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    With the growing popularity of artificial intelligence in drug discovery, many deep-learning technologies have been used to automatically predict unknown drug-target interactions (DTIs). A unique challenge in using these technologies to predict DTIs is fully exploiting the knowledge diversity across different interaction types, such as drug-enzyme, drug-target, drug-path, and drug-structure types. Unfortunately, existing methods tend to learn the specifical knowledge on each interaction type and they usually ignore the knowledge diversity across different interaction types. Therefore, we propose a multitype perception method (MPM) for DTI prediction by exploiting knowledge diversity across different link types. The method consists of a type perceptor and a multitype predictor. The type perceptor learns distinguished edge representations by retaining the specifical features across different interaction types; this maximizes the prediction performance for each interaction type. The multitype predictor evaluates the type similarity between the type perceptor and potential interactions, and a domain gate module is further reconstructed to assign an adaptive weight to each type perceptor. Based on the type preceptor and the multitype predictor, our proposed MPM is proposed to exploit the knowledge diversity across different interaction types for DTI prediction. Extensive experiments demonstrate that our proposed MPM outperforms the state-of-the-art methods in DTI prediction.


    Huan Wang, Ruigang Liu, Baijing Wang, Yifan Hong, Ziwen Cui, Qiufen Ni. Multitype Perception Method for Drug-target Interaction Prediction. IEEE/ACM transactions on computational biology and bioinformatics. 2023 Jun 14;PP

    PMID: 37314917

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