Back to Search
Start Over
Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity
Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity
- Source :
- Methods in Molecular Biology, Methods in Molecular Biology, 2053, pp.1-12, 2019, ⟨10.1007/978-1-4939-9752-7_1⟩, Methods in Molecular Biology ISBN: 9781493997510
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to build RF-Score, a scoring function utilizing the machine-learning technique known as Random Forest (RF). We also point out how to use different data, features, and regression models using either R or Python programming languages.
- Subjects :
- 0303 health sciences
010304 chemical physics
Computer science
business.industry
[SDV]Life Sciences [q-bio]
Intermolecular force
[SDV.BC]Life Sciences [q-bio]/Cellular Biology
Ligand (biochemistry)
Machine learning
computer.software_genre
01 natural sciences
Small molecule
Docking
03 medical and health sciences
Binding affinity
Docking (molecular)
0103 physical sciences
Structure based
Artificial intelligence
Scoring function
business
computer
030304 developmental biology
Macromolecule
Subjects
Details
- Language :
- English
- ISBN :
- 978-1-4939-9751-0
- ISBNs :
- 9781493997510
- Database :
- OpenAIRE
- Journal :
- Methods in Molecular Biology, Methods in Molecular Biology, 2053, pp.1-12, 2019, ⟨10.1007/978-1-4939-9752-7_1⟩, Methods in Molecular Biology ISBN: 9781493997510
- Accession number :
- edsair.doi.dedup.....8535853dc7680f336d3f64459454e2b0
- Full Text :
- https://doi.org/10.1007/978-1-4939-9752-7_1⟩