A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
University of St Andrews · University of Cambridge
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…
Citation impact
- FWCI
- 17.16
- Percentile
- 100%
- References
- 53
Authors
2Topics & keywords
- Overfitting
- Machine learning
- Computer science
- Random forest
- Virtual screening
- Artificial intelligence
- Bootstrapping (finance)
- Drug discovery