Predicting Drug Molecular Properties Based on Ensembling Neural Networks Models

• Published in: Ji suan ji ke xue Vol. 48; no. 9; pp. 251 - 256
• Main Author: XIE Liang-xu, LI Feng, XIE Jian-ping, XU Xiao-jun
• Format: Journal Article
• Language: Chinese
• Published: Editorial office of Computer Science 01.09.2021
• Subjects: computer aided drug discovery|bioinformatics|model ensembling|deep learning|machine learning
• DOI: 10.11896/jsjkx.200700066
• ISSN: 1002-137X

Artificial intelligence (AI) methods have made great success in predicting chemical properties and bioactivity of drug molecules in the Bioinformatics field.Neural network gains wide applications in the process of drug discovery.However,the shallow neural network (SNN) gives lower accuracy while deep neural networks (DNN) are easy to be overfitting.Model ensembling is expected to further improve the predictive performance of weak learners in traditional machine learning methods.Therefore,it is the first time to apply model ensembling strategy to predict the properties of drug molecules.By encoding molecular structures,the combination strategies,averaging,and stacking methods are adopted to increase predicting accuracy of pKa of drug molecules.Compared with DNN,the stacking strategy presents the best predictive accuracy and the Pearson coefficient reaches to 0.86.Ensembling weak learners of the neural networks can reproduce the accuracy of DNN while keeping the satisfied generalization ability.The results show