Sodium Succinate as a Corrosion Inhibitor for Carbon Steel Rebars in Simulated Concrete Pore Solution.

The inhibiting performance of sodium succinate (Na2C4H4O4) was evaluated as an organic environmentally friendly corrosion inhibitor for carbon steel rebars in 0.6 M Cl- simulated concrete pore solution. Potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) measurements were utilized to evaluate the inhibitor performance at different temperatures and concentrations. The investigated corrosion inhibitor showed strong corrosion inhibition performance as it adsorbs on the surface of the rebar, creating a protective adsorption film. According to PDP, the inhibitor is classified as a mixed-type inhibitor with an inhibitor efficiency of 77, 69, 59, and 54% for 25, 35, 45, and 55 °C, respectively. EIS validated the PDP tests, showing that sodium succinate displaces the water molecules at the interface, creating an adsorption film by complexing with ferrous ions. The film thickness was calculated, and sodium succinate was able to produce a thicker protective film (span of nanometers) relative to the reference at every temperature. The adsorption of sodium succinate follows the Temkin adsorption isotherm. ΔG0ads was found to be -32.75 kJ/mol, indicating that the inhibitor adsorption is a combined physisorption and chemisorption process. Different surface characterizations were utilized to substantiate the adsorption of sodium succinate, these include scanning electron microscopy, energy-dispersive X-ray spectroscopy, and micro-Raman spectroscopy. Finally, quantum chemical calculations showed that the delocalized electrons in the carboxyl group have high HOMO energies and electrostatic potential, which facilitates the adsorption of sodium succinate corrosion inhibitor onto the carbon steel rebar surface.

A Validity Study Applying the Rasch Model to the American Association for the Advancement of Science Force and Motion Sub-Topic Assessment for Middle School Students.

The purpose of this research was to investigate the reliability of the scores produced and validity of the inferences drawn from the American Association for the Advancement of Science (AAAS, 2018) force and motion sub-topic assessment for middle school students. The assessment of student outcomes in STEM is an international focus in K-12 education. Project 2061, initiated by the AAAS, focuses on addressing challenges related to standards and assessments. This study informs this effort through testing a 14-item multiple-choice test constructed of questions from the AAAS item bank. Two samples of eighth-grade students participated (N = 1777). Rasch analysis applying the dichotomous model (Rasch, 1960) indicated sufficient item separation and reliability. Thirteen items fit the Rasch model and one item was removed for misfit. Further support for construct validity was observed with 78 percent of item ordering aligned with that predicted by physics educators and stability of measures for 11 items across the two samples. One item exhibited significant differential item functioning by gender and minority status in science. After inspection by physics educators, no bias in item wording or context was determined. Recommendation for additional items is made to increase item targeting and variance explained by the Rasch linear measure.

Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation.

Glycolysis with PK-M2 occurs typically in anaerobic conditions and atypically in aerobic conditions, which is known as the Warburg effect. The Warburg effect is found in many oncogenic situations and is believed to provide energy and biomass for oncogenesis to persist. The work presented targets human PK-M2 (hPK-M2) in a virtual high-throughput screen to identify new inhibitors and leads for further study. In the initial screen, one of the 12 candidates selected for experimental validation showed biological activity (hit-rate = 8.13%). In the second screen with retrained models, six of 11 candidates selected for experimental validation showed biological activity (hit-rate: 54.5%). Additionally, four different scaffolds were identified for further analysis when examining the tested candidates and compounds in the training data. Finally, extrapolation was necessary to identify a sufficient number of candidates to test in the second screen. Examination of the results suggested stepwise extrapolation to maximize efficiency.

Identifying de-NEDDylation inhibitors: Virtual high-throughput screens targeting SENP8.

Protein modification can have far-reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high-throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over-NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high-throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high-throughput screening improved hit rates over traditional high-throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.

Identifying new clotting factor XIa inhibitors in virtual high-throughput screens using PCA-GA-SVM models and signature.

