Although we've constructed the drug-target predictor based on the original feature set described above, it is possible to improve its performance with a better feature set. Apparently, not every feature in the feature set is equally relevant to the drug-target interaction. What's more, features may not be independent with each other. The ''bad'' will have negative impact on the accuracy and efficiency of the predictor, so it is possible to do the feature selection process to construct a more compact and effective feature set. The first step is using Maximum Relevance Minimum Redundancy (mRMR) [36] to do feature evaluation. Maximum Relevance Minimum Redundancy (mRMR) [39] was firstly developed for analysis of microarray data. It ranks each feature according to its relevance to the target and redundancy to other features. The better a feature is deemed to be, the higher the rank it will be assigned to. Mutual information (MI), denoted by I to indicate the dependence of two features used to quantify the relevance and redundancy. MI is defined as following: