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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
- Source :
- Bioinformatics. 26:1169-1175
- Publication Year :
- 2010
- Publisher :
- Oxford University Press (OUP), 2010.
-
Abstract
- Motivation: Accurately predicting the binding affinities of large sets of diverse protein–ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. Results: We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. Contact: pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
- Subjects :
- Statistics and Probability
Protein Conformation
Computer science
Chemical biology
Overfitting
Ligands
Machine learning
computer.software_genre
Biochemistry
Article
Protein structure
Artificial Intelligence
Cluster Analysis
Databases, Protein
Molecular Biology
Models, Statistical
Drug discovery
business.industry
Ligand
Binding protein
Computational Biology
Proteins
Reproducibility of Results
Ligand (biochemistry)
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
Structural biology
Docking (molecular)
Data Interpretation, Statistical
Drug Design
Artificial intelligence
business
computer
Algorithms
Protein Binding
Protein ligand
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....3cfa11f6d9caf478d936eb77a8bfd7dd