MD-GNN: A mechanism-data-driven graph neural network for molecular properties prediction and new material discovery

Abstract

Molecular properties prediction and new material discovery are significant for the pharmaceutical industry, food, chemistry, and other fields. The popular methods are theoretical mechanism calculation and machine learning. There is a deviation between the theoretical mechanism calculation results and the experimental data. Machine learning method provides a promising solution. However, the process is lack of interpretability, and the reliability and the generalization depend on the training data. In this paper, a mechanism correction model combined with graph neural network (GNN) model which is based on the fusion of graph embedding and descriptors vector is proposed as backbone network to proceed molecule properties prediction and new material discovery. The molecular structure is input to graph neural network and the abstracted features are fused with numerical features together for training. The experiment data and computing data are designed as label constructor, and then the theoretical computation (mechanism driven model) is fused with the output of GNN (data-driven model) to form a fused model to modulate the output for the molecular property prediction. Experiments for public data set are executed and the results show that Mechanism-Data-Driven Graph Neural Network (MD-GNN) can effectively make the predicted results more accurate. Nineteen molecules by different construction are designed for potential drug discovery, the prediction from the proposed MD-GNN model shows that there are 9 candidates are discovered.

Keywords: Graph neural network; Machine learning; Mechanism-data-driven; Molecule properties prediction; New material discovery.