iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach.

Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug-protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug-protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug-protein pairs might be mispredicted as non-interactive.

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.

Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called "iNR-Drug" was developed.

iEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networking.

With the features of extremely high selectivity and efficiency in catalyzing almost all the chemical reactions in cells, enzymes play vitally important roles for the life of an organism and hence have become frequent targets for drug design. An essential step in developing drugs by targeting enzymes is to identify drug-enzyme interactions in cells. It is both time-consuming and costly to do this purely by means of experimental techniques alone.

iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking.

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown.

iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints.

Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel-drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques.

Predict drug-protein interaction in cellular networking.

Involved with many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, GPCRs (G-protein-coupled receptors) are the most frequent targets for drug development: over 50% of all prescription drugs currently on the market are actually acting by targeting GPCRs directly or indirectly. Found in every living thing and nearly all cells, ion channels play crucial roles for many vital functions in life, such as heartbeat, sensory transduction, and central nervous system response. Their dysfunction may have significant impact to human health, and hence ion channels are deemed as "the next GPCRs".