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Abstract

Background

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation..In silico rationally designed of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure for the possible potentiating of the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies. Asymmetric bagging and feature selection for activities prediction of drug molecules.Poor cellular delivery and low bioavailability of novel potent therapeutic molecules continue to remain the bottleneck of modern cancer and gene therapy. Cell-penetrating peptides have provided immense opportunities for the intracellular delivery of bioactive cargos and have led to the first exciting successes in experimental therapy of muscular dystrophies. The arsenal of tools for oligonucleotide delivery has dramatically expanded in the last decade enabling harnessing of cell-surface receptors for targeted delivery. A benchmark dataset, consisting of 3028 drugs assigned within nine categories, was constructed by collecting data from KEGG. These prediction rates are much higher than the 11.11% achieved by random guessResearch and Scientific Project. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups.

Results

Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability..In silico rationally designed of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure for the possible potentiating of the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.Asymmetric bagging and feature selection for activities prediction of drug molecules.

Conclusion

Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets..In silico rationally designed of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure for the possible potentiating of the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.Asymmetric bagging and feature selection for activities prediction of drug molecules. Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-Delivernarex-3308 utilising asymmetric bagging and feature selection of a Peptide-mimic pharmacologic low mass predicted chemorecored poly-druggable-structure in silico designed molecules for potentiating the efficient delivery of gene constructs through for the internalization successes in experimental therapy of muscular dystrophies.

Keywords

Asymmetric, bagging, feature, selection, prediction, in silico, rationally, designed drug molecules, Peptide-mimic, pharmacologic, low mass, chemorecored, poly-druggable,-structure, potentiating, efficient, delivery, gene, constructs, internalization, successes, experimental, therapy, muscular, dystrophies.

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017

Citation

Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) Evaluation of an Inverse Molecular Design Algorithm in a Model Binding Site as An In silico predicted and computer-aided molecular designed HIV-1 protease CTLA-4 blockador for the increasement of the antigen-specific W191G mutant of cytochrome c peroxidase CD8+ T-cells to the inprevaccinated patients with melanoma using new cluster of algorithms for Large-Scale Protein-Ligand Docking experiments.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;

E-mail: biogeneadrug@gmail.com

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