Drug-target affinity prediction method based on consistent expression of heterogeneous data

• URL: http://arxiv.org/abs/2211.06792v1
• Date: Sun, 13 Nov 2022 02:58:03 GMT
• Title: Drug-target affinity prediction method based on consistent expression of heterogeneous data
• Authors: Boyuan Liu
• Abstract summary: We propose a method for predicting drug-target binding affinity using deep learning models. The proposed model demonstrates its accuracy and effectiveness in predicting drug-target binding affinity on the DAVIS and KIBA datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules. However, traditional experimental methods for discovering potential small drug molecules are labor-intensive and time-consuming. There is currently a lot of interest in building computational models to screen small drug molecules using drug molecule-related databases. In this paper, we propose a method for predicting drug-target binding affinity using deep learning models. This method uses a modified GRU and GNN to extract features from the drug-target protein sequences and the drug molecule map, respectively, to obtain their feature vectors. The combined vectors are used as vector representations of drug-target molecule pairs and then fed into a fully connected network to predict drug-target binding affinity. This proposed model demonstrates its accuracy and effectiveness in predicting drug-target binding affinity on the DAVIS and KIBA datasets.

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