Random-forest model for drug–target interaction prediction via Kullbeck–Leibler divergence

Random-forest model for drug–target interaction prediction via Kullbeck–Leibler divergence

Abstract Virtual screening has significantly improved the success rate of early stage drug discovery. Recent virtual screening methods have improved owing to advances in machine learning and chemical information. Among these advances, the creative extraction of drug features is important for predict...

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Bibliographic Details
Main Authors: Sangjin Ahn, Si Eun Lee, Mi-hyun Kim
Format: Article
Language:English
Published: BMC 2022-10-01
Series:Journal of Cheminformatics
Subjects:
Chemocentric
3D Molecular Fingerprint
3D Similarity
Drug–Target Interaction Feature
Nonparametric Density Estimation
Kullbeck–Leibler Divergence
Online Access:https://doi.org/10.1186/s13321-022-00644-1

Internet

https://doi.org/10.1186/s13321-022-00644-1

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