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Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze
2015
PLoS Computational Biology
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Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into ...
a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. ...
by Structural Network Biology PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004157 March 27, 2015 13 / 19 ...
doi:10.1371/journal.pcbi.1004157
pmid:25816344
pmcid:PMC4376866
fatcat:u3ipodfcszhrva52jslneqqmuq
Target Prediction for an Open Access Set of Compounds Active against Mycobacterium tuberculosis
2013
PLoS Computational Biology
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Here, we use a computational approach that integrates historical bioassay data, chemical properties and structural comparisons of selected compounds to propose their potential targets in M. tuberculosis ...
We predicted 139 target -compound links, providing a necessary basis for further studies to characterize the mode of action of these compounds. ...
The ''GSK Ligand'' network is linked to the ''PDB ligand'' network by edges corresponding to the compound similarity measure by the RFS. ...
doi:10.1371/journal.pcbi.1003253
pmid:24098102
pmcid:PMC3789770
fatcat:k7t5aifspzgihg2lzihy3suqpu
Release of 50 new, drug-like compounds and their computational target predictions for open source anti-tubercular drug discovery
2015
PLoS ONE
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The 50 most attractive compounds were then progressed for evaluation in three different predictive computational biology algorithms based on structural similarity or GSK historical biological assay data ...
Papadatos G, et al. (2015) Release of 50 new, druglike compounds and their computational target predictions for open source anti-tubercular drug discovery. PLoS ONE 10(12): e0142293. ...
network of structural relationships between ligands and targets. ...
doi:10.1371/journal.pone.0142293
pmid:26642067
pmcid:PMC4671658
fatcat:rk2323qpbvd3bjunr662n4oini
Predicting Drug–Target Interactions With Multi-Information Fusion
2017
IEEE journal of biomedical and health informatics
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Experimental results on four classes of drugtarget interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. ...
The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B-and beta1-adrenergic receptors. ...
Martínez-Jiménez and Marti-Renom [43] assumed that structurally similar binding sites are likely to bind similar ligands and developed a network-based inference method, namely, nAnnoLyze, by integrating ...
doi:10.1109/jbhi.2015.2513200
pmid:26731781
fatcat:uwgzmzf5sjaqxoj6zo7aiftgkq
Computational analyses of small molecules activity from phenotypic screens
2019
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In this thesis, the use of machine learning algorithms for in silico ligand-target prediction for target deconvolution in phenotypic screening datasets was investigated. ...
A computational workflow is presented in Chapter 2, that allows to improve the coverage of mechanism of action hypotheses obtained by combining two conceptually different target prediction algorithms. ...
Network-based analyses are also employed to predict putative ligand-target pairs ( Table 3) . ...
doi:10.17863/cam.40403
fatcat:chvw7b7hmjgctkh3et7zykmyfu