Moreover, the confidence of this rule is defined as the support of X and Y divided by the support X which is the conditional probability of Y being true under the circumstance of X (Equation ). In classical ACM, all attributes or features are treated equally where each attribute/feature has the same weight. However, the assumption that attributes/features have the same significance within a transaction or within the whole feature space is not always right. In a commercial case, for example, {beer → beef, 4%, 80%} where 4% is the support and 80% is the confidence may be more important than {beer → snack, 5%, 85%} even though the former has a lower support and confidence. This is because generally the first one comes with relatively high profit per unit sale. Nonetheless, this difference of significance is ignored by the classical ACM. Similarly, in the chemical domain, if molecules are represented by features, those descriptors of whether a molecule having Actinide or Lanthanide elements may not be as significant as those encoding the number of 6-membered rings, since most of the time the Actinide or Lanthanide elements are not used in drugs. To discover significant relationships in transactional datasets in a weighted setting, the mining process needs to focus on relationships with features that have significant weights, instead of exploring the huge combinational space for insignificant relationships. An efficient mining method, weighted association rule mining (WARM), is suggested by Cai, Wang and Tao . So every item can be assigned a weight based on its significance, for instance, {beer, 0.2}, {beef, 0.5} and {snack, 0.1}. WARM then derives frequent items and association rules by taking into account those weighting factors. The biggest challenges are: a) how to get the weight information of the items, and b) the invalidation of "downward closure property" (DCP). The DCP principle says that if the itemset is frequent, then all of its subsets should be frequent as well. Given the compounds C1-C6, their features and the weight of features (Table -2), if itemset {84, 85} is frequent, then all of its subsets {84} and {85} must be frequent as well. However, given the straightforward definition of itemset weight (Equation ) and weighted support of itemset WS(is) (Equation ), in WACM the DCP does not hold.