New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods

To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R² = 0.900 (training set); Q² = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.

Experimental Investigations of AlMg3 Components with Polyurethane and Graphene Oxide Nanosheets Composite Coatings, after Accelerated UV-Aging

The use of graphene (Gr) and its derivates graphene oxide (GO) showed that these materials are good candidates to enhance the properties of polyurethane (PU) coatings, especially the anticorrosion ones since graphene absorbs most of the light and provides hydrophobicity for repelling water. An important aspect of these multifunctional materials is that all these improvements can be realized even at very low filler loadings in the polymer matrix. In this work, an ultrasound cavitation technique was used for the proper dispersion of GO nanosheets (GON) in polyurethane (PU) resin to obtain a composite coating to protect the AlMg3 substrate. The addition of GON considerably improved the physical properties of coatings, as demonstrated by electrochemical impedance spectroscopy (EIS) analysis, promising improved anticorrosion performance after accelerated UV-ageing. Computational methods and Differential Scanning Calorimetry (DSC) measurements showed that GON facilitates the formation of additional bonds and stabilizes the PU structures during the ultraviolet (UV) exposure and aggressive attack of corrosive species. Limiting oxygen index (LOI) data reveal a slow burning behaviour of PU-GON coatings during UV exposure, which is better than PU alone.

Application of Molecular Docking, Homology Modeling, and Chemometric Approaches to Neonicotinoid Toxicity against Aphis craccivora

Neonicotinoids are known as effective pesticides against various insect species. They can harm useful insects including honeybees, with a relatively low threat to nontarget organisms and the environment. This paper presents combined methods to explore the insecticidal activity of neonicotinoids with diverse scaffolds, active against Aphis craccivora. Pharmacophore, molecular docking into the active site of nicotinic acetylcholine receptor homology model, and linear and non-linear regression approaches were used to find new insecticide candidates. The potential toxic effects against honeybees were evaluated using the molecular docking in the active site of the new Aphis mellifera homology model. Four new untested compounds were assigned as insecticide candidates, active against Aphis craccivora with less potential toxic effects for honeybees. This approach may be an effective strategy to design environmentally friendly insecticides against the cowpea aphid.

Small Molecules of Natural Origin as Potential Anti-HIV Agents: A Computational Approach

The human immunodeficiency virus type 1 (HIV-1), one of the leading causes of infectious death globally, generates severe damages to people's immune systems and makes them susceptible to serious diseases. To date, there are no drugs that completely remove HIV from the body. This paper focuses on screening 224,205 natural compounds of ZINC15 NPs subset to identify those with bioactivity similar to non-nucleoside reverse transcriptase inhibitors (NNRTIs) as promising candidates to treat HIV-1. To reach the goal, an in silico approach involving 3D-similarity search, ADMETox, HIV protein-inhibitor prediction, docking, and MM-GBSA free-binding energies was trained. The FDA-approved HIV drugs, efavirenz, etravirine, rilpivirine, and doravirine, were used as queries. The prioritized compounds were subjected to ADMETox, docking, and MM-GBSA studies against HIV-1 reverse transcriptase (RT). Lys101, Tyr181, Tyr188, Trp229, and Tyr318 residues and free-binding energies have proved that ligands can stably bind to HIV-1 RT. Three natural products (ZINC37538901, ZINC38321654, and ZINC67912677) containing oxan and oxolan rings with hydroxyl substituents and one (ZINC2103242) having 3,6,7,8-tetrahydro-2H-pyrido[1,2-a]pyrazine-1,4-dione core exhibited comparable profiles to etravirine and doravirine, with ZINC2103242 being the most promising anti-HIV candidate in terms of drug metabolism and safety profile. These findings may open new avenues to guide the rational design of novel HIV-1 NNRTIs.

