Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification

Abstract

Performance prediction based on candidates and screening based on predicted performance value are the core of product development. For example, the performance prediction and screening of equipment components and parts are an important guarantee for the reliability of equipment products. The prediction and screening of drug bioactivity value and performance are the keys to pharmaceutical product development. The main reasons for the failure of pharmaceutical discovery are the low bioactivity of the candidate compounds and the deficiencies in their efficacy and safety, which are related to the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the compounds. Therefore, it is very necessary to quickly and effectively perform systematic bioactivity value prediction and ADMET property evaluation for candidate compounds in the early stage of drug discovery. In this paper, a data-driven pharmaceutical products screening prediction model is proposed to screen drug candidates with higher bioactivity value and better ADMET properties. First, a quantitative prediction method for bioactivity value is proposed using the fusion regression of LGBM and neural network based on backpropagation (BP-NN). Then, the ADMET properties prediction method is proposed using XGBoost. According to the predicted bioactivity value and ADMET properties, the BVAP method is defined to screen the drug candidates. And the screening model is validated on the dataset of antagonized Erα active compounds, in which the mean square error (MSE) of fusion regression is 1.1496, the XGBoost prediction accuracy of ADMET properties are 94.0% for Caco-2, 95.7% for CYP3A4, 89.4% for HERG, 88.6% for hob, and 96.2% for Mn. Compared with the commonly used methods for ADMET properties such as SVM, RF, KNN, LDA, and NB, the XGBoost in this paper has the highest prediction accuracy and AUC value, which has better guiding significance and can help screen pharmaceutical product candidates with good bioactivity, pharmacokinetic properties, and safety.

Figure 1

The pharmaceutical products screening model flowchart.

Figure 2

Flow chart of LGBM.

Figure 3

BP-NN method structure diagram.

Figure 4

Molecular descriptor correlation heat map.

Figure 5

LGBM feature extraction of the top 20 molecular descriptors.

Figure 6

The loss value of each training round.

Figure 7

Six methods' prediction accuracy of ADMET properties: XGBoost, SVM, RF, LDA, KNN, and NB.

Figure 8

Comparison of comprehensive performance of six prediction methods against five ADMET properties: (a) six prediction methods' ROC curve for Caco-2, (b) six prediction methods' ROC curve for CYP3A4, (c) six prediction methods' ROC curve for hERG, (d) six prediction methods' ROC curve for HOB, and (e) six prediction methods' ROC curve for MN.