Where c is the class, sc is the set of compounds in c connecting to feature i, and count(k) represents the connectivity of k. The higher the FF, the more compounds in c contain this feature, thus the higher the weight of this feature should be and vice versa. Like the TFIDF model, FF itself is insufficient for a highquality weight feature for classification. Although the IDF could be directly used, in machine learning field, information gain (IG) is more suitable for measuring the amount of information obtained for classification predication by knowing the existing or non-existing of features. The term frequency-based information gain (TFIG) method performed better than the classical TFIDF . Similarly, the "IDF" can be replaced by IG which will be: