Journal Article10.1093/bib/bbad161
Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network
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TL;DR: In this paper , a multi-modality attributes learning-based framework for drug-protein interaction prediction with molecular transformer and graph convolutional networks was proposed, which extracted intermolecular sub-structural information and chemical semantic representations from biomedical data.
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Abstract: 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.
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Citations
Revisiting Drug-Protein Interaction Prediction: A Novel Global-Local Perspective.
Zhecheng Zhou,Qingquan Liao,Jinhang Wei,Linlin Zhuo,Xiaonan Wu,Xiangzheng Fu,Quan Zou +6 more
TL;DR: A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model, and is expected to offer valuable insights for furthering drug repurposing and personalized medicine research.
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Multimodal Fused Deep Learning for Drug Property Prediction: Integrating Chemical Language and Molecular Graph
Xiaohua Lu,Liangxu Xie,Lei Xu,Rongzhi Mao,Xiaojun Xu,Shan Chang +5 more
TL;DR: The proposed multi-modal fused deep learning model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability and shows the ability to acquire complementary information by using proper models and suitable fusion approaches.
GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction
Huishuang Tan,Zhixin Wang,Guang Hu +2 more
TL;DR: A geometry-aware attention-based deep learning model, GAABind, is proposed, which effectively predicts the pocket–ligand binding pose and binding affinity within a multi-task learning framework and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.
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DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.
Xiaokun Li,Qiang Yang,Longpeng Xu,Weihe Dong,Gongning Luo,Wei Wang,Suyu Dong,Kuanquan Wang,Ping Xuan,Xianyu Zhang,Xin Gao +10 more
TL;DR: The downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment, and achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods.
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Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method
Shujuan Yang,Mei Bai,Weichi Liu,Weicheng Li,Zhi Zhong,Lai‐Yu Kwok,Gaifang Dong,Zhihong Sun +7 more
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