iNGNN-DTI: prediction of drug–target interaction with interpretable nested graph neural network and pretrained molecule models

Author:

Sun Yan123,Li Yan Yi4ORCID,Leung Carson K2ORCID,Hu Pingzhao1234567ORCID

Affiliation:

1. Department of Biochemistry, Western University , London, ON, N6G 2V4, Canada

2. Department of Computer Science, University of Manitoba , Winnipeg, MB, R3T 2N2, Canada

3. Department of Computer Science, Western University , London, ON, N6G 2V4, Canada

4. Division of Biostatistics, University of Toronto , Toronto, ON, M5T 3M7, Canada

5. Department of Oncology, Western University , London, ON, N6G 2V4, Canada

6. Department of Epidemiology and Biostatistics, Western University , London, ON, N6G 2V4, Canada

7. The Children’s Health Research Institute, Lawson Health Research Institute , London, ON, N6A 4V2, Canada

Abstract

Abstract Motivation Drug–target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization. Results We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the target graphs are created based on 3D structures using Alphafold2. This architecture is highly expressive in capturing substructures of the graph data. We use a cross-attention module to capture interaction information between the substructures of drugs and targets. To improve feature representations, we integrate features learned by models that are pre-trained on large unlabeled small molecule and protein datasets, respectively. We evaluate our model on three benchmark datasets, and it shows a consistent improvement on all baseline models in all datasets. We also run an experiment with previously unseen drugs or targets in the test set, and our model outperforms all of the baselines. Furthermore, the iNGNN-DTI can provide more insights into the interaction by visualizing the weights learned by the cross-attention module. Availability and implementation The source code of the algorithm is available at https://github.com/syan1992/iNGNN-DTI.

Funder

Canadian Institute of Health Research

Natural Sciences and Engineering Research Council of Canada

Publisher

Oxford University Press (OUP)

Reference37 articles.

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