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Small Random Forest Models for Effective Chemogenomic Active Learning
2017
Journal of Computer Aided Chemistry
The identification of new compound-protein interactions has long been the fundamental quest in the field of medicinal chemistry. With increasing amounts of biochemical data, advanced machine learning techniques such as active learning have been proven to be beneficial for building high-performance prediction models upon subsets of such complex data. In a recently published paper, chemogenomic active learning had been applied to the interaction spaces of kinases and G protein-coupled receptors
doi:10.2751/jcac.18.124
fatcat:z4onn42nh5ahnay7232yfswwo4