Blood Clotting Factor XI is an important actor in the clotting mechanism: it activates downstream zymogen involved in the clotting process. It can be targeted for activation or inhibition depending on treatment goals to enhance or inhibit clotting. In terms of antithrombosis treatment, Factor XI has emerged as a promising target to focus on. In this work, an iterative virtual high-throughput screening pipeline was proposed that can supplement current efforts to find inhibitors. The first iteration identified 11 compounds to test with 3 active for a hit-rate of 27.3%. The second iteration of the pipeline identified another 11 compounds to test with 7 active for a hit-rate of 63.6%. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1553-1565, 2018.

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s.

When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue damage occurs. Current Food and Drug Administration approved treatments include supplemental recombinant C1 inhibitor, but these are extremely costly and a more economical solution is desired. In our work, we have utilized an existing data set of 136 compounds that have been previously tested for activity against C1. Using these compounds and the activity data, we have created models using principal component analysis, genetic algorithm, and support vector machine approaches to characterize activity. The models were then utilized to virtually screen the 72 million compound PubChem repository. This first round of virtual high-throughput screening identified many economical and promising inhibitor candidates, a subset of which was tested to validate their biological activity. These results were used to retrain the models and rescreen PubChem in a second round vHTS. Hit rates for the first round vHTS were 57%, while hit rates for the second round vHTS were 50%. Additional structure⁻property analysis was performed on the active and inactive compounds to identify interesting scaffolds for further investigation.

Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models.

There currently is renewed interest in blood clotting Factor XII as a potential target for thrombosis inhibition. Historically untargeted, there is little drug information with which to start drug candidate searches. Typical high-throughput screening can identify potential drug candidates, but is inefficient. Virtual high-throughput screening can be used to raise efficiency by focusing experimental efforts on compounds predicted to be active and is applied here to identify new Factor XIIa inhibitors. We combine principal component analysis, genetic algorithm and support vector machine to create the models used in the virtual high-throughput screening. In this work, experimental data from a PubChem Bioassay was used to train predictive models of Factor XIIa inhibition activity. The models created were then used to virtually screen the entire 72 million PubChem Compound database. Experimental validation of select candidates identified by this process resulted in a 42.9% hit-rate in the first-pass and 100% hit-rate in the second-pass, suggesting the effectiveness of the approach.

Potential glucocorticoid receptor ligands with pulmonary selectivity using I-QSAR with the signature molecular descriptor.

We intend in this research to establish a rational method for the development of novel glucocorticoid receptor ligands to more effectively prevent respiratory inflammation. Corticosteroids, a class of steroid hormones, are naturally inclined to bind to the glucocorticoid receptor and, in this research, are the basis for exploring other novel and non-intuitive structures. To be more effective than currently available medications, novel compounds must be highly selective toward the lungs and must be inactivated when exposed to the main circulation, thus preventing the participation of the ligand in other systems and consequently reducing systemic side-effects. We look to use the inverse-quantitative structure-activity relationship algorithm with the Signature molecular descriptor to generate new ligands based upon the structures and activities of 65 experimentally studied corticosteroids. Inverse-quantitative structure-activity relationship explore many possible combinations of atom connectivity while structural filters and other scoring approaches are used to predict and identify the most promising candidates for further study. Properties explored include high receptor binding affinity, high systemic clearance, high plasma protein binding and low oral bioavailability. Among more than 300 million potential candidates generated, 84 high priority compounds with properties predicted to be at least as or more effective than currently available corticosteroids have been identified with this procedure.

Data mining PubChem using a support vector machine with the Signature molecular descriptor: classification of factor XIa inhibitors.

The amount of high-throughput screening (HTS) data readily available has significantly increased because of the PubChem project (http://pubchem.ncbi.nlm.nih.gov/). There is considerable opportunity for data mining of small molecules for a variety of biological systems using cheminformatic tools and the resources available through PubChem. In this work, we trained a support vector machine (SVM) classifier using the Signature molecular descriptor on factor XIa inhibitor HTS data. The optimal number of Signatures was selected by implementing a feature selection algorithm of highly correlated clusters. Our method included an improvement that allowed clusters to work together for accuracy improvement, where previous methods have scored clusters on an individual basis. The resulting model had a 10-fold cross-validation accuracy of 89%, and additional validation was provided by two independent test sets. We applied the SVM to rapidly predict activity for approximately 12 million compounds also deposited in PubChem. Confidence in these predictions was assessed by considering the number of Signatures within the training set range for a given compound, defined as the overlap metric. To further evaluate compounds identified as active by the SVM, docking studies were performed using AutoDock. A focused database of compounds predicted to be active was obtained with several of the compounds appreciably dissimilar to those used in training the SVM. This focused database is suitable for further study. The data mining technique presented here is not specific to factor XIa inhibitors, and could be applied to other bioassays in PubChem where one is looking to expand the search for small molecules as chemical probes.

