Parham reaction is very important route for the synthesis of heterocyclic compounds, which consists of the intramolecular reaction of aryllithiums generated by lithium–halogen exchange with different types of internal electrophiles.¹ In this paper we collected a dataset of >100 reactions for many substrates and internal electrophiles (mainly, amides and esters) with a wide range of reaction yields (0 – 99%). The reactions have been carried out in many different experimental conditions with different values non-structural variables (δ_k) like: temperature of addition, addition time, organolithium equivalents, reaction times, and reaction temperature. Next, we calculated many structural and/or physicochemical variables (V_k) for the substrates and products of the reaction. After that, we constructed a Quantitative Structure-Reactivity Relationship (QSRR) model² able to predict the yield of reaction under many different conditions with acceptable accuracy. We also carried a 10.000-points simulation of the reaction conditions.

References

1. a) Ruiz, J.; Sotomayor, N.; Lete, E. Org. Lett. 2003, 5, 1115. b) Ruiz, J.; Ardeo, A.; Ignacio, R.; Sotomayor, N.; Lete, E. Tetrahedron 2005, 61, 3311. For a review, see: Sotomayor, N.; Lete, E. Curr. Org. Chem. 2003, 7, 275.

2. For related examples of our work; see: a) Blázquez-Barbadillo, C.; Aranzamendi, E.; Coya, E.; Lete, E.; Sotomayor, N.; González-Díaz, H. RSC Adv. 2016; 6, 38602. b) Aranzamendi, E.; Arrasate, S.; Lete, E.; Sotomayor, N.; González-Díaz, H. ChemistryOpen 2016, http://dx.doi.org/10.1002/open.201600120.

The prediction of the antagonistic activity on the receivers of the Angiotensin II (AII) for diverse compounds, using molecular describers of topologic order calculated with the software DRAGON, allowed generate 81 independent variables. A total of 202 compounds divided in two series was used: one of training that included 176 compounds, with 41 compounds in the active group and 135 in the inactive one; and a second serie of prediction, integrated by 26 compounds, of which 7 are considered active and 19 take part in the inactive one. After the carry out of the model's validation, were achieved a 97.73% of good classification for the training serie and a 96.15% of good total classification for the prediction one. The later evaluation in the developed pattern of structures with new molecular entities, that were obtained by molecular modification, showed that 4 of them could be potentially active. The results demonstrated that the factor to modify is the alone since lipophilic property is allowed practically to subtract carbons in the chain carbon atoms and to maintain the activity, not happening this if they modify the heterocyclic systems, what seems to indicate that the same ones are part of the pharmacophore . Comparison settled down with other reported models, using different calculation ways, demonstrated the superiority of the methodology developed in our work.For the development of new drugs, the discovery of new series heads to considered like possible active agents that blockade the receiving AT1 of the angiotensin II is a promissory alternative that opens up to the generation of new libraries of compounds that facilitate the virtual sifted.

The calculation methodology MODESLAB was used to modelate the anticoagulant activity of different drugs. The spectral moments of the adjacent matrix were determinated using different parameters, between the edges of the molecular graph with suppressed hydrogens, leading to the classification in active or inactives a total of 985 compounds in the main diagonal. The calculated descriptors were employed in a serie of training, as well as in a prediction one, in order to obtain and evaluate the model, respectively. A discriminant function for the anticoagulant activity was defined by the use of the training serie, leading to a good total classification of 92.29%. The external prediction one, with a total of 146 compounds, was used to validate the model, leading to a good total classification of 95.89%. The links´s contribution to the activity (understructural analysis) allowed the identification of the positive isocontribution´s zones or pharmacophere, as well as the negative isocontribution´s ones, that can functionate as groups of transport for the involved molecules; which gives us an idea of the sites that can interact with a determinated receptor, as well as those that facilitate the drug´s arriving to its site of action

The prediction of immunogenic peptides that can be used for production of antipeptide antibodies is of great importance for design of vaccines however a problem in immunology is the impact on the immunological response after of a perturbation or variation in the sequence of a known peptide and/or other boundary conditions. Methods that establish mathematical models to identify the structure-Activity/Property (QSAR/QSPR) relationships have been developed in the past. On the other hand, Epitope Data- base (IEDB) http://www.iedb.org/, released public data useful for these studies. Specifically, Perturbation Theory QSAR method (PT-QSAR) has been used to predict B-epitopes from IEDB database. This method adds variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. In this specific case, the method predicts the epitope activity E_q(c_j) of one query peptide (q) in a set of experimental conditions (c_j). In so doing, the method uses as input the epitope activity E_r(ʹc_j) of one similar peptide already known that is used as peptide of reference (r); which have been assayed on the same or a different set of experimental conditions (ʹc_j). The method also uses as input the information about the sequences and conditions of assay of both peptides in the pair. In the present study we developed a model able to classify 500000 cases of perturbations with accuracy, sensitivity to 99%, and specificity 100% for training validation series. The perturbations include structural changes in 83683 peptides determined in experimental assays with boundary conditions involving 1448 epitope organism name, 2283 host organisms, 15 biological process, 28 experimental techniques and 505 possible adjuvants. The model may be useful for the prediction and optimization in silico of new epitopes under different boundary conditions for vaccine development [1–4].

