| Title: |
Drug–Target Interaction Prediction Based on an Interactive Inference Network. |
| Authors: |
Chen, Yuqi1 (AUTHOR) 21yqchen2@stu.edu.cn, Liang, Xiaomin1 (AUTHOR) 19xmliang@stu.edu.cn, Du, Wei2 (AUTHOR) weidu@jlu.edu.cn, Liang, Yanchun2 (AUTHOR) ycliang@jlu.edu.cn, Wong, Garry3 (AUTHOR) garrygwong@um.edu.mo, Chen, Liang1 (AUTHOR) chenliang@stu.edu.cn |
| Source: |
International Journal of Molecular Sciences. Jul2024, Vol. 25 Issue 14, p7753. 20p. |
| Subject Terms: |
*DRUG repositioning, *ALZHEIMER'S disease, *CONVOLUTIONAL neural networks, *DNA fingerprinting, *LINEAR network coding, *FEATURE extraction, *NANOMEDICINE |
| Abstract: |
Drug–target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug–target interactions (DTIs). In this study, a novel drug–target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction, and output layers. In addition, this study used Morgan and PubChem molecular fingerprints as additional information for drug encoding. The interaction layer in our model simulates the drug–target interaction process, which assists in understanding the interaction by representing the interaction space. Our method achieves high levels of predictive performance, as well as interpretability of drug–target interactions. Additionally, we predicted and validated 22 Alzheimer's disease-related targets, suggesting our model is robust and effective and thus may be beneficial for drug repurposing. [ABSTRACT FROM AUTHOR] |
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| Database: |
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