Ilp Based Screening Applied To Predicting Carbonic Anhydrase Ii Ligands

Machine learning has been often used for drug discovery in recent years. Inductive logic programming (ILP), that can express common features of each data in a qualitative, is one of the machine learning methods. The advantage of ILP is that the classification model is clear compared with other machine learning methods such as Support Vector Machine (SVM) because ILP classifies inhibitors using generated rules. ILP is allowed to learn structure of the compounds so that we can draw the common structure of the ligands from the generated rules. In this method, the data of ligands and decoys are collected from "A Database of Useful Decoys: Enhanced" (DUD-E). ILP provides classification model called a rule by learning these data. This study applies the ILP algorithm to the virtual screening of inhibitors of carbonic anhydrase II (CAH2). We demonstrate its performance by classifying ligands and decoys which aren't included in DUD-E. Our results show that ILP has the performance equivalent to SVM which is known for its high classification performance. In addition, this paper shows that ILP can derive rules explaining structural features of CAH2 ligands. Several of these rules are consistent with known property of CAH2 ligand. This paper demonstrate that ILP has high classification performance and clear classification model. Our method is useful for generating rules for ligand design.