Predictive models for tyrosinase inhibitors: Challenges from heterogeneous activity data determined by different experimental protocols

Quantitative Structure-Activity Relationship (QSAR) models of tyrosinase inhibitors were built using Random Forest (RF) algorithm and evaluated by the out-of-bag estimation (R²_OOB) and 10-fold cross validation (Q²_CV). We found that the performances of QSAR models were closely correlated with the systematic errors of inhibitory activities of tyrosinase inhibitors arising from the different measuring protocols. By defining ERR_sys, outliers with larger errors can be efficiently identified and removed from heterogeneous activity data. A reasonable QSAR model (R²_OOB of 0.74 and Q²_CV of 0.80) was obtained by the exclusion of 13 outliers with larger systematic errors. It is a clear example of the challenge for QSAR model that can overwhelm heterogeneous data from different experimental protocols.     

QSAR of conformationally flexible molecules: Comparative Molecular Field Analysis of protein-tyrosine kinase inhibitors

Summary Comparative Molecular Field Analysis (CoMFA) has been applied to a study of quantitative structureactivity relationships (QSAR) of conformationally flexible molecules. The relationship between three-dimensional structure and activity of 20 styrene derivatives which inhibit protein-tyrosine kinase was determined. A technique was developed that allows accurate prediction of the inhibitory activity of these molecules and identification in each case of the active conformation. The problem of multiple energetically acceptable conformations was approached in an iterative procedure. Use was made of the varying degrees of symmetry among the molecules. First, CoMFA QSAR models were developed using only those compounds that possess a symmetrical substituent pattern on the phenyl ring. These CoMFA models were then used to select the active conformers of the less symmetrical compounds in the set. Allowing multiple conformers for each compound in the dataset yielded higher crossvalidated r² values and better predictivity of the QSAR models. Different probe atoms (C⁺, O^–, neutral C) were explored, the O^– probe atom exhibiting the highest selectivity in the conformer selection process.     

QSAR Modeling,Molecular Docking and Cytotoxic Evaluation for Novel Oxidovanadium(IV) Complexes as Colon Anticancer Agents

Four new drug-based oxidovanadium (IV) complexes were synthesized and characterized by various spectral techniques, including molar conductance, magnetic measurements, and thermogravimetric analysis. Moreover, optimal structures geometry for all syntheses was obtained by the Gaussian09 program via the DFT/B3LYP method and showed that all of the metal complexes adopted a square-pyramidal structure. The essential parameters, electrophilicity (ω) value and expression for the maximum charge that an electrophile molecule may accept (ΔN_max) showed the practical biological potency of [VO(CTZ)₂] 2H₂O. The complexes were also evaluated for their propensity to bind to DNA through UV–vis absorption titration. The result revealed a high binding ability of the [VO(CTZ)₂] 2H₂O complex with K_b = 1.40 × 10⁶ M⁻¹. Furthermore, molecular docking was carried out to study the behavior of the VO (II) complexes towards colon cancer cell (3IG7) protein. A quantitative structure–activity relationship (QSAR) study was also implemented for the newly synthesized compounds. The results of validation indicate that the generated QSAR model possessed a high predictive power (R² = 0.97). Within the investigated series, the [VO(CTZ)₂] 2H₂O complex showed the greatest potential the most selective compound comparing to the stander chemotherapy drug.     

Antioxidant and Starch-Hydrolyzing Enzymes Inhibitory Properties of Striga-Resistant Yellow-Orange Maize Hybrids

