Evidential deep learning-based drug-target interaction prediction

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions. To solve these problems, we propose EviDTI, a novel approach utilizing evidential deep learning (EDL) for uncertainty quantification in neural network-based DTI prediction. EviDTI integrates multiple data dimensions, including drug 2D topological graphs and 3D spatial structures, and target sequence features. Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhances the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. In a case study focused on tyrosine kinase modulators, uncertainty-guided predictions identify novel potential modulators targeting tyrosine kinase FAK and FLT3. These results underscore the potential of evidential deep learning as a robust tool for uncertainty quantification in DTI prediction and its broader implications for accelerating drug discovery.

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Publication Details

Journal
Nature Communications
Published
2025-07-26
DOI
https://doi.org/10.1038/s41467-025-62235-6
Citations
26
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
39.11

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article

Evidential deep learning-based drug-target interaction prediction

Duo Yi, Shangze Li, Yixin Zhang, Lianlian Wu et al.
26 citations
Nature Communications
Computational Drug Discovery Methods
39.11
article

Evidential deep learning-based drug-target interaction prediction

Duo Yi, Shangze Li, Yixin Zhang, Lianlian Wu, Huiyan Xu, Guowei Zhou, Zhengshan Chen, Ziyi Liu, Chengkun Wu, Min Wan, Mengfan Li, Xuanze Wang, Peng Zan, Hongyang Zhang, Song He, Ruijiang Li, Yanpeng Zhao, Dongsheng Zhao, Yuting Xing, Xiaochen Bo, Yifei Wang
article en
26 citations

Abstract

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions. To solve these problems, we propose EviDTI, a novel approach utilizing evidential deep learning (EDL) for uncertainty quantification in neural network-based DTI prediction. EviDTI integrates multiple data dimensions, including drug 2D topological graphs and 3D spatial structures, and target sequence features. Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhances the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. In a case study focused on tyrosine kinase modulators, uncertainty-guided predictions identify novel potential modulators targeting tyrosine kinase FAK and FLT3. These results underscore the potential of evidential deep learning as a robust tool for uncertainty quantification in DTI prediction and its broader implications for accelerating drug discovery.

Nature CommunicationsVol. 16(1)
Shanghai University (CN), Tianjin University (CN), National University of Defense Technology (CN), Academy of Military Medical Sciences (CN), Tianjin Medical University (CN)
Natural Science Foundation of Shanghai, National Key Research and Development Program of China
Openalex Percentile: Top 0%
Computational Drug Discovery Methods
39.11
DOIOpen Access PDFOpenAlex
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