We performed a comparative assessment of the proposed methods on four different AbMole Gambogic-acid drug-target interaction networks from humans involving enzymes, ion channels, GPCRs and nuclear receptors. Results indicated that WNN is competitive in predicting interaction for unknown drug compounds with more involved machine learning methods recently proposed, notably a fully probabilistic method based on a Bayesian formulation that combines kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification. Furthermore we showed that the direct integration of WNN in a recent kernel based machine learning method provides a general and powerful tool for finding drug-target interactions. A limitation of our approach is that it does not make a difference between an inactive target and a target that has not been measured for a compound. Compounds with a higher mutual chemical similarity also have a higher chance of having the same bioactivity. This information could be considered by WNN by determining directly the weights from the similarity, instead of using the proposed ranking-based decay mechanism. In this way all the compounds with high similarity would be considered with a high weight and all the compounds with low similarity would only have a minor contribution to the final predicted profile. On the same reasoning there is also a similarity threshold from where the chance is so low that two compounds have the same profile that it would be better not to predict something in the first place. In particular for new screening data from very large 
screening libraries chances are high that none of the references are really similar to the screening hits, which would most likely have a detrimental effect in the overall prediction performance, if predictions would be made for all such compounds. Many published target prediction algorithms apply such “applicability domain” or confidence estimations for their predictions. WNN could be modified to address this issue for instance by including a binary annotation based on a similarity threshold, or a more advanced procedure based on the similarities of all compounds considered for the generation of the profile. The discovery and subsequent development of novel chemical entities into anti-cancer drugs are one of the major goals of oncologists. Compounds from traditional Chinese medicine have been examined for their anti-cancer potential in recent decades, and for those compounds that do exhibit such potential, the delineation of their mechanism of action may have an enormous influence on the development of new strategies in cancer therapies. Evodiamine is one of the important components of Chinese herb Wu-Chu-Yu. Numerous reports have revealed the effects of evodiamine including anti-angiogenesis, anti-tumor growth, anti-invasive and metastatic activities, up-regulating apoptosis anti-allergic effects, and anti-inflammatory effects.