Chao Shen
Orcid: 0000-0003-2783-5529Affiliations:
- Zhejiang University, College of Pharmaceutical Sciences, Hangzhou, China
- CarbonSilicon AI Technology Co., Ltd, Hangzhou, Zhejiang, China
According to our database1,
Chao Shen
authored at least 23 papers
between 2019 and 2024.
Collaborative distances:
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Bibliography
2024
Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes.
J. Chem. Inf. Model., 2024
2023
J. Cheminformatics, December, 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
Can molecular dynamics simulations improve predictions of protein-ligand binding affinity with machine learning?
Briefings Bioinform., March, 2023
Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.
Briefings Bioinform., January, 2023
Nat. Comput. Sci., 2023
2022
J. Cheminformatics, 2022
2021
ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions.
J. Cheminformatics, 2021
The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.
J. Cheminformatics, 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
Improving structure-based virtual screening performance via learning from scoring function components.
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
Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
Briefings Bioinform., 2021
Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions.
Briefings Bioinform., 2021
Beware of the generic machine learning-based scoring functions in structure-based virtual screening.
Briefings Bioinform., 2021
2020
Improving Docking-Based Virtual Screening Ability by Integrating Multiple Energy Auxiliary Terms from Molecular Docking Scoring.
J. Chem. Inf. Model., 2020
ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.
J. Cheminformatics, 2020
Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power.
Briefings Bioinform., 2020
2019
ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches.
J. Chem. Inf. Model., 2019