Dejun Jiang

Orcid: 0000-0002-2035-5074

  • Zhejiang University, Hangzhou, Zhejiang, China

According to our database1, Dejun Jiang authored at least 19 papers between 2020 and 2024.

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



In proceedings 
PhD thesis 


Online presence:



Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.
J. Chem. Inf. Model., 2024

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.
J. Chem. Inf. Model., 2024

PPFlow: Target-aware Peptide Design with Torsional Flow Matching.
CoRR, 2024

Deep Lead Optimization: Leveraging Generative AI for Structural Modification.
CoRR, 2024

TB-IECS: an accurate machine learning-based scoring function for virtual screening.
J. Cheminformatics, December, 2023

Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR.
Sensors, April, 2023

Learning on topological surface and geometric structure for 3D molecular generation.
Nat. Comput. Sci., 2023

Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?
CoRR, 2023

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

<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

ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.
J. Cheminformatics, 2020