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Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze

Francisco Martínez-Jiménez, Marc A. Marti-Renom, Avner Schlessinger
2015 PLoS Computational Biology  
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

Francisco Martínez-Jiménez, George Papadatos, Lun Yang, Iain M. Wallace, Vinod Kumar, Ursula Pieper, Andrej Sali, James R. Brown, John P. Overington, Marc A. Marti-Renom, Alexander Donald MacKerell
2013 PLoS Computational Biology  
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

María Jose Rebollo-Lopez, Joël Lelièvre, Daniel Alvarez-Gomez, Julia Castro-Pichel, Francisco Martínez-Jiménez, George Papadatos, Vinod Kumar, Gonzalo Colmenarejo, Grace Mugumbate, Mark Hurle, Vanessa Barroso, Rob J. Young (+13 others)
2015 PLoS ONE  
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

Lihong Peng, Bo Liao, Wen Zhu, Zejun Li, Keqin Li
2017 IEEE journal of biomedical and health informatics  
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

Azedine Zoufir, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository, Andreas Bender
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