Advancing Drug-Target Interaction prediction with BERT and subsequence embedding

Comput Biol Chem. 2024 Apr 5;110:108058. doi: 10.1016/j.compbiolchem.2024.108058. Online ahead of print.ABSTRACTExploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for ...

Source: Computational Biology and Chemistry - April 9, 2024 Category: Bioinformatics Authors: Zhihui Yang Juan Liu Feng Yang Xiaolei Zhang Qiang Zhang Xuekai Zhu Peng Jiang Source Type: research