PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm.

Combinatorial therapy is a promising strategy for combating complex diseases by improving the efficacy and reducing the side effects. To facilitate the identification of drug combinations in pharmacology, we proposed a new computational model, termed PDC-SGB, to predict effective drug combinations by integrating biological, chemical and pharmacological information based on a stochastic gradient boosting algorithm. To begin with, a set of 352 golden positive samples were collected from the public drug combination database. Then, a set of 732 dimensional feature vector involving biological, chemical and pharmaceutical information was constructed for each drug combination to describe its properties. To avoid overfitting, the maximum relevance & minimum redundancy (mRMR) method was performed to extract useful ones by removing redundant subsets. Based on the selected features, the three different type of classification algorithms were employed to build the drug combination prediction models. Our results demonstrated that the model based on the stochastic gradient boosting algorithm yield out the best performance. Furthermore, it is indicated that the feature patterns of therapy had powerful ability to discriminate effective drug combinations from non-effective ones. By analyzing various features, it is shown that the enriched features occurred frequently in golden positive samples can help predict novel drug combinations.

Design, synthesis and antimicrobial evaluation of novel 1,3-oxazolidin-2- one derivatives.

A series of novel derivatives of 1,3-oxazolidin-2-one 12a-12n has been synthesized starting from 4-nitro-(L)- phenylalanine by involving five-step reaction sequence. All the compounds were screened for their in vitro antibacterial activity against four pathogenic bacterial strains namely, Staphylococcus aureus, Bacillus subtilis (Gram-positive), Escherichia coli, Pseudomonas aeruginosa (Gram-negative) and in vitro antifungal activity against two pathogenic fungal strains namely, Candida albicans and Saccharomyces cerevisiae. All the synthesized compounds showed activity against Gram-positive bacteria. Compounds 12c and 12l exhibited maximum antibacterial activity against Gram-positive bacteria. However, against Gram-negative bacteria only five of screened compounds were found to be active. Compounds 12c and 12i displayed best antifungal activity against the tested fungi. Docking studies were carried out in order to gain insight into the mechanism of action and the binding mode of these compounds. These studies were in agreement with the biological data.