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Integrating sequence and graph information for enhanced drug-target affinity prediction

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References

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 62176272), Research and Development Program of Guangzhou Science and Technology Bureau (Grant No. 2023B01J1016), Key-Area Research and Development Program of Guangdong Province (Grant No. 2020B1111100001).

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Author notes

  1. He H H and Chen G X have the same contribution to this work.

Authors and Affiliations

  1. Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China

    Haohuai He, Guanxing Chen & Calvin Yu-Chian Chen

Authors
  1. Haohuai He
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  2. Guanxing Chen
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  3. Calvin Yu-Chian Chen
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Corresponding author

Correspondence to Calvin Yu-Chian Chen.

Additional information

Supporting information Appendixes A–E. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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He, H., Chen, G. & Chen, C.YC. Integrating sequence and graph information for enhanced drug-target affinity prediction. Sci. China Inf. Sci. 67, 129101 (2024). https://doi.org/10.1007/s11432-022-3793-7

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  • DOI: https://doi.org/10.1007/s11432-022-3793-7

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