CCL-DTI: contributing the contrastive loss in drug–target interaction prediction

Background The Drug–Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module. Results In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets. Conclusions Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein–protein interaction networks and drug–drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches..

Medienart:	E-Artikel

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	Zur Gesamtaufnahme - volume:25
Enthalten in:	BMC bioinformatics - 25(2024), 1 vom: 30. Jan.

Sprache:	Englisch

Beteiligte Personen:	Dehghan, Alireza [VerfasserIn] Abbasi, Karim [VerfasserIn] Razzaghi, Parvin [VerfasserIn] Banadkuki, Hossein [VerfasserIn] Gharaghani, Sajjad [VerfasserIn]

Links:	Volltext [kostenfrei]

Themen:	Contrastive loss function Drug–target interaction Drug discovery Multimodal deep learning

Anmerkungen:	© The Author(s) 2024

doi:	10.1186/s12859-024-05671-3

funding:
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PPN (Katalog-ID):	SPR054589924