Most of the health benefits derived from cereals are attributed to their bioactive compounds. This study evaluated the levels of the bioactive compounds, and the antioxidant and starch-hydrolyzing enzymes inhibitory properties of six pipeline Striga-resistant yellow-orange maize hybrids (coded AS1828-1, 4, 6, 8, 9, 11) in vitro. The maize hybrids were grown at the International Institute of Tropical Agriculture (IITA), Nigeria. The bioactive compounds (total phenolics, tannins, flavonoids, and phytate) levels, antioxidant (DPPH^• and ABTS^•+ scavenging capacity and reducing power) and starch-hydrolyzing enzymes (α-amylase and α-glucosidase) inhibitory activities of the maize hybrids were determined by spectrophotometry. At the same time, carotenoids were quantified using a reverse-phase HPLC system. The ranges of the bioactive compounds were: 11.25–14.14 mg GAE/g (total phenolics), 3.62–4.67 mg QE/g (total flavonoids), 3.63–6.29 mg/g (tannins), 3.66–4.31% (phytate), 8.92–12.11 µg/g (total xanthophylls), 2.42–2.89 µg/g (total β-carotene), and 3.17–3.77 µg/g (total provitamin A carotenoids). Extracts of the maize hybrids scavenged DPPH^• (SC₅₀: 9.07–26.35 mg/mL) and ABTS^•+ (2.65–7.68 TEAC mmol/g), reduced Fe³⁺ to Fe²⁺ (0.25 ± 0.64–0.43 ± 0.01 mg GAE/g), and inhibited α-amylase and α-glucosidase, with IC₅₀ ranges of 26.28–52.55 mg/mL and 47.72–63.98 mg/mL, respectively. Among the six clones of the maize hybrids, AS1828-9 had the highest (p < 0.05) levels of tannins and phytate and the strongest antioxidant and starch-hydrolyzing enzymes inhibitory activities. Significant correlations were observed between total phenolics and the following: ABTS^•+ (p < 0.01, r = 0.757), DPPH^• SC₅₀ (p < 0.01, r = −0.867), reducing power (p < 0.05, r = 0.633), α-amylase IC₅₀ (p < 0.01, r = −0.836) and α-glucosidase IC₅₀ (p < 0.05, r = −0.582). Hence, the Striga-resistant yellow-orange maize hybrids (especially AS1828-9) may be beneficial for alleviating oxidative stress and postprandial hyperglycemia.     

Non-Toxic Dimeric Peptides Derived from the Bothropstoxin-I Are Potent SARS-CoV-2 and Papain-like Protease Inhibitors

The COVID-19 outbreak has rapidly spread on a global scale, affecting the economy and public health systems throughout the world. In recent years, peptide-based therapeutics have been widely studied and developed to treat infectious diseases, including viral infections. Herein, the antiviral effects of the lysine linked dimer des-Cys¹¹, Lys¹²,Lys¹³-(pBthTX-I)₂K ((pBthTX-I)₂K)) and derivatives against SARS-CoV-2 are reported. The lead peptide (pBthTX-I)₂K and derivatives showed attractive inhibitory activities against SARS-CoV-2 (EC₅₀ = 28–65 µM) and mostly low cytotoxic effect (CC₅₀ > 100 µM). To shed light on the mechanism of action underlying the peptides’ antiviral activity, the Main Protease (M^pro) and Papain-Like protease (PL^pro) inhibitory activities of the peptides were assessed. The synthetic peptides showed PL^pro inhibition potencies (IC₅₀s = 1.0–3.5 µM) and binding affinities (K_d = 0.9–7 µM) at the low micromolar range but poor inhibitory activity against M^pro (IC₅₀ > 10 µM). The modeled binding mode of a representative peptide of the series indicated that the compound blocked the entry of the PL^pro substrate toward the protease catalytic cleft. Our findings indicated that non-toxic dimeric peptides derived from the Bothropstoxin-I have attractive cellular and enzymatic inhibitory activities, thereby suggesting that they are promising prototypes for the discovery and development of new drugs against SARS-CoV-2 infection.     

Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Obesity is one of the most provoking health burdens in the developed countries. One of the strategies to prevent obesity is the inhibition of pancreatic lipase enzyme. The aim of this study was to build QSAR models for natural lipase inhibitors by using the Monte Carlo method. The molecular structures were represented by the simplified molecular input line entry system (SMILES) notation and molecular graphs. Three sets – training, calibration and test set of three splits – were examined and validated. Statistical quality of all the described models was very good. The best QSAR model showed the following statistical parameters: r² = 0.864 and Q² = 0.836 for the test set and r² = 0.824 and Q² = 0.819 for the validation set. Structural attributes for increasing and decreasing the activity (expressed as pIC₅₀) were also defined. Using defined structural attributes, the design of new potential lipase inhibitors is also presented. Additionally, a molecular docking study was performed for the determination of binding modes of designed molecules.     