Combined Experimental and Theoretical Insights into the Corrosion Inhibition Activity on Carbon Steel Iron of Phosphonic Acids

The inhibition effect of N,N'-phosphonomethylglycine (PMG) and vinyl phosphonic acid (VPA) on the 3% NaCl acidic solution corrosion of carbon steel iron was studied at different immersion times by potentiodynamic polarization, electrochemical impedance spectroscopy, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, and computational methods. It is found from the polarization studies that PMG and VPA behave as mixed-type inhibitors in NaCl. Values of charge transfer resistance (Rct) and double layer capacitance (Cdl) in the absence and presence of inhibitors are determined. The PMG and VPA inhibitors were capable of inhibiting the corrosion process up to ≈91% and ≈85%, respectively. In the presence of PMG, the synergic effect of chlorine ions was observed. Density functional theory (DFT) was engaged to establish the adsorption site of PMG, VPA, and their deprotonated states. For studied compounds, the resulted values of ELUMO, EHOMO, energy gap (∆E), dipole moment (μ), electronic hardness (η), global softness (σ), electrophilic index (ω), and the electronic potential map are in concordance with the experimental data results regarding their corrosion inhibition behavior and adsorption on the metal surface.

Comprehensive investigation of selectivity landscape of glycogen synthase kinase-3 inhibitors

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.

Partial Least Squares Discriminant Analysis and 3D Similarity Perspective Applied to Analyze Comprehensively the Selectivity of Glycogen Synthase Kinase 3 Inhibitors

The current work was conducted to investigate the effectiveness of two conceptually distinct in silico ligand-based tools: Partial Least Squares Discriminant Analysis (PLS-DA) and 3D similarity, including shape, physico-chemical and electrostatics to classify target-specific pharmacophores with enrichment power for selective GSK-3 inhibitors against the phylogenetically related CDK-2, CDK-4, CDK-5 and PKC. All virtual screens were performed on four data sets of targets matched pairwise, including selective and nonselective inhibitors for GSK-3. The classification method PLS-DA results revealed that all obtained models are statistically robust according to the cross-validation and response permutation tests. Regarding selective GSK-3 inhibitors differentiation in terms of selectivity (Se), specificity (Sp), and accuracy (ACC), the PLS-DA models for CDK-4/GSK-3, and PKC/GSK-3 datasets are highly efficient discriminative. 3D similarity searches for CDK-4/GSK-3, PKC/GSK-3, and CDK-2/GSK-3 datasets using the most selective reference molecules lead to highest enrichments of selective GSK-3 inhibitors. EON yields excellent early and overall enrichments for ET_ST and ET_combo for most selective query for CDK-4/GSK-3. CDK-5/GSK-3 dataset didn't show consistent statistically significant enrichments for 3D similarity virtual screening. The current methodology is reliable and could be used as a powerful tool for the detection of potentially selective molecules targeting GSK-3.

In silico studies on smoothened human receptor and its antagonists in search of anticancer effects

Lately, the cancers related with abnormal hedgehog (Hh) signalling pathway are targeted by smoothened (SMO) receptor inhibitors that are rapidly developing. Still, the problems of known inhibitors such as severe side effects, weak potency against solid tumors or even the acquired resistance need to be overcome by developing new suitable inhibitors. To explore the structural requirements of antagonists needed for SMO receptor inhibition, pharmacophore mapping, 3D-QSAR models, database screening and docking studies were performed. The best selected pharmacophore hypothesis based on which statistically significant atom-based 3D-QSAR model was developed (R2 = = 0.856, Q2 = 0.611 and Pearson-R = 0.817), was further subjected to dataset screening in order to evaluate its ability to prioritize active compounds over decoys. The efficiency of one four-points pharmacophore hypothesis (AAHR.524) was observed based on good evaluation metrics such as the area under the curve (0.795), and weighted average precision (0.835), suggesting that the model is trustworthy in predicting novel inhibitors against SMO receptor.

