ETDock: an equivariant transformer for protein-ligand docking

Yiqiang YI , Yuting HUANG , Xu WAN , Yatao BIAN , Debby D. WANG , Peilin ZHAO , Le OU-YANG

Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (5) : 2105903

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Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (5) :2105903 DOI: 10.1007/s11704-026-51026-x
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ETDock: an equivariant transformer for protein-ligand docking
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Yiqiang YI, Yuting HUANG, Xu WAN, Yatao BIAN, Debby D. WANG, Peilin ZHAO, Le OU-YANG. ETDock: an equivariant transformer for protein-ligand docking. Front. Comput. Sci., 2027, 21(5): 2105903 DOI:10.1007/s11704-026-51026-x

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References

[1]

Trott O, Olson A J . AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 2010, 31( 2): 455–461

[2]

Koes D R, Baumgartner M P, Camacho C J . Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. Journal of Chemical Information and Modeling, 2013, 53( 8): 1893–1904

[3]

Friesner R A, Banks J L, Murphy R B, Halgren T A, Klicic J J, Mainz D T, Repasky M P, Knoll E H, Shelley M, Perry J K, Shaw D E, Francis P, Shenkin P S . Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 2004, 47( 7): 1739–1749

[4]

Zhu M, Wang D D, Yan H . Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung cancer treatment revealed by molecular dynamics simulations. BMC Molecular and Cell Biology, 2021, 22( 1): 34

[5]

Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T. DiffDock: diffusion steps, twists, and turns for molecular docking. In: Proceedings of the 11th International Conference on Learning Representations. 2023

[6]

Lu W, Wu Q, Zhang J, Rao J, Li C, Zheng S. TANKBind: trigonometry-aware neural networks for drug-protein binding structure prediction. In: Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS 2022). 2022, 7236−7249

[7]

Stärk H, Ganea O, Pattanaik L, Barzilay R, Jaakkola T. EquiBind: geometric deep learning for drug binding structure prediction. In: Proceedings of the 39th International Conference on Machine Learning. 2022, 20503−20521

[8]

Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks?. In: Proceedings of the 7th International Conference on Learning Representations. 2019

[9]

Jing B, Eismann S, Suriana P, Townshend R J L, Dror R O. Learning from protein structure with geometric vector perceptrons. In: Proceedings of the 9th International Conference on Learning Representations. 2021

[10]

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, . et al. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596( 7873): 583–589

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