Quantitative structure–activity relationship model for prediction study of corrosion inhibition efficiency using two‐stage sparse multiple linear regression

A new quantitative structure–activity relationship (QSAR) of the inhibition of mild steel corrosion in 1 M hydrochloric acid using furan derivatives was developed by proposing two‐stage sparse multiple linear regression. The sparse multiple linear regression using ridge penalty and sparse multiple linear regression using elastic net (SMLRE) were used to develop the QSAR model. The results show that the SMLRE‐based model possesses high predictive power compared with sparse multiple linear regression using ridge penalty‐based model according to the mean‐squared errors for both training and test datasets, leave‐one‐out internal validation (Q²_int = 0.98), and external validation (Q²_ext = 0.95). In addition, the results of applicability domain assessment using the leverage approach reveal a reliable and robust SMLRE‐based model. In conclusion, the developed QSAR model using SMLRE can be efficiently used in the studies of corrosion inhibition efficiency. Copyright © 2016 John Wiley & Sons, Ltd.     

Predicting skin permeability from complex chemical mixtures: incorporation of an expanded QSAR model

Quantitative structure–activity relationship (QSAR) models have been widely used to study the permeability of chemicals or solutes through skin. Among the various QSAR models, Abraham’s linear free-energy relationship (LFER) model is often employed. However, when the experimental conditions are complex, it is not always appropriate to use Abraham’s LFER model with a single set of regression coefficients. In this paper, we propose an expanded model in which one set of partial slopes is defined for each experimental condition, where conditions are defined according to solvent: water, synthetic oil, semi-synthetic oil, or soluble oil. This model not only accounts for experimental conditions but also improves the ability to conduct rigorous hypothesis testing. To more adequately evaluate the predictive power of the QSAR model, we modified the usual leave-one-out internal validation strategy to employ a leave-one-solute-out strategy and accordingly adjust the Q² _LOO statistic. Skin permeability was shown to have the rank order: water > synthetic > semi-synthetic > soluble oil. In addition, fitted relationships between permeability and solute characteristics differ according to solvents. We demonstrated that the expanded model (r² = 0.70) improved both the model fit and the predictive power when compared with the simple model (r² = 0.21).     

Computational Approaches for the Design of Novel Anticancer Compounds Based on Pyrazolo[3,4-d]pyrimidine Derivatives as TRAP1 Inhibitor

In the present in-silico study, various computational techniques were applied to determine potent compounds against TRAP1 kinase. The pharmacophore hypothesis DHHRR_1 consists of important features required for activity. The 3D QSAR study showed a statistically significant model with R² = 0.96 and Q² = 0.57. Leave one out (LOO) cross-validation (R² CV = 0.58) was used to validate the QSAR model. The molecular docking study showed maximum XP docking scores (−11.265, −10.532, −10.422, −10.827, −10.753 kcal/mol) for potent pyrazole analogs (42, 46, 49, 56, 43), respectively, with significant interactions with amino acid residues (ASP 594, CYS 532, PHE 583, SER 536) against TRAP1 kinase receptors (PDB ID: 5Y3N). Furthermore, the docking results were validated using the 100 ns MD simulations performed for the selected five docked complexes. The selected inhibitors showed relatively higher binding affinities than the TRAP1 inhibitor molecules present in the literature. The ZINC database was used for a virtual screening study that screened ZINC05297837, ZINC05434822, and ZINC72286418, which showed similar binding interactions to those shown by potent ligands. Absorption, distribution, metabolism, and excretion (ADME) analysis showed noticeable results. The results of the study may be helpful for the further development of potent TRAP1 inhibitors     

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling,Auto-QSAR,and Molecular Docking Approaches

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.     

