Abstract: The identification of molecular targets is a critical step in the discovery and development process of new drugs.Among large known drug targets,ion channel proteins are the most attractive drug targets,which are closely linked to some diseases such as cardiovascular and central nervous systems.Traditional biological methods have the characteristics of high-cost and time-consuming in mining drug targets.Our work discussed the mining of potential ion channel drug targets based on random forests,which is aimed at speeding up the discovery process of drug targets and saving money.Since the lengths of sequences related to drug targets are diverse,thirteen types of protein encoding features were considered which can transform the protein sequences with distinct lengths into the sequences with same lengths in our study.A feature subset which has better performance in the division between drug targets and non-targets was chosen by numerical experiments and the ensemble learning was introduced to attain prediction models.Our study attains high accuracy by comparison to the developed methods,which plays the critical roles in the mining of new drug targets.

Key words: Ion channel,Random forests,Drug targets,Classifiers,Ensemble learning