Breast cancer drug candidate screening based on ensemble learning algorithm
Zhuang Wang, Yu Cao, Chengyin Ye · 2023
For the current machine learning prediction model, there are problems such as low prediction accuracy of the pharmacokinetic properties of breast cancer candidate drugs, too many parameters of the neural network prediction model, and long training time. In this paper, we propose a lightweight multi-layer perceptual ensemble learning model based on imbalanced datasets to classify the pharmacokinetic properties of drug candidates. Experiments have proved that the proposed model is about 7% more accurate than the traditional machine learning model and 3% more accurate than the current advanced mainstream neural network model under the same public data set.