Discovery of New Coumarin-Based Lead with Potential Anticancer,CDK4 Inhibition and Selective Radiotheranostic Effect: Synthesis, 2D & 3D QSAR,Molecular Dynamics,In Vitro Cytotoxicity,Radioiodination, and Biodistribution Studies

Novel 6-bromo-coumarin-ethylidene-hydrazonyl-thiazolyl and 6-bromo-coumarin-thiazolyl-based derivatives were synthesized. A quantitative structure activity relationship (QSAR) model with high predictive power r² = 0.92, and RMSE = 0.44 predicted five compounds; 2b, 3b, 5a, 9a and 9i to have potential anticancer activities. Compound 2b achieved the best ΔG of –15.34 kcal/mol with an affinity of 40.05 pki. In a molecular dynamic study 2b showed an equilibrium at 0.8 Å after 3.5 ns, while flavopiridol did so at 0.5 Å after the same time (3.5 ns). 2b showed an IC₅₀ of 0.0136 µM, 0.015 µM, and 0.054 µM against MCF-7, A-549, and CHO-K1 cell lines, respectively. The CDK4 enzyme assay revealed the significant CDK4 inhibitory activity of compound 2b with IC₅₀ of 0.036 µM. The selectivity of the newly discovered lead compound 2b toward localization in tumor cells was confirmed by a radioiodination biological assay that was done via electrophilic substitution reaction utilizing the oxidative effect of chloramine-t. ¹³¹I-2b showed good in vitro stability up to 4 h. In solid tumor bearing mice, the values of tumor uptake reached a height of 5.97 ± 0.82%ID/g at 60 min p.i. ¹³¹I-2b can be considered as a selective radiotheranostic agent for solid tumors with promising anticancer activity.     

Exploring the Dual Characteristics of CH3OH Adsorption to Metal Atomic Structures on Si (111)-7 × 7 Surface

Metal atoms were deposited on an Si (111)-7 × 7 surface, and they were adsorbed with alcohol gases (CH₃OH/C₂H₅OH/C₃H₇OH). Initially, C_nH_2n+1OH adsorption was simply used as an intermediate layer to prevent the chemical reaction between metal and Si atoms. Through scanning tunneling microscopy (STM) and a mass spectrometer, the C_nH_2n+1OH dissociation process is further derived as the construction of a surface quasi-potential with horizontal and vertical directions. With the help of three typical metal depositions, the surface characteristics of CH₃OH adsorption are more clearly presented in this paper. Adjusting the preheating temperature, the difference of thermal stability between CH₃O^– and H⁺ could be obviously derived in Au deposition. After a large amount of H⁺ was separated, the isolation characteristic of CH₃O^– was discussed in the case of Fe deposition. In the process of building a new metal-CH₃O^–-H⁺ model, the dual characteristics of CH₃OH were synthetically verified in Sn deposition. CH₃O^– adsorption is prone to influencing the interaction between the metal deposition and substrate surface in the vertical direction, while H⁺ adsorption determines the horizontal behavior of metal atoms. These investigations lead one to believe that, to a certain extent, the formation of regular metal atomic structures on the Si (111)-7 × 7-CH₃OH surface is promoted, especially according to the dual characteristics and adsorption models we explored.     

Low temperature methanation of CO2 over an amorphous cobalt-based catalyst

CO₂ methanation is an important reaction in CO₂ valorization. Because of the high kinetic barriers, the reaction usually needs to proceed at higher temperature (>300 °C). High-efficiency CO₂ methanation at low temperature (<200 °C) is an interesting topic, and only several noble metal catalysts were reported to achieve this goal. Currently, design of cheap metal catalysts that can effectively accelerate this reaction at low temperature is still a challenge. In this work, we found that the amorphous Co–Zr_0.1–B–O catalyst could catalyze the reaction at above 140 °C. The activity of the catalyst at 180 °C reached 10.7 mmol_CO2 g_cat⁻¹ h⁻¹, which is comparable to or even higher than that of some noble metal catalysts under similar conditions. The Zr promoter in this work had the highest promoting factor to date among the catalysts for CO₂ methanation. As far as we know, this is the first report of an amorphous transition metal catalyst that could effectively accelerate CO₂ methanation. The outstanding performance of the catalyst could be ascribed to two aspects. The amorphous nature of the catalyst offered abundant surface defects and intrinsic active sites. On the other hand, the Zr promoter could enlarge the surface area of the catalyst, enrich the Co atoms on the catalyst surface, and tune the valence state of the atoms at the catalyst surface. The reaction mechanism was proposed based on the control experiments.

It is discovered that an amorphous transition metal catalyst Co–Zr0.1–B–O could effectively accelerate CO2 methanation, at a rate that is comparable to or even higher than that of some noble metal catalysts under similar conditions.