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Chapitre D'ouvrage Année : 2019

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity

Résumé

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.
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hal-02533421 , version 1 (06-04-2020)

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Maciej Wójcikowski, Pawel Siedlecki, Pedro J Ballester. Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity. Methods in Molecular Biology, 2053, pp.1-12, 2019, ⟨10.1007/978-1-4939-9752-7_1⟩. ⟨hal-02533421⟩

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