Improvement of protein-ligand binding affinity prediction using machine learning techniques
Tipus de documentText en actes de congrés
Data publicació2015-05-05
EditorBarcelona Supercomputing Center
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Predicting protein-ligand binding affinities constitutes a key computational method in the early stages of the drug discovery process. Molecular docking programs attempt to predict them by using mathematical approximations, namely, scoring functions. In the last years, several scoring functions have been developed, encompassing different terms, from electrostatic forces to protein-ligand interaction fingerprints and beyond. However, it has been noticed that usually each individual scoring function cannot be generalized and its predictive power is arguable. The aim of this study is to improve the binding affinity prediction by finding potential models to combine ten different scoring functions, exploiting machine learning techniques.
CitacióHernandez, Gabriela; Iglesias, Jelisa; Saen-oon, Suwipa. Improvement of protein-ligand binding affinity prediction using machine learning techniques. A: 3rd BSC International Doctoral Symposium. "Book of abstracts". Barcelona: Barcelona Supercomputing Center, 2015, p. 148-150.
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148-150 Improve ... oral Symposium 2016-39.pdf | 959,1Kb | Visualitza/Obre |