Motivation Machine learning methods for predicting compounds protein interactions (CPIs) are crucial for the in-silico screening step of drug discovery. In recent years , many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms. Much effort has been paid on improving the capability of the model by taking advantage of neural attention mechanism , either in learning compound representation, or learning the interaction between protein representation and compound representation . However, seldom has been done to improve the protein representation learning, while current approaches have manifest flaw of lacking the ability of learning amino acids long-distance interactions , which are essential for determine the proteins properties due to protein folding. Results The authors propose a novel approach for incorporating self-attention in the protein representation learning module of CPI models, providing the module with the capability of capturing long-distance interaction information within proteins. And such approach can be universally applied to nearly any deep learning models or submodels for protein representationlearning. By applying such approach on an existing CPI model, the modified version, with our proposed Protein Transformer as its protein learning module, has a significant improvement in the prediction performance . Practical tips for training Protein Transformer are also provided .
The prediction of protein interactions (CPIs) is crucial for the in-silico screening step in drug discovery. Recently , many end-to-end representation learning methods using deep neural networks have achieved significantly better performance than traditional machine learning algorithms. Much effort has focused on the compound representation or the information extraction from the compound-protein interaction to improve the model capability by taking the advantage of the neural attention mechanism . However, previous studies have paid little attention to representing the protein sequences, in which the long-range interactions of residue pairs are essential for characterizing the structural properties arising from the protein folding. We incorporate the self-attention mechanism into the protein representation module for CPI modeling, which aims at capturing the long-range interaction information within proteins. The proposed module concerning protein representation, called Protein Transformer, with an integration with an existing CPI model, has shown a significant improvement in the prediction performance when compared with several existing CPI models .
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Motivation Machine learning methods for predicting compounds
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focused on the compound representation or the information extraction from the compound-protein interaction to improve the model capability by taking the advantage of the
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, either in learning compound representation, or learning the interaction between protein representation and compound representation
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seldom has been done to improve the protein representation learning, while current approaches have manifest flaw of lacking the ability of learning amino acids long-distance interactions , which
previous studies have paid little attention to representing the protein sequences, in which the long-range interactions of residue pairs
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determine the proteins properties due to
characterizing the structural properties arising from the
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Results The authors propose a novel approach for incorporating
We incorporate the
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mechanism into
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learning module of CPI models, providing the module with the capability of capturing long-distance
module for CPI modeling, which aims at capturing the long-range
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And such approach can be universally applied to nearly any deep learning models or submodels for protein representationlearning. By applying such approach on an
The proposed module concerning protein representation, called Protein Transformer, with an integration with an
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the modified version, with our proposed Protein Transformer as its protein learning module, has
has shown
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when compared with several existing CPI models