Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.
Bibliographical noteKAUST Repository Item: Exported on 2023-05-02
Acknowledged KAUST grant number(s): FCC/1/1976-44-01, FCC/1/1976-45-01, REI/1/5234-01-01
Acknowledgements: The project is supported by the National Natural Science Foundation of China (Nos 81273649, 61501132, 61672181, 62001144, 62272135); the Natural Science Foundation of Heilongjiang Province (Nos LH2019F049, LH2019A029); the China Postdoctoral Science Foundation (No. 2019M650069); the Research Funds for the Central Universities (No. 3072019CFT0603); the Fund for Young Innovation Team of Basic Scientific Research in Heilongjiang Province (No. RCYJTD201805); Fund from China Scholarship Council (CSC) and the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No FCC/1/1976-44-01, FCC/1/1976-45-01 and REI/1/5234-01-01. The authors thank the reviewers for their valuable comments and suggestions.
ASJC Scopus subject areas
- Molecular Biology
- Information Systems