OBJECTIVE: To predict multi-targets by multi-compounds found in Aconiti Lateralis Radix Praeparata and construct the corresponding multi-compound-multi-target network. METHOD: Based on drug-target relationships of FDA approved drugs, a model for predicting targets was established by random forest algorithm. This model was then applied to predict the targets of Aconiti Lateralis Radix Praeparata and construct the multi-compound-multi-target network. RESULT: The predicted targets of 22 compounds of Aconiti Lateralis Radix Praeparata are validated by literature. Each compound in the established network was correlated with 16. 3 targets on average, while each target was correlated with 4. 77 compounds on average, which reflects the "multi-compound and multi-target" characteristic of Chinese medicine. CONCLUSION: The proposed approach can be used to find potential targets of Chinese medicine.
OBJECTIVE: To predict multi-targets by multi-compounds found in Aconiti Lateralis Radix Praeparata and construct the corresponding multi-compound-multi-target network. METHOD: Based on drug-target relationships of FDA approved drugs, a model for predicting targets was established by random forest algorithm. This model was then applied to predict the targets of Aconiti Lateralis Radix Praeparata and construct the multi-compound-multi-target network. RESULT: The predicted targets of 22 compounds of Aconiti Lateralis Radix Praeparata are validated by literature. Each compound in the established network was correlated with 16. 3 targets on average, while each target was correlated with 4. 77 compounds on average, which reflects the "multi-compound and multi-target" characteristic of Chinese medicine. CONCLUSION: The proposed approach can be used to find potential targets of Chinese medicine.