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Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

Authors: DJEDDI, Warith Eddine; HERMI, Khalil; BEN YAHIA, Sadok; DIALLO, Gayo;

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

Abstract

We proposed an innovative approach for predicting DTIs, leveraging both contextual and local strategies. In terms of contextual information, DTIOG utilizes KGE techniques such as the DistMult model to generate drug and target embedding vectors. These vectors are derived from the knowledge graph, capturing associations and similarities between drugs and targets. Instead, DTIOG uses ProtBERT, a language model that has already been trained on protein sequences, to figure out how amino acids in proteins are put together. These embeddings, obtained through either KGE or ProtBERT, are integrated into the process of prediction. Adding to that, some recent deep learning-based models that have been pre-trained on a large corpus of protein sequences, such as ProtBERT, have been utilized to extract features of the proteins. For example, ProtBERT can be used to provide meaningful, context-aware representations of protein sequences, which are crucial for the accurate identification of lysine glutarylation sites. For the local strategy, DTIOG gets information about drugs by using the RDKit library to turn SMILES representations into molecular fingerprints. The Avalon fingerprint generator identifies specific fragments within the molecular structure, creating numerical representations for each drug. Regarding protein sequences, DTIOG processes them into feature vectors based on amino acid biochemical properties. A sliding window of size 3 categorizes amino acids into groups (i.e., non-polar, polar neutral, acidic, and basic), transforming sequences into numerical representations.

Keywords

ProtBERT, Knowledge graph embedding, COVID-19, Drug-target interaction prediction

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  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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