Pharmacophore Modeling and Docking Study of Pyrazolylaminoquinazoline Derivatives as Highly Potent Fibroblast Growth Factor Receptor Inhibitors2 (FGFR2)

In this study pharmacophore modeling and molecular docking investigations have been performed on pyrazolylaminoquinazoline derivatives, highly potent fibroblast growth factor receptor2 (FGFR2) inhibitors. The best pharmacophore hypotheses displaying five features (ADHRR.2051 and AADHR.798) were generated using a set of 28 compounds. The associated 3D atom-based quantitative structure � activity relationships (QSAR) models were statistically robust showing high correlation coefficients (R-squared = 0.981 / 0.982), and cross validation coefficients (Q-squared = 0.645 / 0.671). The R-Pearson values for the test set of 0.805 / 0.820 indicate that the models are robust and exhibit good predictive power. The interactions of pyrazolylaminoquinazoline with FGFR2 binding site revealed two hydrogen bonds with Ala567. The obtained pharmacophore, 3D atom-based QSAR models and binding features resulted from docking studies can help medicinal chemists to design new pyrazolylaminoquinazoline inhibitors with improved potency.

An Approach to Identify New Insecticides Against Myzus Persicae. In silico Study Based on Linear and Non-linear Regression Techniques

Neonicotinoids are known to have high insecticidal potency, low mammalian toxicity and relatively tough activity for the development of resistance against aphids. A series of guadipyr insecticides, active against Myzus persicae was engaged in silico studies, based on Multiple Linear Regression (MLR), Partial Least Squares regression (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Pharmacophore modeling. Robust and predictive models were built using correlations between the insecticidal profile, expressed by experimental pLC₅₀ values, and molecular descriptors, calculated from the energy optimized structures. Four new potential insecticides active against Myzus persicae and their predicted pLC₅₀ toxicity values were reported for the first time. The models presented here can be used as an approach in the screening and prioritization of chemicals in a scientific and regulatory frame and for toxicity prediction.

ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes

Flavonoids, the vastest class of natural polyphenols, are extensively investigated for their multiple benefits on human health. Due to their physicochemical or biological properties, many representatives are considered to exhibit low selectivity among various protein targets or to plague high-throughput screening (HTS) outcomes. The aim of this study is to highlight reliable, bioselective compounds sharing flavonoidic scaffolds in HTS experiments. A filtering scheme was applied to remove undesired flavonoids (and related compounds) from confirmatory PubChem bioassays. A number of 433 compounds addressing various protein targets form the core of the collection of bioselective flavonoids and related compounds (ColBioS-FlavRC). With an additional set of 2908 inactive related compounds, ColBioS-FlavRC offers the grounds for method optimization and validation. We exemplified the use of ColBioS-FlavRC by pharmacophore modeling, subsequently (externally) validated for virtual screening purposes. The early enrichment capabilities of the pharmacophore hypotheses were measured by means of the median exponential retriever operating curve enrichment (MeROCE), a suited metric in comparative evaluations of virtual screening methods. ColBioS-FlavRC is available in the Supporting Information and is freely accessible for further studies.

PLS and shape-based similarity analysis of maleimides--GSK-3 inhibitors

CONTEXT

Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer's disease, cancer, inflammation, obesity, etc.

OBJECTIVE

The aim of the current investigation was to model the inhibitory activity of maleimide derivatives--inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure-Activity Relationship (QSAR) and the effect of the template on shape-similarity--binding affinity relationship.

MATERIALS AND METHODS

Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated.

RESULTS

Reliable statistics R(2)(Y(CUM)) = 0.938/0.920, Q((2)(Y)(CUM)) = 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained.

DISCUSSION AND CONCLUSIONS

The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

MTD−PLS: A PLS Variant of the Minimal Topologic Difference Method. III. Mapping Interactions between Estradiol Derivatives and the Alpha Estrogenic Receptor

A homogeneous collection of 45 estrogen agonist derivatives with relative binding affinities measured to the estrogen receptor from Ratus norvegicus was used. The quantitative structure-activity relationships were derived using an improved minimal topologic difference (MTD) method in a partial least-squares (PLS) variant. The spatially assigned analysis of fragment properties can provide receptor site maps, within the limits of the existing series. A steric misfit was found for the steroidal position 2; benefic hydrophobic and van der Waals (enhanced by high polarizability) interactions were found for the 17alpha-CH=CH-X group. MTD-PLS mapping results are confirmed by the experimentally derived estradiol-estrogen receptor binding site contacts (based on X-ray crystallography). Our results suggest that this MTD-PLS method can yield useful results for interactions with receptors of unknown 3D structure and, generally, for the steric rigidity of receptor sites.