Abstract: 【Purpose/significance】Excavate the tacit knowledge in the screening of medicines, replace biological experimental meth?
ods with the prediction ability of machine learning(ML), and reduce R&D period and economic cost of pharmaceutical process.
【Method/process】This paper proposes ADMET intelligence prediction theoretical framework for knowledge discovery and four tradi?
tional machine learning methods and two ensemble learning methods are used to construct Six classification prediction models. We ex? tract the tacit knowledge, compare the advantages of different models, and evaluate the economic value of the optimal model.【Result/conclusion】It is feasible to predict ADMET with the molecular descriptor information. The comprehensive ranking results of the six models are RF, GBDT, LR, SVM, KNN and GNB. The cutting-edge information technologies can be effectively applied to the drug
knowledge discovery. Information economics analysis can predict a positive revenue, which is an important means to reduce costs and
increase efficiency of pharmaceutical processes in the future.【Innovation/limitation】We should integrate expert knowledge, add ex?
perimental verification and enrich reference indicators later.