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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 \u2019multi-modality attributes\u2019 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.<\/jats:p>","DOI":"10.1093\/bib\/bbad161","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T10:15:54Z","timestamp":1682676954000},"source":"Crossref","is-referenced-by-count":32,"title":["Multi-modality attribute learning-based method for drug\u2013protein interaction prediction based on deep neural network"],"prefix":"10.1093","volume":"24","author":[{"given":"Weihe","family":"Dong","sequence":"first","affiliation":[{"name":"College of information and Computer Engineering, Northeast Forestry University , Hexing Road, 150040, Harbin , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Heilongjiang University , Xuefu Road, 150080, Harbin , 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