compound with serial number ID 4 was identified to have the highest binding energy of − 9.9 kcal/mole among the designed compounds. It bound to the binding site of the β-glucuronidase enzyme via halogen, hydrophobic, electrostatic, and hydrogen bond. The design compounds were discovered to be better than the template used in the design and the standard drug.

Quantitative Structure-Property Relationship Study for Prediction of Boiling Point and Enthalpy of Vaporization of Alkenes.

Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°vap/kJ.mol-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One-Out (LOO) cross-validation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2F1, Q2F2, Q2F3), the concordance correlation coefficient (CCC) and the predictive parameter R2 m. The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.

Predicting pungency and understanding the pungency mechanism of capsaicinoids using TOPS-MODE approach

ABSTRACT Quantitative structure–property relationship (QSPR) models were developed for predicting the pungency of a set of capsaicinoids. Multiple linear regression (MLR) coupled with topological substructural molecular descriptor (TOPS-MODE) approach was used. The best MLR model based on only five orthogonalized TOPS-MODE variables allowed us to obtain a coefficient of determination of 0.954 on the training set. The predictive power of the model was validated through a test set and several external validation parameters. This showed that the TOPS-MODE descriptors weighted by bond dipole moments, van der Waals atomic radii, and the total solute hydrogen bond basicity affected pungency. The contributions of certain bonds and fragments to pungency were used to understand the pungency mechanism of capsaicinoids. The selected model can more accurately predict pungency of capsaicinoids compared than those found in the literature, and especially bring insights into the structural features and chemical factors related to pungency.

Quantitative structure–chromatographic retention relationship of synthesized peptides (HGRFG, NPNPT) and their derivatives

Carapax Trionycis extract peptides (HGRFG, NPNPT) are able to protect against CCl4-induced liver fibrosis. Therefore, this study applies to deal with chromatographic lipophilicity determination of synthesized peptides (HGRFG, NPNPT) and their derivatives using reversed-phase high performance liquid chromatography (RP-HPLC) combined with methanol-water mobile phase and two reversed-phase chromatographic columns (COSMOISL 5C18-MS-II and SHIMADZU-C18). The chromatographic lipophilicity of the analyzed compounds was expressed as logkw constant and correlated with lipophilicity descriptors. Quantitative structure-retention relationships (QSRR) analysis was performed to imitate chromatographic lipophilicity behavior using molecular descriptors. Modeling was performed using linear regression (LR) and multiple linear regression (MLR) methods with the help of principal component analysis (PCA) and hierarchical cluster analysis (HCA). The most influential molecular descriptors were lipophilicity descriptors, which are important for molecules ability to pass through biological membranes. All established QSRR models were statistically validated by standards, cross- and external validation parameters. According to these statistical validation parameters, MLR models (R2 > 0.856) were better for chromatographic lipophilicity prediction of peptide compounds. It can be concluded that chromatographic systems with COSMOISL 5C18-MS-II column were better for modeling of logkw than systems with SHIMADZU-C18 column. Modeling was performed in order to obtain lipophilicity profiles of investigated compounds as future drug candidates.

Cheminformatic modelling of β-amyloid aggregation inhibitory activity against Alzheimer's disease

In the current research, we have developed robust two-dimensional quantitative structure-activity relationship (2D-QSAR) and pharmacophore models using a dataset of 314 heterocyclic β-amyloid aggregation inhibitors. The main purpose of this study is to determine the essential structural features which are responsible for the inhibition of β-amyloid aggregation. Prior to the development of the 2D-QSAR model, we applied a multilayered variable selection method to reduce the size of the pool of descriptors, and the final models were built by the partial least squares (PLS) regression technique. The models obtained were thoroughly analysed by applying both internal and external validation parameters. The validation metrics obtained from the analysis suggested that the developed models were significant and sufficient to predict the inhibitory activity of unknown compounds. The structural features obtained from the pharmacophore model, such as the presence of aromatic rings and hydrogen bond acceptor/donor or hydrophobic sites, are well corroborated with those of the 2D-QSAR models. Additionally, we also performed a molecular docking study to understand the molecular interactions involved in binding, and the results were then correlated with the requisite structural features obtained from the 2D-QSAR and 3D-pharmacophore models.

In silico QSAR and molecular docking simulation of some novel aryl sulfonamide derivatives as inhibitors of H5N1 influenza A virus subtype

BackgroundThis research provides a comprehensive analysis of QSAR modeling performed on 25 aryl sulfonamide derivatives to predict their effective concentration (EC50) against H5N1 influenza A virus by using some numerical information derived from structural and chemical features (descriptors) of the compounds to generate a statistically significant model. Subsequently, the molecular docking simulations were done so as to determine the binding modes of some potent ligands in the dataset with the M2 proton channel protein of the H5N1 influenza A virus as the target.ResultsIn building the QSAR model, the genetic algorithm task was employed in the variable selection of the descriptors which are used to form the multi-linear regression equation. The model with descriptors, RDF100m, nO, and RDF45p, showed satisfactory internal and external validation parameters (R2train = 0.72963, R2adjusted = 0.67169, Q2cv = 0.598, {R}_{mathrm{pred}}^2= 0.67295, R2test = 0.6860) which passed the model criteria of acceptability. Docking simulation results of the more potent compounds (ligands 2, 3, and 8) revealed the formation of hydrophobic and hydrogen bonds with the binding pockets of M2 protein of influenza A virus.ConclusionThe results in this study can help to advance the research in designing (in silico design) and synthesis of more potent aryl sulfonamides derivatives against H5N1 influenza virus.

