Jianmin Wang
Orcid: 0000-0001-8910-0929Affiliations:
- Hunan University, School of Computer Science and Engineering, Changsha, China
- Yonsei University, Department of Integrative Biotechnology, Integrative Biotechnology & Translational Medicine, Korea
According to our database1,
Jianmin Wang
authored at least 17 papers
between 2020 and 2024.
Collaborative distances:
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Bibliography
2024
IEEE Trans. Neural Networks Learn. Syst., April, 2024
MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature.
Comput. Biol. Medicine, February, 2024
FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins.
J. Chem. Inf. Model., 2024
J. Chem. Inf. Model., 2024
Local Scaffold Diversity-Contributed Generator for Discovering Potential NLRP3 Inhibitors.
J. Chem. Inf. Model., 2024
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model.
CoRR, 2024
A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.
Comput. Biol. Medicine, 2024
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
2023
J. Chem. Inf. Model., December, 2023
J. Cheminformatics, December, 2023
2022
Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework.
Nat. Mac. Intell., November, 2022
A transformer-based model to predict peptide-HLA class I binding and optimize mutated peptides for vaccine design.
Nat. Mach. Intell., 2022
Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction.
Briefings Bioinform., 2022
<i>De novo</i> molecular design with deep molecular generative models for PPI inhibitors.
Briefings Bioinform., 2022
2021
Bioinform., 2021
A spatial-temporal gated attention module for molecular property prediction based on molecular geometry.
Briefings Bioinform., 2021
2020
VGG16-T: A Novel Deep Convolutional Neural Network with Boosting to Identify Pathological Type of Lung Cancer in Early Stage by CT Images.
Int. J. Comput. Intell. Syst., 2020