Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models.

ABSTRACT: Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering various property endpoints, the predictive capacity and computational efficiency of the prediction models developed by eight machine learning (ML) algorithms, including four descriptor-based models (SVM, XGBoost, RF and DNN) and four graph-based models (GCN, GAT, MPNN and Attentive FP), were extensively tested and compared. The results demonstrate that on average the descriptor-based models outperform the graph-based models in terms of prediction accuracy and computational efficiency. SVM generally achieves

SUBMITTER: Jiang D 

PROVIDER: S-EPMC7888189 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature