93 compounds which can permeate the placenta barrier were collected as data set for the construction of support vector regression (SVR) model. Besides, 140 compounds with reproductive toxicity and 170 compounds with no reproductive toxicity were collected as another data set for the construction of support vector classification (SVC) model. 1481 molecular descriptors were calculated to represent the structure characteristics of all the compounds mentioned above by Dragon2.1. CfsSubsetEval valuation method and BestFirst-D1-N5 searching method were used to optimize the subset of molecular descriptors. Then based on the above data, SVR model for prediction the placenta barrier permeability (PBP) and SVC model for prediction the reproductive toxicity were built respectively by using LibSVM program. Both the SVR model and the SVC model obtained better prediction ability. The correlation coefficient (R 2) values of the training set and test set of the optimal SVR model were 0.990 and 0.780. The accuracy, sensitivity, and specificity values of the optimal SVC model were all above 80%. Subsequently, the SVR model was utilized to predict the PBP of the compounds which were collected from 13 commonly used tocolytic Chinese herbs. The compounds with higher permeability were further studied by the SVC model and 15 compounds were classified as positive compounds with reproductive toxicity. The two models constructed in this study might be employed in guiding the application of the tocolytic Chinese herbs in clinical.

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The computational approaches of support vector machine (SVM), support vector regression (SVR) and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 (CYP1A2) inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R 2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biological activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identified to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding affinity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug interactions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy.

Quantitative structure-activity relationship (QSAR) indicates the relationship between structural property and biological activity of compounds, which are widely used in the field of pharmacy, materials science, agronomy, environment, etc. 1,2. The basic construction procedures of QSAR model include four important steps.Firstly, as the structural description of the training set compounds is recorded, this structural information and relative biological activity are used to construct correlation function model by suitable algorithm. Afterwards, statistical methods are applied for internal validation of the model. Finally, the test set is used for external test of the model. Reliable QSAR model should be satisfied with the requirement of internal validation and external validation 3,4. The main purpose of QSAR model is to predict the activity of compounds, guide compound design and optimize leads. Classification of QSAR methodologies: Different classification methods of QSAR were presented to make the study more reliable and credible. The two main methods, introduced as following, have been widely accepted by QSAR researchers.

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