qnhu@whu.edu.cn.

The EC numbers represent enzymes and enzyme genes (genomic information), but they are also utilized as identifiers of enzymatic reactions (chemical information). In the present work (ECAssigner), our newly proposed reaction difference fingerprints (RDF) are applied to assign EC numbers to enzymatic reactions. The fingerprints of reactant molecules minus the fingerprints of product molecules will generate reaction difference fingerprints, which are then used to calculate reaction Euclidean distance, a reaction similarity measurement, of two reactions. The EC number of the most similar training reaction will be assigned to an input reaction. For 5120 balanced enzymatic reactions, the RDF with a fingerprint length at 3 obtained at the sub-subclass, subclass, and main class level with cross-validation accuracies of 83.1%, 86.7%, and 92.6% respectively. Compared with three published methods, ECAssigner is the first fully automatic server for EC number assignment. The EC assignment system (ECAssigner) is freely available via: http://cadd.whu.edu.cn/ecassigner/.

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.