Efficient Compound Activity Prediction Model Based on Bayesian Regularization Method for Candidate Drugs

In breast cancer diagnoses, antagonist compounds are used to resist
$\alpha $
Subtype of Estrogen Receptor (ER
$\alpha$
) bioactivity. However, those compounds are difficult to be obtained in the process of drug screening. In this paper, an efficient bioactivity prediction model is proposed with consideration of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties as an auxiliary prediction to achieve the minimization of the damage caused to human body whereas maximizing the efficacy of estrogen activity inhibitors. In the proposed prediction model, Pearson correlation analysis and Bayesian regularization algorithm under neural network are specifically used to train and analyze the data of bioactivity. To calculate the loss of neural network, cross-entropy and supervise learning are applied. The results show that the accuracy of our proposed compound prediction model can reach up to 92.2%, 94.3%, and 90.2% for Caco2, CYP3A4, and hERG, respectively. Moreover, the Area Under the Curve (AUC) value of our proposed model is 0.9752, which is 13-14% higher than that of SVM method. In addition, the Matthews Correlation Coefficient (MCC) can reach up to 0.93 ± 0.03, 37-40% higher than that of SVM method. Due to correlation analysis and valid data screening, our model can shorten the prediction time by 15%. Due to its advantages in accuracy and speed of prediction, this research can contribute to the upgrading of diagnosis and treatment of breast cancer.