Objective: To study the accurate curative effect of traditional Chinese medicines (TCM) compound Jinfukang in treating lung cancer

and optimize the prescription composition to inhibit lung cancer cell proliferation

so as to provide a new approach for the optimization of TCM compound prescription. Method: Screening test design (Plackett-Burman) and random forest algorithm were applied in establishing the prediction model based on design prescription set. With 61 composition design matrixes as the input

and natural logarithm of their corresponding IC50 as the output

random forest regression model was establish. The optimization model parameter mtry was verified by 5-time and 10-fold interaction. Finally

a grid search algorithm was applied to get the optimal group

and verify the lung cancer cell proliferation experiment. With the IC50 obtained from different formula of Jinfukang on A549 lung adenocarcinoma cancer cells as an index

the composition of Jinfukang were optimize to inhibit lung cancer cell proliferation. Result: Through random forest model combined with grid search algorithm

we got the optimal compatible herbs

namely Astragali Radix

Ophiopogonis Radix

Paridis Rhizoma

Ligustri Lucidi Fructus and Gynostemmatis Pentaphylli Herba seu Radix. Through the experiment

optimized Jinfukang showed a better effect in cell inhibiting and proliferation than original Jinfukang in the aspects of inhibition of lung cancer cell proliferation. Conclusion: The random forest model combined with the grid search algorithm complexity can provide methodology reference for the optimization of composition of complex TCM compound formula.