Yu Kang

Orcid: 0000-0002-0999-8802

Affiliations:
  • Zhejiang University, Hangzhou, China


According to our database1, Yu Kang authored at least 29 papers between 2017 and 2024.

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

Timeline

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Bibliography

2024
A general model for predicting enzyme functions based on enzymatic reactions.
J. Cheminformatics, December, 2024

Genetic Algorithm-Based Receptor Ligand: A Genetic Algorithm-Guided Generative Model to Boost the Novelty and Drug-Likeness of Molecules in a Sampling Chemical Space.
J. Chem. Inf. Model., February, 2024

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

Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios.
J. Chem. Inf. Model., 2024

Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation.
CoRR, 2024

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

Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data Sets.
J. Chem. Inf. Model., November, 2023

ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling.
Nat. Mac. Intell., September, 2023

ML-PLIC: a web platform for characterizing protein-ligand interactions and developing machine learning-based scoring functions.
Briefings Bioinform., September, 2023

On the Dynamic Mechanism of Long-Flexible Fatty Acid Binding to Fatty Acid Binding Protein: Resolving the Long-Standing Debate.
J. Chem. Inf. Model., August, 2023

Deep Generation Model Guided by the Docking Score for Active Molecular Design.
J. Chem. Inf. Model., May, 2023

Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?
Briefings Bioinform., March, 2023

Quasiclassical Trajectory Simulation as a Protocol to Build Locally Accurate Machine Learning Potentials.
J. Chem. Inf. Model., February, 2023

Efficient and accurate large library ligand docking with KarmaDock.
Nat. Comput. Sci., 2023

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

2022
Organic Compound Synthetic Accessibility Prediction Based on the Graph Attention Mechanism.
J. Chem. Inf. Model., 2022

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation.
J. Chem. Inf. Model., 2022

ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction.
J. Chem. Inf. Model., 2022

Molecular View on the Dissociation Pathways and Transactivation Regulation Mechanism of Nonsteroidal GR Ligands.
J. Chem. Inf. Model., 2022

ReMODE: a deep learning-based web server for target-specific drug design.
J. Cheminformatics, 2022

fastDRH: a webserver to predict and analyze protein-ligand complexes based on molecular docking and MM/PB(GB)SA computation.
Briefings Bioinform., 2022

2021
CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors.
Nucleic Acids Res., 2021

The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.
J. Cheminformatics, 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

2020
Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities.
J. Chem. Inf. Model., 2020

2019
farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methods.
Bioinform., 2019

2018
Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products.
J. Chem. Inf. Model., 2018

2017
Characterizing Drug-Target Residence Time with Metadynamics: How To Achieve Dissociation Rate Efficiently without Losing Accuracy against Time-Consuming Approaches.
J. Chem. Inf. Model., August, 2017

HawkRank: a new scoring function for protein-protein docking based on weighted energy terms.
J. Cheminformatics, 2017


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