title: Improved machine learning models for predicting selective compounds.
abstract: The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step toward successful drug discovery. However, there is still a lack of efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multitask learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out nonselective compounds. The multitask method incorporates both activity and selectivity models into one multitask model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with those of other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multitask methods significantly improve the selectivity prediction performance.
authors: Department of Computer Science & Engineering, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, USA.
authors: Xia
authors: X
authors: Ning
authors: Michael
authors: M
authors: Walters
authors: George
authors: G
authors: Karypis
authors: George
authors: G
authors: Karypisxy
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D055808:Drug Discovery / Q000379:methods*; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D054852:Small Molecule Libraries / Q000737:chemistry*; D012984:Software
chemical_list: D054852:Small Molecule Libraries