Pengyong Li

Orcid: 0000-0003-2957-7033

According to our database1, Pengyong Li authored at least 19 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

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

Secure architecture for Industrial Edge of Things(IEoT): A hierarchical perspective.
Comput. Networks, 2024

Improving drug response prediction via integrating gene relationships with deep learning.
Briefings Bioinform., 2024

2023
Deep generative model for drug design from protein target sequence.
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

HCL: Improving Graph Representation with Hierarchical Contrastive Learning.
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

Simulated annealing for optimization of graphs and sequences.
Neurocomputing, 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

TrimNet: learning molecular representation from triplet messages for biomedicine.
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

PASH at TREC 2020 Deep Learning Track: Dense Matching for Nested Ranking.
Proceedings of the Twenty-Ninth Text REtrieval Conference, 2020


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