Netpredictor: R and Shiny package to perform drug-target network analysis and prediction of missing links.

Background: Netpredictor is an R package for prediction of missing links in any given unipartite or bipartite network. The package provides utilities to compute missing links in a bipartite and well as unipartite networks using Random Walk with Restart and Network inference algorithm and a combination of both. The package also allows computation of Bipartite network properties, visualization of communities for two different sets of nodes, and calculation of significant interactions between two sets of nodes using permutation based testing.

Predicting drug target interactions using meta-path-based semantic network analysis.

Background: In the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction.

Optimizing drug-target interaction prediction based on random walk on heterogeneous networks.

Background: Predicting novel drug-target associations is important not only for developing new drugs, but also for furthering biological knowledge by understanding how drugs work and their modes of action. As more data about drugs, targets, and their interactions becomes available, computational approaches have become an indispensible part of drug target association discovery. In this paper we apply random walk with restart (RWR) method to a heterogeneous network of drugs and targets compiled from DrugBank database and investigate the performance of the method under parameter variation and choice of chemical fingerprint methods.

Tools, resources and databases for SNPs and indels in sequences: a review.

Single Nucleotide Polymorphism (SNP) is a mutation where, a single base in the DNA differs from the usual base at that position. SNPs are the marker of choice in genetic analysis and also useful in locating genes associated with diseases. SNPs are important and frequently occurring point mutations in genomes and have many practical implications.

Enhanced ranking of PknB Inhibitors using data fusion methods.

Background: Mycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis.

In-silico predictive mutagenicity model generation using supervised learning approaches.

Background: Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties.

Docking study of HIV-1 reverse transcriptase with phytochemicals.

Natural products are important sources of drug discovery. In this context groups of different set of phytochemicals were taken and docked into the different cavities of the Reverse transcriptase (PDB ID: 1REV) of Human immunodeficiency virus (HIV) and results were discussed. Natural compounds such as Curcumin, Geranin, Gallotannin, Tiliroside, Kaempferol-3-o-glucoside and Trachelogenin were found to very effective according to its binding energy and ligand efficiency score.