Association patterns in (HF)(m)(H2O)(n) (m + n = 2-8) clusters.

In an attempt to understand the phase behavior of aqueous hydrogen fluoride, the clustering in the mixture is investigated at the molecular level. The study is performed at the mPW1B95/6-31+G(d,p) level of theory. Several previous studies attempted to describe the dissociation of HF in water, but in this investigation, the focus is only on the association patterns that are present in this binary mixture. A total of 214 optimized geometries of (HF)n(H2O)m clusters, with m + n as high as 8, were investigated. For each cluster combination, several different conformations are investigated, and the preferred conformations are presented. Using multiple linear regressions, the average strengths of the four possible H-bonding interactions are obtained. The strongest H-bond interaction is reported to be the H2O...H-F interaction. The most probable distributions of mixed clusters as a function of composition are also deduced. It is found that the larger (HF)n(H2O)m clusters are favored both energetically and entropically compared to the ones that are of size m + n < or = 3. Also, the clusters with equimolar contributions of HF and H2O are found to have the strongest interactions.

Isothermal compressibility maxima of hydrogen fluoride in the supercritical and superheated vapor regions.

The highly nonideal behavior of hydrogen fluoride (HF) vapor has been considered to be the origin of its numerous vapor phase anomalies. In this work, we report one such potential vapor phase anomaly for HF. For a nonassociating substance like propane, the response functions go through a maximum only once in the supercritical region. However, for HF, when an association model is used to predict the isothermal compressibility (KT), it exhibits a maximum in the supercritical region more than once, and this peak extends well in to the superheated vapor region upon decompression. This theoretical prediction is also supported by two other models recently developed for HF. Note that experimental values of KT for HF have not been reported in the literature so far. Preliminary investigations on this KT maximum for HF have suggested no reentrant spinodal, singularity-free scenario, or any additional first-order phase transition, unlike water, and, also, no lambda (or higher-order phase) transitions, unlike liquid helium. However, this KT peak is similar to the experimentally supported heat capacity (CP) peak of HF which extends into the supercritical and superheated vapor regions. Similar to the CP peak, which is understood based on vapor-phase clustering in HF, we relate KT to the derivatives of enthalpy and entropy of the system. Also, we analyze some of the P-v-T experimental data that are available to provide an overview of the KT behavior in the region of interest, and compare them with the model results. Finally, to explore the effect of including a distribution pattern for the oligomers, we report the results on a model that only includes association. Using this approach, we report KT results with and without a Poisson-type oligomer distribution and show that the KT appears once this distribution scheme is specified.

Impulse penetration into idealized granular beds: behavior of cumulative surface kinetic energy.

We report a particle dynamics based simulational study of the propagation of delta function mechanical impulses in idealized three-dimensional hexagonal close packed lattices of monosized Hertz spheres. This paper presents five key results on the kinetic energy of grains at the surface of a granular bed after the generation of a normal impulse into the bed. (i) We find that the time integrated or cumulative average kinetic energy per surface grain, kappa, drops as an impulse penetrates into the bed. The minimum value of kappa, say kappa(0), is reached at some time t=tau after the impulse has been generated. (ii) This value, kappa(0), depends upon the restitutional losses at the grain contacts and kappa(0) increases as restitutional losses at granular contacts increase in magnitude. (iii) The asymptotic value of kappa is denoted by kappa(final) . Our data show that increasing the area across which an impulse is generated, A, leads to kappa(final) proportional to A(-1/2) . (iv) If we assign random masses to our monosized grains, kappa(final) grows quadratically as a function of the range of mass variation about a mean mass. We find that at large times, i.e., t>>tau , kappa proportional to (1-exp [k (1-t/tau)]) , where the constant k is roughly independent of restitution for the typical values of restitution encountered. (v) Our data suggest that at early times, the backscattering process carries signatures of ballistic propagation of the mechanical energy while at late times, the backscattering process is reminiscent of vibrations of an essentially ergodic system. Given the ballisticlike propagation of mechanical energy into granular beds, we conclude that a wave equation based description of mechanical energy propagation into granular beds may not always be appropriate.

