Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates,...

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Veröffentlicht in:	Scientific reports 2019-05, Vol.9 (1), p.7703, Article 7703
Hauptverfasser:	Li, Li, Koh, Ching Chiek, Reker, Daniel, Brown, J. B., Wang, Haishuai, Lee, Nicholas Keone, Liow, Hien-haw, Dai, Hao, Fan, Huai-Meng, Chen, Luonan, Wei, Dong-Qing
Format:	Artikel
Sprache:	eng
Schlagworte:	119/118 631/114/2163 631/114/2397 631/154/1435 Algorithms Bayes Theorem Bayesian analysis Binding Sites Chemogenomics Drug Development Drug discovery G protein-coupled receptors High-Throughput Screening Assays - methods Humanities and Social Sciences Humans Hydrophobic and Hydrophilic Interactions Ion channels Learning algorithms Ligands Machine Learning Models, Chemical Models, Molecular Molecular Docking Simulation multidisciplinary Nuclear receptors Protein Binding Proteins Proteins - drug effects Science Science (multidisciplinary) Side effects Statistics, Nonparametric Vigilance
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