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Tingjun Hou

Showing results (251-260 of 464) with videos related to

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Bioinformatics (Oxford, England)|October 18, 2018
farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methodsZhe Wang, Xuwen Wang, Youyong Li, et al.
Oncotarget|July 9, 2014
A virtual screen identified C96 as a novel inhibitor of phosphatidylinositol 3-kinase that displays potent preclinical activity against multiple myeloma in vitro and in vivoJuan Tang, Jingyu Zhu, Yang Yu, et al.
RNA (New York, N.Y.)|June 23, 2018
Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexesFu Chen, Huiyong Sun, Junmei Wang, et al.
Scientific Reports|February 14, 2015
Revealing the favorable dissociation pathway of type II kinase inhibitors via enhanced sampling simulations and two-end-state calculationsHuiyong Sun, Sheng Tian, Shunye Zhou, et al.
Journal of Chemical Information and Modeling|March 14, 2024
Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide ComplexesHuifeng Zhao, Dejun Jiang, Chao Shen, et al.
Nature Communications|June 5, 2026
Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflowKai Zhu, Huating Wang, Jintu Zhang, et al.
Journal of Chemical Information and Modeling|January 31, 2025
Comprehensive Evaluation of End-Point Free Energy Methods in DNA-Ligand Interaction PredictionsCuiyu Li, Hongyan Du, Chengwei Zhang, et al.
Journal of Chemical Information and Modeling|October 26, 2023
Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data SetsZhe Wang, Haiyang Zhong, Jintu Zhang, et al.
Briefings in Bioinformatics|December 20, 2021
Comprehensive assessment of deep generative architectures for de novo drug designMingyang Wang, Huiyong Sun, Jike Wang, et al.
Chemical Science|September 12, 2022
Root-aligned SMILES: a tight representation for chemical reaction predictionZipeng Zhong, Jie Song, Zunlei Feng, et al.
Pageof 47

Showing results (251-260 of 464) with videos related to

Sort By:
Pageof 47
Bioinformatics (Oxford, England)|October 18, 2018
farPPI: a webserver for accurate prediction of protein-ligand binding structures for small-molecule PPI inhibitors by MM/PB(GB)SA methodsZhe Wang, Xuwen Wang, Youyong Li, et al.
Oncotarget|July 9, 2014
A virtual screen identified C96 as a novel inhibitor of phosphatidylinositol 3-kinase that displays potent preclinical activity against multiple myeloma in vitro and in vivoJuan Tang, Jingyu Zhu, Yang Yu, et al.
RNA (New York, N.Y.)|June 23, 2018
Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexesFu Chen, Huiyong Sun, Junmei Wang, et al.
Scientific Reports|February 14, 2015
Revealing the favorable dissociation pathway of type II kinase inhibitors via enhanced sampling simulations and two-end-state calculationsHuiyong Sun, Sheng Tian, Shunye Zhou, et al.
Journal of Chemical Information and Modeling|March 14, 2024
Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide ComplexesHuifeng Zhao, Dejun Jiang, Chao Shen, et al.
Nature Communications|June 5, 2026
Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflowKai Zhu, Huating Wang, Jintu Zhang, et al.
Journal of Chemical Information and Modeling|January 31, 2025
Comprehensive Evaluation of End-Point Free Energy Methods in DNA-Ligand Interaction PredictionsCuiyu Li, Hongyan Du, Chengwei Zhang, et al.
Journal of Chemical Information and Modeling|October 26, 2023
Small-Molecule Conformer Generators: Evaluation of Traditional Methods and AI Models on High-Quality Data SetsZhe Wang, Haiyang Zhong, Jintu Zhang, et al.
Briefings in Bioinformatics|December 20, 2021
Comprehensive assessment of deep generative architectures for de novo drug designMingyang Wang, Huiyong Sun, Jike Wang, et al.
Chemical Science|September 12, 2022
Root-aligned SMILES: a tight representation for chemical reaction predictionZipeng Zhong, Jie Song, Zunlei Feng, et al.
Pageof 47