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publications

To Improve Protein Sequence Profile Prediction through Image Captioning on Pairwise Residue Distance Map

Published in Journal of Chemical Information and Modeling, 2019

protein design/ protein sequence profile prediction.

Recommended citation: Chen, S., Sun, Z., Lin, L., Liu, Z., Liu, X., Chong, Y., ... & Yang, Y. (2019). To improve protein sequence profile prediction through image captioning on pairwise residue distance map. Journal of chemical information and modeling, 60(1), 391-399.
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Predicting drug–protein interaction using quasi-visual question answering system

Published in Nature Machine Intelligence, 2020

Identifying drug–protein interactions

Recommended citation: Zheng, S., Li, Y., Chen, S., Xu, J., & Yang, Y. (2020). Predicting drug–protein interaction using quasi-visual question answering system. Nature Machine Intelligence, 2(2), 134-140.
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SEGEM: A fast and accurate automated protein backbone structure modeling method for cryo-EM

Published in IEEE BIBM 2021, 2021

cryo-EM protein structure modeling

Recommended citation: Chen, S., Zhang, S., Li, X., Liu, Y., & Yang, Y. (2021, December). SEGEM: A fast and accurate automated protein backbone structure modeling method for cryo-EM. In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 24-31). IEEE.
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AlphaFold2-aware protein–DNA binding site prediction using graph transformer

Published in Briefings in bioinformatics, 2022

identification of Protein–DNA interactions

Recommended citation: Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022). AlphaFold2-aware protein–DNA binding site prediction using graph transformer. Briefings in Bioinformatics, 23(2), bbab564.
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Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning

Published in Briefings in Bioinformatics, 2022

identification of metal ion-binding residues

Recommended citation: Yuan, Q., Chen, S., Wang, Y., Zhao, H., & Yang, Y. (2022). Alignment-free metal ion-binding site prediction from protein sequence through pretrained language model and multi-task learning. Briefings in bioinformatics, 23(6), bbac444.
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Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation

Published in Journal of Computational Chemistry, 2023

Mutation-caused protein solubility change prediction

Recommended citation: Wang, J., Chen, S., Yuan, Q., Chen, J., Li, D., Wang, L., & Yang, Y. (2024). Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation. Journal of Computational Chemistry, 45(8), 436-445.
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SE (3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation

Published in IEEE BIBM 2023, 2023

Protein side chain conformation prediction

Recommended citation: Liu, D., Chen, S., Zheng, S., Zhang, S., & Yang, Y. (2023, December). SE (3) Equivalent Graph Attention Network as an Energy-Based Model for Protein Side Chain Conformation. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 120-123). IEEE.
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DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model

Published in Journal of Chemical Information and Modeling, 2024

Pocket-conditioned molecular generation.

Recommended citation: Xie, J., Chen, S., Lei, J., & Yang, Y. (2024). DiffDec: structure-aware scaffold decoration with an end-to-end diffusion model. Journal of Chemical Information and Modeling, 64(7), 2554-2564.
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Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation

Published in Chemical Science, 2024

Dual-target drug design

Recommended citation: Chen, S., Xie, J., Ye, R., Xu, D. D., & Yang, Y. (2024). Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation. Chemical Science, 15(27), 10366-10380.
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Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

Published in Nature Communications, 2024

Protein function prediction

Recommended citation: Song, Y., Yuan, Q., Chen, S., Zeng, Y., Zhao, H., & Yang, Y. (2024). Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures. Nature Communications, 15(1), 8180.
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Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences

Published in Nature Communications, 2024

Protein structure prediction

Recommended citation: Chen, S., Zhang, S., Fang, X. et al. Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences. Nat Commun 15, 8808 (2024). https://doi.org/10.1038/s41467-024-53116-5
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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015