Fast rule-based bioactivity prediction using associative classification mining

Relating chemical features to bioactivities is critical in molecular design and is used extensively in the lead discovery and optimization process. A variety of techniques from statistics, data mining and machine learning have been applied to this process. In this study, we utilize a collection of methods, called associative classification mining (ACM), which are popular in the data mining community, but so far have not been applied widely in cheminformatics. More specifically, classification based on predictive association rules (CPAR), classification based on multiple association rules (CMAR) and classification based on association rules (CBA) are employed on three datasets using various descriptor sets. Experimental evaluations on anti-tuberculosis (antiTB), mutagenicity and hERG (the human Ether-a-go-go-Related Gene) blocker datasets show that these three methods are computationally scalable and appropriate for high speed mining. Additionally, they provide comparable accuracy and efficiency to the commonly used Bayesian and support vector machines (SVM) methods, and produce highly interpretable models.