The signature molecular descriptor. 3. Inverse-quantitative structure-activity relationship of ICAM-1 inhibitory peptides.

We present a methodology for solving the inverse-quantitative structure-activity relationship (QSAR) problem using the molecular descriptor called signature. This methodology is detailed in four parts. First, we create a QSAR equation that correlates the occurrence of a signature to the activity values using a stepwise multilinear regression technique. Second, we construct constraint equations, specifically the graphicality and consistency equations, which facilitate the reconstruction of the solution compounds directly from the signatures. Third, we solve the set of constraint equations, which are both linear and Diophantine in nature. Last, we reconstruct and enumerate the solution molecules and calculate their activity values from the QSAR equation. We apply this inverse-QSAR method to a small set of LFA-1/ICAM-1 peptide inhibitors to assist in the search and design of more-potent inhibitory compounds. Many novel inhibitors were predicted, a number of which are predicted to be more potent than the strongest inhibitor in the training set. Two of the more potent inhibitors were synthesized and tested in-vivo, confirming them to be the strongest inhibiting peptides to date. Some of these compounds can be recycled to train a new QSAR and develop a more focused library of lead compounds.

The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies.

We present a new descriptor named signature based on extended valence sequence. The signature of an atom is a canonical representation of the atom's environment up to a predefined height h. The signature of a molecule is a vector of occurrence numbers of atomic signatures. Two QSAR and QSPR models based on signature are compared with models obtained using popular molecular 2D descriptors taken from a commercially available software (Molconn-Z). One set contains the inhibition concentration at 50% for 121 HIV-1 protease inhibitors, while the second set contains 12865 octanol/water partitioning coefficients (Log P). For both data sets, the models created by signature performed comparable to those from the commercially available descriptors in both correlating the data and in predicting test set values not used in the parametrization. While probing signature's QSAR and QSPR performances, we demonstrates that for any given molecule of diameter D, there is a molecular signature of height h

The signature molecular descriptor. 2. Enumerating molecules from their extended valence sequences.

We present a new algorithm that enumerates molecular structures matching a predefined extended valence sequence or signature. The algorithm can construct molecular structures composed of about 50 non-hydrogen atoms in CPU seconds time scale. The algorithm is run to produce all molecular structures matching the binding affinities (IC(50)) of some HIV-1 protease inhibitors. The algorithm is also used to compute the degeneracy, or the number of molecular structures, corresponding to a given signature. Signature degeneracy is systematically studied for varying signature heights on four molecular series, alkanes, alcohols, fullerene-type structures, and peptides. Signature degeneracy is compared with similar results obtained with popular topological indices (TIs). As a general rule, we find that signature degeneracy decreases as the signature height increases. We also find that alkanes, alcohols, and fullerene-type structures comprising n non-hydrogen atoms are uniquely characterized by signatures of height n/4, while peptides up to 4000 amino acids can be singled out with signatures of heights as small as 2 and 3.

Developing a methodology for an inverse quantitative structure-activity relationship using the signature molecular descriptor.

The concept of signature as a molecular descriptor is introduced and various topological indices used in quantitative structure-activity relationships (QSARs) are expressed as functions of the new descriptor. The effectiveness of signature versus commonly used descriptors in QSAR analysis is demonstrated by correlating the activities of 121 HIV-1 protease inhibitors. Our approach to the inverse-QSAR problem consists of first finding the optimum sets of descriptor values best matching a target activity and then generating a focused library of candidate structures from the solution set of descriptor values. Both steps are facilitated by the use of signature.