机器学习在抗乳腺癌候选药物预测模型中的应用

Application of Machine Learning in the Predictive Model of Anti-Breast Cancer Drug Candidates

为了提高抗乳腺癌候选药物特征筛选与模型预测的准确性,提出一种新的多种组合特征筛选方法对抗乳腺癌候选药物——雌激素受体α亚型(ERα)的分子描述符进行特征筛选,并根据筛选的分子描述符构建化合物活性预测(QSAR)模型。采用1DCNN算法模型预测化合物活性,该模型的RMSE、MAE、MAPE评价指标值分别为0.40、0.41和0.08,相比传统随机森林和支持向量机算法的预测效果提高了10%。基于多种组合的特征筛选方法和1DCNN模型预测为药物化合物特征筛选与活性预测提供了新思路,后续可用于其他药物化合物的预测。

In order to improve the accuracy of feature screening and model prediction of anti-breast cancer drug candidates, propose a new method for screening multiple combinations of features for anti-breast cancer drug candidates——estrogen receptors alpha(ERα) molecular descriptors to perform feature screening, and to construct compounds based on the molecular descriptors screened Activity prediction(QSAR) model. In this study, the RMSE, MAE, and MAPE of the phase model of the 1DCNN algorithm are 0.40, 0.41 and 0.08, respectively. Compared with the traditional random forest and support vector machine algorithm, the prediction effect is improved by 10%. Feature screening methods based on multiple combinations and 1DCNN model prediction provide new ideas for feature screening and activity prediction of pharmaceutical compounds, which can be subsequently used for other pharmaceutical compound predictions.