Correcting the impact of docking pose generation error on binding affinity prediction

BMC Bioinformatics

Volumn 17, Issue , 2016, Pages

  Li, Hongjian ^a   Leung, Kwong Sak ^a   Wong, Man Hon ^a   Ballester, Pedro J ^b,c,d  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b AIX MARSEILLE UNIVERSITY   (France)

^c INSTITUT PAOLI CALMETTES   (France)

^d AIX MARSEILLE UNIVERSITY   (France)

Author keywords

Binding affinity; Drug discovery; Machine learning; Molecular docking

Indexed keywords

ARTIFICIAL INTELLIGENCE; BINDING ENERGY; COMPLEXATION; FORECASTING; LEARNING SYSTEMS; LIGANDS; PROTEINS;

AUTODOCK VINAS; BINDING AFFINITIES; DIVERSE PROTEINS; DRUG DISCOVERY; ERROR-CORRECTING; MOLECULAR DOCKING; PROTEIN LIGANDS; SCORING FUNCTIONS;

ERRORS;

LIGAND; NUCLEAR PROTEIN; PI3KCA PROTEIN, HUMAN; PROTEIN BINDING; PYRAZINE DERIVATIVE; TRANSCRIPTION FACTOR;

CHEMISTRY; HUMAN; METABOLISM; MOLECULAR DOCKING; PROTEIN CONFORMATION; SOFTWARE; STANDARDS;

HUMANS; LIGANDS; MOLECULAR DOCKING SIMULATION; NUCLEAR PROTEINS; PROTEIN BINDING; PROTEIN CONFORMATION; PYRAZINES; SOFTWARE; TRANSCRIPTION FACTORS;

EID: 84995688316     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-1169-4     Document Type: Conference Paper

References (17)

1. Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010; 31(2):455-61.
2. Li H, Leung KS, Ballester PJ, Wong MH. istar: A Web Platform for Large-Scale Protein-Ligand Docking. PLoS ONE. 2014; 9(1):85678.
3. Ain QU, Aleksandrova A, Roessler FD, Ballester PJ. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. WIREs Comput Mol Sci. 2015; 5(6):405-24.
4. Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics. 2010; 26(9):1169-75.
5. Ballester PJ, Schreyer A, Blundell TL. Does a More Precise Chemical Description of Protein-Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?J Chem Inf Model. 2014; 54(3):944-55.
6. Li H, Leung KS, Wong MH, Ballester PJ. Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets. Mol Inf. 2015; 34(2-3):115-26.
7. Li H, Leung KS, Wong MH, Ballester P. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinforma. 2014; 15(1):291.
8. Ballester PJ, Mangold M, Howard NI, Robinson RLM, Abell C, Blumberger J, Mitchell JBO. Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification. J R Soc Interface. 2012; 9(77):3196-207.
9. Li H, Leung KS, Wong MH, Ballester PJ. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest. Molecules. 2015; 20(6):10947-62.
10. Wang JC, Lin JH, Chen CM, Perryman AL, Olson AJ. Robust Scoring Functions for Protein-Ligand Interactions with Quantum Chemical Charge Models. Journal of Chemical Information and Modeling. 2011; 51(10):2528-37.
11. Breiman L. Random Forests. Mach Learn. 2001; 45(1):5-32.
12. Ballester PJ. Machine Learning Scoring Functions Based on Random Forest and Support Vector Regression. In: Pattern Recognition in Bioinformatics. Lecture Notes in Computer Science, vol. 7632. Berlin: Springer: 2012. p. 14-25.
13. Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and Regression Trees. London: Chapman & Hall; 1984.
14. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J Chem Inf Comput Sci. 2003; 43(6):1947-58.
15. Cheng T, Li X, Li Y, Liu Z, Wang R. Comparative Assessment of Scoring Functions on a Diverse Test Set. J Chem Inf Model. 2009; 49(4):1079-93.
16. Zilian D, Sotriffer CA. SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein-Ligand Complexes. J Chem Inf Model. 2013; 53(8):1923-33.
17. Li H, Leung KS, Nakane T, Wong MH. iview: an interactive WebGL visualizer for protein-ligand complex. BMC Bioinformatics. 2014; 15(1):56.