Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination

Abstract

Backgrounds Traditional Chinese medicine and Western medicine combination (TCM-WMC) increased the complexity of compounds ingested. Objective To develop a method for screening hepatotoxic compounds in TCM-WMC based on chemical structures using artificial intelligence (AI) methods. Methods Drug-induced liver injury (DILI) data was collected from the public databases and published literatures. The total dataset formed by DILI data was randomly divided into training set and test set at a ratio of 3:1 approximately. Machine learning models of SGD (Stochastic Gradient Descent), kNN (k-Nearest Neighbor), SVM (Support Vector Machine), NB (Naive Bayes), DT (Decision Tree), RF (Random Forest), ANN (Artificial Neural Network), AdaBoost, LR (Logistic Regression) and one deep learning model (deep belief network, DBN) were adopted to construct models for screening hepatotoxic compounds. Result Dataset of 2035 hepatotoxic compounds was collected in this research, in which 1505 compounds were as training set and 530 compounds were as test set. Results showed that RF obtained 0.838 of classification accuracy (CA), 0.827 of F1-score, 0.832 of Precision, 0.838 of Recall, 0.814 of area under the curve (AUC) on the training set and 0.767 of CA, 0.731 of F1, 0.739 of Precision, 0.767 of Recall, 0.739 of AUC on the test set, which was better than other eight machine learning methods. The DBN obtained 82.2% accuracy on the test set, which was higher than any other machine learning models on the test set. Conclusion The DILI AI models were expected to effectively screen hepatotoxic compounds in TCM-WMC.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

“…Chen et al . 198 , for example, constructed a model using ML to screen for hepatotoxic components among many compounds in TCM.…”

The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine

“…To predict toxicity or side effects, Liu et al used a previously developed framework and a deep learning method to predict toxicity or side effects to evaluate the safety of TCM prescriptions recommended for COVID-19 treatment in China [54]. Chen et al developed a machine learning-based method for screening hepatotoxic compounds in TCM and Western medicine combinations, utilizing algorithms like support vector machines, neural networks, decision trees, and random forests [55]. In recent years, several successful AI models, including DeepHit, FP-ADMET, and ResNet18DNN, have been used to predict the toxicity properties of compounds like hERG, LD50, DILI, Ames mutagenesis, carcinogenesis, skin sensitization, and Tox21 assay endpoints [56].…”

Machine learning in TCM with natural products and molecules: current status and future perspectives

“…Their work helped screen potential hepatoprotectants from natural products. Chen [ 59 ] developed a method for screening hepatotoxic compounds in TCM and Western medicine combinations on the basis of chemical structures by using SVM, neural networks, DT, and RF. Their results showed that RF yielded a classification accuracy of 0.838, which was better than other machine learning methods.…”

“…Natural product development Deep neurol network [47,50], RF [53,54,58,59,93], SVM [51,53,54,57,59,93], DT [59,93], neural network [53,59] RF was better than SVM, neurol network and DT in screening hepatotoxic compounds [59]. RF model is more accurate than SVM and DT in identifying molecular characteristics of natural product compounds with the meridians of TCM [93] Disease diagnosis SVM [10,61,66,[81][82][83], DT [68,[81][82][83], neural network [45, 61-63, 65, 82, 83], RF [61,64,67,82,83], CNN [64,67,[70][71][72][73][74][75][76][77][78]81], RNN…”