Dejun Jiang
Orcid: 0000-0002-2035-5074Affiliations:
- Zhejiang University, Hangzhou, Zhejiang, China
According to our database1,
Dejun Jiang
authored at least 15 papers
between 2020 and 2023.
Collaborative distances:
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Bibliography
2023
J. Cheminformatics, December, 2023
Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR.
Sensors, April, 2023
Nat. Comput. Sci., 2023
Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
CoRR, 2023
2022
An adaptive graph learning method for automated molecular interactions and properties predictions.
Nat. Mach. Intell., 2022
Knowledge-based BERT: a method to extract molecular features like computational chemists.
Briefings Bioinform., 2022
Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning.
Briefings Bioinform., 2022
2021
<i>DeepChargePredictor</i>: a web server for predicting QM-based atomic charges via <i>state-of-the-art</i> machine-learning algorithms.
Bioinform., November, 2021
Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning.
Nat. Mach. Intell., 2021
VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations.
J. Chem. Inf. Model., 2021
Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models.
J. Cheminformatics, 2021
Identification of active molecules against Mycobacterium tuberculosis through machine learning.
Briefings Bioinform., 2021
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Briefings Bioinform., 2021
Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.
Briefings Bioinform., 2021
2020
ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.
J. Cheminformatics, 2020