References

Altschul S F, Gish W, Miller W, et al. Basic local alignment search tool[J]. Journal of molecular biology, 1990, 215(3): 403-410.

Yu T, Cui H, Li J C, et al. Enzyme function prediction using contrastive learning[J]. Science, 2023, 379(6639): 1358-1363.

Song Y, Yuan Q, Chen S, et al. Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures[J]. Nature Communications, 2024, 15(1): 8180.

van der Weg K, Merdivan E, Piraud M, et al. TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor[J]. Nature Communications, 2025, 16(1): 2737.

Ryu J Y, Kim H U, Lee S Y. Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers[J]. Proceedings of the National Academy of Sciences, 2019, 116(28): 13996-14001.

Van Kempen M, Kim S S, Tumescheit C, et al. Fast and accurate protein structure search with Foldseek[J]. Nature biotechnology, 2024, 42(2): 243-246.

Schroff F, Kalenichenko D, Philbin J. Facenet: A unified embedding for face recognition and clustering[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 815-823.

Wang Z, Wang P, Liu T, et al. HPT: Hierarchy-aware prompt tuning for hierarchical text classification[J]. arXiv preprint arXiv:2204.13413, 2022.

Kim G B, Kim J Y, Lee J A, et al. Functional annotation of enzyme-encoding genes using deep learning with transformer layers[J]. Nature Communications, 2023, 14(1): 7370.

Wang W, Shuai Y, Zeng M, et al. DPFunc: accurately predicting protein function via deep learning with domain-guided structure information[J]. Nature Communications, 2025, 16(1): 70.

Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: Visualising image classification models and saliency maps[J]. arXiv preprint arXiv:1312.6034, 2013.

Quinlan J R. Induction of decision trees[J]. Machine learning, 1986, 1: 81-106.