Modality-DTA: Multimodality Fusion Strategy for Drug-Target Affinity Prediction

Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2023-03, Vol.20 (2), p.1200-1210
Main Authors: Yang, Xixi, Niu, Zhangming, Liu, Yuansheng, Song, Bosheng, Lu, Weiqiang, Zeng, Li, Zeng, Xiangxiang
Format: Article
Language:English
Subjects:
Affinity
Amino Acid Sequence
Amino acids
Benchmarking
Computational modeling
Datasets
Deep learning
Differential thermal analysis
Drug Discovery
drug modality
Drugs
drug–target affinity prediction
Encoding
Feature extraction
modality fusion
Multimodal Imaging
multimodality
Neural networks
Neural Networks, Computer
Noise reduction
Predictions
Proteins
Representations
target modality
Task analysis
Teaching methods
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Summary:Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2022.3205282