Identification of prodigious and under-privileged structural features for RG7834 analogs as Hepatitis B virus expression inhibitor

In the present work, QSAR (quantitative structure−activity relationship) analysis has been executed for RG7834 analogs. RG7834 is a first-in-class selective and orally available dihydroquinolizinone (DHQ)-based small molecule Hepatitis B virus expression inhibitor. OECD’s guidelines have been followed for developing multiple QSAR models for Hepatitis B virus expression inhibitory activity of 73 RG7834 analogs. The present multiple QSAR models are not only easily interpretable but possess high external predictive ability, as well. These are effective in the recognition of many privileged and underprivileged molecular descriptors, which could be very valuable for the use of these models by the experts and nonexperts of QSAR in future optimizations. The models satisfy threshold values for many fitting, internal and external validation parameters, such as R2 = 0.83, Q2 = 0.80, CCCext = 0.88, etc., thereby demonstrating good external predictive ability of the models. The multiple QSAR and pharmacophoric models successfully identified a good number of important positively and negatively related structural features of RG7834 analogs that govern their Hepatitis B virus expression inhibitory activity. The results could be very beneficial to synthetic/medicinal chemists for future alterations of RG7834 analogs as better drug candidates.

Modeling the Reduction and Cross-Contamination of Salmonella in Poultry Chilling Process in China.

The study was to establish a predictive model for reduction and cross-contamination of Salmonella on chicken in chilling process. Reduction of Salmonella on chicken was 0.75 ± 0.04, 0.74 ± 0.08, and 0.79 ± 0.07 log CFU/g with 20, 50, and 100 mg/L of chlorine, respectively. No significant differences of bacterial reductions with 20–100 mg/L of chlorine were found and a Normal (−0.75, 0.1) distribution could describe the uncertainty of bacterial reductions. Inoculated and non-inoculated chicken samples were washed together and bacterial transfer rates among them were 0.13%–0.004% with 20–100 mg/L of chlorine. No significant differences of transfer rates with 50–100 mg/L of chlorine were observed and a Triangle (−2.5, −1.5, −1.1) distribution could describe the log transfer rate. Additionally, a 3-factor response surface model based on the central composite design was developed to evaluate the effects of initial contamination level (1–5 log CFU/g), pre-chill incidence (3%–40%) and chlorine concentration (0–100 mg/L) on post-chill incidence. The post-chill incidences in these treatments were within 30%–91.7%. The developed model showed a satisfactory performance to predict the post-chill incidence as evidenced by statistical indices (pseudo-R2 = 0.9; p < 0.0001; RMSE = 0.21) and external validation parameters (Bf = 1.02; Af = 1.11).

Risk assessment of heterogeneous TiO2-based engineered nanoparticles (NPs): a QSTR approach using simple periodic table based descriptors

Nowadays, the risk assessment of engineered nanoparticles (NPs) on human health and animals is of great importance. We have used here simple periodic table based descriptors for mixture compounds to predict the cytotoxicity for the heterogeneous NPs. We have developed mono parametric quantitative structure-toxicity relationship (QSTR) models for 34 TiO2-based NPs modified with (poly) metallic clusters of noble metals (Au, Ag, Pt) to assess the cytotoxicity (-log EC50) towards Chinese Hamster Ovary cell line. After critical statistical analysis of the developed five linear regression (LR) models, we found that the derived models are close to each other in terms of different metric values (R2 = 0.922–0.926; Q2 = 0.907–0.911; R2adj = 0.918–0.922; Q2F1 = 0.930–0.938; Q2F2 = 0.924–0.932). Thus, we have developed a partial least squares (PLS) model using the five descriptors obtained from the five LR models. The developed PLS model showed good predictivity and robustness in terms of both internal (R2 = 0.925; Q2 = 0.911) and external validation (Q2F1 = 0.944; Q2F2 = 0.938) parameters. The descriptors, Electrochemical equivalent (Eq), 2nd ionization potential (2χpi), covalent radius (Rc), amount of Ag (Agamt) and thermal conductivity (Tc) obtained from the final PLS model well explained the cause of cytotoxicity of the heterogeneous NPs without requiring any computationally expensive descriptors. The insights obtained from the developed models suggested that higher electronegativity, lower oxidation state, and release of metal cation from its oxide increase cytotoxicity through various mechanisms. Thus, these models can be used as efficient tools to assess the toxicity with physiological property of the new heterogeneous NPs in the future.

Application of 3D-QSAR, Pharmacophore, and Molecular Docking in the Molecular Design of Diarylpyrimidine Derivatives as HIV-1 Nonnucleoside Reverse Transcriptase Inhibitors.

Diarylpyrimidines (DAPYs), acting as HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs), have been considered to be one of the most potent drug families in the fight against acquired immunodeficiency syndrome (AIDS). To better understand the structural requirements of HIV-1 NNRTIs, three-dimensional quantitative structure–activity relationship (3D-QSAR), pharmacophore, and molecular docking studies were performed on 52 DAPY analogues that were synthesized in our previous studies. The internal and external validation parameters indicated that the generated 3D-QSAR models, including comparative molecular field analysis (CoMFA, = 0.679, = 0.983, and = 0.884) and comparative molecular similarity indices analysis (CoMSIA, = 0.734, = 0.985, and = 0.891), exhibited good predictive abilities and significant statistical reliability. The docking results demonstrated that the phenyl ring at the C4-position of the pyrimidine ring was better than the cycloalkanes for the activity, as the phenyl group was able to participate in π–π stacking interactions with the aromatic residues of the binding site, whereas the cycloalkanes were not. The pharmacophore model and 3D-QSAR contour maps provided significant insights into the key structural features of DAPYs that were responsible for the activity. On the basis of the obtained information, a series of novel DAPY analogues of HIV-1 NNRTIs with potentially higher predicted activity was designed. This work might provide useful information for guiding the rational design of potential HIV-1 NNRTI DAPYs.