References and Notes

1. B. Peters, J. Sidney, P. Bourne et al., “The immune epitope database and analysis resource: from vision to blueprint,” PLoS Biology, vol. 3, no. 3, p. e91, 2005.
2. P. Wang, A. A. Morgan, Q. Zhang, A. Sette, and B. Peters, “Automating document classification for the Immune Epitope Database,” BMC Bioinformatics, vol. 8, article 269, 2007.
3. H. Gonzalez-Diaz, S. Arrasate, A. Gomez-San Juan et al., “New theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry,” Current Topics in Medicinal Chemistry, 2013.

The ubiquitin-proteasome system (UPS) plays an important role in the degradation of cellular proteins and regulation of different cellular processes that include cell cycle control, proliferation, differentiation, and apoptosis. In this sense, the disruption of proteasome activity leads to different pathological states linked to clinical disorders such as: inflammation, neurodegeneration, and cancer. The use of UPP inhibitors is one of the proposed approaches to manage these alterations. On other hand, the ChEMBL database contains >5000 experimental outcomes for >2000 compounds tested as possible proteasome inhibitors using a large number of pharmacological assay protocols. All these assays report a large number of experimental parameters of biological activity like EC₅₀, IC₅₀, percent of inhibition, and many others that have been determined under many different conditions, targets, organisms, etc. Although this large amount of data offers new opportunities for the computational discovery of proteasome inhibitors, the complexity of these data represents a bottleneck for the development of predictive models. In this work, we used linear molecular indices calculated with the software TOMOCOMD-CARDD (TC) and Bob-Jenkins moving average operators to develop a multi-output model that can predict outcomes for 20 experimental parameters in >450 assays carried out under different conditions. This generated multi-output model showed values of accuracy, sensitivity, and specificity above 70% for training and validation series. Finally, this model is considered multi-target and multi-scale, because it predicts the inhibition of the UPP for drugs against 22 molecular or cellular targets of different organisms contained in the ChEMBL database

In this work, the modeling of inhibitory grown activity against Tetrahymena pyriformis is described. The 0-2D Dragon descriptors based on structural aspects to gain some knowledge of factors influencing aquatic toxicity are mainly used. Besides, it is done by an enlarged data of phenol derivatives describe for the first time. It overcomes the previous datasets with about one hundred compounds. Moreover, the results of the model evaluation by the parameters in the training, prediction and validation provide adequate results comparable with those of the previous works. The more influential descriptors involved in the model are: X3A, MWC02, MWC10 and piPC03 with positive contributions to the dependent variable; and MWC09, piPC02 and TPC with negative influences. In a next step, a median-size database of nearly 8,000 phenolic compounds extracted from ChEMBl was evaluated with the quantitative-structure toxicity relationship (QSTR) model developed providing some clues (SARs) for identification of ecotoxicological compounds. The outcome of this report are very useful to screen chemical databases in use for finding the compounds responsible of aquatic contamination in the biomarker used in the current work.

Alzheimer’s disease (AD) is the most common neurodegenerative disorder among older people, characterized by an insidious onset and a progressive decline of cognitive functions. Nowadays, there is no cure for AD mainly because its etiology is still unclear and current diagnostic tests show great limitations, including low sensitivity and specificity, as well as the impossibility to detect characteristic symptoms at early stages of disease. Thus, the main objective of this work was the optimization of complementary metabolomic approaches based on mass spectrometry in order to investigate AD pathogenesis and discover potential biomarkers for diagnosis. With the aim to get a comprehensive metabolome coverage, multiple analytical platforms were developed, including screening procedures based on direct mass spectrometry analysis and hyphenated approaches with orthogonal separation mechanisms such as liquid chromatography, gas chromatography and capillary electrophoresis. The application of these techniques to serum samples from patients suffering from Alzheimer’s disease and mild cognitive impairment enabled the identification of numerous metabolic alterations linked to pathogenesis of this disorder and its progression from pre-clinical stages, including abnormalities in the composition of membrane lipids, deficits in energy metabolism and neurotransmission, and oxidative stress, among others. Accordingly, it could be concluded that the combination of complementary metabolomic platforms allows studying etiology associated with Alzheimer’s disease in a deeper manner. See also slide presentation: https://sciforum.net/editor/submission/file/download/a92bcde043cd79bece22fe64e94831f8/slides

Analysis of DNA and protein has become a very important aspect in the field of research, especially for Bioinformatics. This is important as the basic analysis of these protein and DNA can lead to further advanced analysis of the sequence, which may lead to new discoveries. Basic analysis of sequences is done in the industry, research as well as education. R language is a statistical program that is used in the analysis of DNA and protein sequences, through the application of packages in the Comprehensive R Archive Network. This analysis package helps to analyze sequences, but in a command prompt analysis. However, the process is slow as the researcher has to enter several lines of codes to obtain the result for the analysis. The research is to develop a prototype web application package with an interactive new interface for the DNA and protein analysis. The prototype is fully coded in R with options to download the results as well as providing information about the codes being used for the analysis and the package reference. This application is made to assist in the sequence analysis of DNA and protein without having to write the codes. 

In this work, we introduce by the first time a new type of algorithm aimed to predict the more promising topology of one ANN to be trained in order to model a given dataset of complex system. In so doing, we can quantify topological (connectivity) information of both the complex networks under study and a set of ANNs trained using Shannon measures. Using information parameters as inputs, we developed one scheduler for 338050 outputs of 10 different ANNs for the respective 33805 pair of nodes in 73 Biological Networks. The overall accuracy of the SANN-HPC schedulers found was of >72% for Biological Networks; in training and validation series.

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