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.

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.KIF20A (RAB6KIFL) belongs to the kinesin superfamilyof motor proteins, which play critical roles in the traffickingof molecules and organelles during the growth of pancreatic cancer.Immunotherapy using a previously identified epitope peptide forKIF20A is expected to improve clinical outcomes. A phase I clinicaltrial combining KIF20A-derived peptide with gemcitabine (GEM) was therefore conducted among patients with advancedpancreatic cancer who had received prior therapy such as chemotherapyand/or radiotherapy. Despite, huge importance of the field, no dedicated AVP resource is available. In the present Research Scientific Project , we have collected 1245 peptides with antiviral activity targeting important human viruses like influenza, HIV, HCV and SARS, etc. After removing redundant peptides, 1056 peptides were divided into 951 training and 105 validation data sets. We have exploited various peptides sequence features, i.e. motifs and alignment followed by amino acid composition and physicochemical properties during 5-fold cross validation using Support Vector Machine. Physiochemical properties-based model achieved maximum 85% accuracy and 0.70 Matthew’s Correlation Coefficient (MCC). Therefore, AVPpred—the first web server for predicting the highly effective AVPs would certainly be helpful to researchers working on peptide-based antiviral development. The web server is freely available at http://crdd.osdd.net/ servers/avpp. Here, in Biogenea we have discovered for the first time an in silico KIF20A-derived Peptide mimic designed poly-chemo-pharmacophoric macroscaffold as a future super-antagonist for the treatment of PatientsWith Advanced Pancreatic Cancer.An in silico KIF20A-derived Peptide agonistic mimicking sited and computer-aided designed poly-chemo-scaffold as an innovative drug-like molecule comprising potential clinical hyper-inhibitor properties in Patients With Advanced Pancreatic Cancer when combined with Gemcitabine.Asymmetric bagging and feature selection for activities prediction of drug molecules.

Keywords

Asymmetric, bagging, feature, selection, activities, prediction, in silico, KIF20A-derived, Peptide, agonistic, drug molecules, mimicking, computer-aided, designed, poly-chemo-scaffold, innovative, drug-like, molecule, comprising, potential, clinical, hyper-inhibitor, Advanced Pancreatic Cancer, Gemcitabine.