Activity assessment of small drug molecules in estrogen receptor using multilevel prediction model

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model

In this study, efforts are created to develop a quantitative structure-activity relationship (QSAR)-based model, which are used for the prediction of toxicities to reduce testing in animals, time, and money in the early stages of drug development. An efficient machine learning model is developed to predict the toxicity of those drug molecules which binds to the androgen receptor (AR). Toxicity prediction is performed in terms of their activity, activity score, potency, and efficacy by using various physicochemical properties. A multilevel ensemble model is proposed, where its first level is performed ensemble-based classification of activity, and the second level is performed ensemble-based regression of activity score, potency, and efficacy of only those drug molecules which have been found active during the classification level. The AR dataset has 10,273 drug molecules where 461 are active, and 9812 are inactive, and each drug molecule has 1444 features. Therefore, our dataset is highly imbalanced having a very large number of features. Initially, we performed feature selection then the class imbalance problem is resolved. The k-fold cross-validation is accomplished to measure the consistency of the model. Finally, our proposed multilevel ensemble model has been validated and compared with some existing models.