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Multiple conformational states in retrospective virtual screening - homology models vs. crystal structures: beta-2 adrenergic receptor case study.

, , , , и . J. Cheminformatics, (2015)

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Sabina Sauer

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Sabina Gerhart

 

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The influence of negative training set size on machine learning-based virtual screening., , и . J. Cheminformatics, 6 (1): 32 (2014)Multiple conformational states in retrospective virtual screening - homology models vs. crystal structures: beta-2 adrenergic receptor case study., , , , и . J. Cheminformatics, (2015)The influence of training actives/inactives ratio on machine learning performance., , и . J. Cheminformatics, 5 (S-1): 30 (2013)Application of Structural Interaction Fingerpints (SIFts) into post-docking analysis - insight into activity and selectivity., , , , , и . J. Cheminformatics, 5 (S-1): 28 (2013)The influence of hashed fingerprints density on the machine learning methods performance., , и . J. Cheminformatics, 5 (S-1): 25 (2013)Evaluation of different machine learning methods for ligand-based virtual screening., , и . J. Cheminformatics, 3 (S-1): 41 (2011)The influence of the inactives subset generation on the performance of machine learning methods., , и . J. Cheminformatics, (2013)Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods - A Case Study of Serotonin Receptors 5-HT6 and 5-HT7., , , , , и . Journal of Chemical Information and Modeling, 55 (4): 823-832 (2015)