Pengyong Li
Orcid: 0000-0003-2957-7033
According to our database1,
Pengyong Li
authored at least 19 papers
between 2020 and 2025.
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Bibliography
2025
SFML: A personalized, efficient, and privacy-preserving collaborative traffic classification architecture based on split learning and mutual learning.
Future Gener. Comput. Syst., 2025
2024
MuLDOM: Forecasting Multivariate Anomalies on Edge Devices in IIoT Using Multibranch LSTM and Differential Overfitting Mitigation Model.
IEEE Internet Things J., December, 2024
A deep learning approach for rational ligand generation with toxicity control via reactive building blocks.
Nat. Comput. Sci., November, 2024
PT-ADP: A personalized privacy-preserving federated learning scheme based on transaction mechanism.
Inf. Sci., 2024
Comput. Networks, 2024
Improving drug response prediction via integrating gene relationships with deep learning.
Briefings Bioinform., 2024
2023
J. Cheminformatics, December, 2023
Improving drug-target affinity prediction via feature fusion and knowledge distillation.
Briefings Bioinform., May, 2023
2022
An adaptive graph learning method for automated molecular interactions and properties predictions.
Nat. Mach. Intell., 2022
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer.
CoRR, 2022
Proceedings of the Semantic Web - ISWC 2022, 2022
2021
Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.
PLoS Comput. Biol., 2021
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks.
CoRR, 2021
An effective self-supervised framework for learning expressive molecular global representations to drug discovery.
Briefings Bioinform., 2021
Briefings Bioinform., 2021
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021
2020
Learn molecular representations from large-scale unlabeled molecules for drug discovery.
CoRR, 2020
Proceedings of the Twenty-Ninth Text REtrieval Conference, 2020