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. 2025 Aug 12;15(1):106.
doi: 10.1186/s13550-025-01300-z.

Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery

Muath Almaslamani  1   2 , Jingyu Yang  1 , Chi Soo Kang  1   2 , Choong Mo Kang  1   2 , Jung Mi Park  3 , Sang-Keun Woo  4   5
Affiliations

Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery

Muath Almaslamani et al. EJNMMI Res. .

Abstract

Background: NDUFS1 is the largest subunit of OXPHOS complex I (MC-I) and mutations in this gene are associated with MC-I deficiency. This study aims to develop a graph neural network and attention mechanism-based radiopharmaceutical-protein (RP-protein) interaction prediction model for identifying an imaging candidate of mitochondrial function through targeting its core subunit NDUFS1.

Results: The estimated cell viability values for trastuzumab, 177Lu-DOTA-trastuzumab, and 225Ac-DOTA-trastuzumab were 290.1, 89.01, and 8.262 nM, respectively. The deep learning (DL) model was pretrained with normal compound-protein pairs. Afterwards, the model was fine-tuned with the dataset of RP-protein pairs and evaluated with five-fold cross validation. The prediction model trained with normal compound-protein pairs effectively predicted the binding affinity. The fine-tuned model incorporating radioactive properties outperformed the same model trained only on normal compounds. The model estimated the important substructure of a compound related to its binding to the target protein. NDUFS1 protein-targeting compounds were identified and BDBM210829 compound had the best binding affinities, binding rank, and LogP as it binds to the NDUFS1.

Conclusions: This study proposed a DL-based radiolabelled compound-protein interaction prediction model to identify a radiopharmaceutical (RP) that binds to the mitochondrial core subunit NDUFS1. The proposed model shows good performance for predicting RP-protein interaction. BDBM210829 was identified as a top candidate for radiolabeling and targeting the mitochondrial core subunit NDUFS1. This model can be used as an effective virtual screening tool for RP discovery.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13550-025-01300-z.

Keywords: Binding affinity; Compound protein interaction; Graph neural network; Mitochondria; NDUFS1; Radiopharmaceutical discovery.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by the Institutional Review Board of KIRAMS (IRB No.: 2022-09-006-002). All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication: Informed consent was obtained from all participants involved in the study. Competing interests: The authors have declared that no competing interest exists. Clinical trial number: Not applicable.

Figures

Fig. 1
Fig. 1
Network architecture of the radiopharmaceutical-compound interaction prediction model
Fig. 2
Fig. 2
Scatter plot of the predicted natural log of binding affinities by the prediction model and their ground-truth of the normal test dataset. A: Ki; B: Kd
Fig. 3
Fig. 3
Binding affinity with corresponding ranking of NDUFS1-binding compounds identified by the proposed model
Fig. 4
Fig. 4
Chemical structure of top 8 selected NDUFS1-binding compounds identified by the proposed model. (A) BDBM85009, (B) BDBM210829, (C) CYN154806, (D) BDBM214012, (E) BDBM214042, (F) CID135423658, (G) CHEMBL4290888, and (H) CHEMBL3605412
Fig. 5
Fig. 5
Docking simulation results using AutoDock 4 of compounds binding to NDUFS1 protein. Dotted yellow line indicates the docking site of the pair. (A) BDBM210829; (B) BDBM214042; (C) Rotenone; (D) BDBM210829-NDUFS1 interactions
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