Tingjun Hou

Molecular Biology · 2002–2021

A researcher in the Science corpus: 14 papers; trusts 5 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 229 citations into it; #4,137 of 17,773 by trust here, #4,880 by citations
• Materials Chemistry — 109 citations into it; #9,832 of 11,513 by trust here, #8,524 by citations
• Atomic and Molecular Physics, and Optics — 26 citations into it; #4,090 of 7,135 by trust here, #4,097 by citations
• Cell Biology — 14 citations into it

Whom Tingjun Hou trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Holger Gohlke
2. David A. Case
3. Junmei Wang
4. Alexey V. Onufriev
5. Hans-Joachim Böhm
6. T. Darden

Who trusts Tingjun Hou overall

1. Samuel Genheden
2. John C. Dearden
3. Junmei Wang
4. Ulf Ryde
5. Elizabeth Yuriev
6. Scott A. Hollingsworth
7. Gregory Sliwoski
8. Feng-Xu Wu
9. Huiyong Sun
10. Xing Du

Papers from Tingjun Hou's perspective

Ordered by how strongly the researchers Tingjun Hou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Protein data bank
2. The Amber biomolecular simulation programs
3. Proteins
4. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading
5. GRAMM-X public web server for protein–protein docking (Nucleic Acids Res.)
6. PatchDock and SymmDock: servers for rigid and symmetric docking
7. CAPRI: A Critical Assessment of PRedicted Interactions (Proteins: Structure, Function, and Bioinformatics)
8. SwarmDock: a server for flexible protein-protein docking
9. Scikit-learn: Machine Learning in Python
10. Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library (PLoS ONE)

Their papers

• InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions. (2021) — 241 citations
• The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction (2021) — 33 citations
• ADME evaluation in drug discovery (2002) — 652 citations
• Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models (2020) — 616 citations
• HawkDock: a web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA (2019) — 565 citations
• Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking. (2016) — 418 citations
• Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power. (2016) — 706 citations
• Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations (2010) — 2,526 citations
• End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. (2019) — 1,862 citations
• Assessing the performance of the MM/PBSA and MM/GBSA methods: II. The accuracy of ranking poses generated from docking (2010) — 711 citations
• Recent Advances in Free Energy Calculations with a Combination of Molecular Mechanics and Continuum Models (2006) — 362 citations
• ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties (2021) — 2,384 citations
• The application of in silico drug-likeness predictions in pharmaceutical research. (2015) — 441 citations
• Insight into Crizotinib Resistance Mechanisms Caused by Three Mutations in ALK Tyrosine Kinase using Free Energy Calculation Approaches (2013) — 81 citations