J Chem Inf Model - Improved machine learning models for predicting selective compounds.

Tópicos

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Resumo

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.

Resumo Limpo

identif small potent compound select bind target consider high affin critic step toward success drug discoveri howev still lack effici accur comput method predict compound select properti paper propos set machin learn method compound select predict particular propos novel cascad learn method multitask learn method cascad method decompos select predict two step one model step effect filter nonselect compound multitask method incorpor activ select model one multitask model better differenti compound select properti conduct comprehens set experi compar result convent select predict method result demonstr cascad multitask method signific improv select predict perform

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