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Rodríguez-Pérez, Raquel, Vogt, Martin, Bajorath, Jürgen (2017) Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. Journal of Chemical Information and Modeling, 57 (4) 710-716 doi:10.1021/acs.jcim.7b00088

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Reference TypeJournal (article/letter/editorial)
TitleInfluence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds
JournalJournal of Chemical Information and Modeling
AuthorsRodríguez-Pérez, RaquelAuthor
Vogt, MartinAuthor
Bajorath, JürgenAuthor
Year2017 (April 24)Volume57
Issue4
PublisherAmerican Chemical Society (ACS)
DOIdoi:10.1021/acs.jcim.7b00088Search in ResearchGate
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Mindat Ref. ID14612912Long-form Identifiermindat:1:5:14612912:6
GUID0
Full ReferenceRodríguez-Pérez, Raquel, Vogt, Martin, Bajorath, Jürgen (2017) Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. Journal of Chemical Information and Modeling, 57 (4) 710-716 doi:10.1021/acs.jcim.7b00088
Plain TextRodríguez-Pérez, Raquel, Vogt, Martin, Bajorath, Jürgen (2017) Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. Journal of Chemical Information and Modeling, 57 (4) 710-716 doi:10.1021/acs.jcim.7b00088
In(2017, April) Journal of Chemical Information and Modeling Vol. 57 (4) American Chemical Society (ACS)

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Xerxa, Elena, Vogt, Martin, Bajorath, Jürgen (2024) Influence of Data Curation and Confidence Levels on Compound Predictions Using Machine Learning Models. Journal of Chemical Information and Modeling, 64 (24). doi:10.1021/acs.jcim.4c01573
Balfer, Jenny, Bajorath, Jürgen (2015) Visualization and Interpretation of Support Vector Machine Activity Predictions. Journal of Chemical Information and Modeling, 55 (6) 1136-1147 doi:10.1021/acs.jcim.5b00175
Heikamp, Kathrin, Bajorath, Jürgen (2013) Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations. Journal of Chemical Information and Modeling, 53 (4) 791-801 doi:10.1021/ci400090t
Heikamp, Kathrin, Bajorath, Jürgen (2013) Comparison of Confirmed Inactive and Randomly Selected Compounds as Negative Training Examples in Support Vector Machine-Based Virtual Screening. Journal of Chemical Information and Modeling, 53 (7) 1595-1601 doi:10.1021/ci4002712
Miyao, Tomoyuki, Funatsu, Kimito, Bajorath, Jürgen (2019) Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling. Journal of Chemical Information and Modeling, 59 (3) 983-992 doi:10.1021/acs.jcim.8b00584
Horvath, Dragos, Marcou, Gilles, Varnek, Alexandre, Kayastha, Shilva, de la Vega de León, Antonio, Bajorath, Jürgen (2016) Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression. Journal of Chemical Information and Modeling, 56 (9) 1631-1640 doi:10.1021/acs.jcim.6b00359
Geppert, Hanna, Horváth, Tamás, Gärtner, Thomas, Wrobel, Stefan, Bajorath, Jürgen (2008) Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds. Journal of Chemical Information and Modeling, 48 (4) 742-746 doi:10.1021/ci700461s
Vogt, Martin, Bajorath, Jürgen (2013) Similarity Searching for Potent Compounds Using Feature Selection. Journal of Chemical Information and Modeling, 53 (7) 1613-1619 doi:10.1021/ci4003206
Heikamp, Kathrin, Hu, Xiaoying, Yan, Aixia, Bajorath, Jürgen (2012) Prediction of Activity Cliffs Using Support Vector Machines. Journal of Chemical Information and Modeling, 52 (9) 2354-2365 doi:10.1021/ci300306a
Ahmadi, Mohsen, Vogt, Martin, Iyer, Preeti, Bajorath, Jürgen, Fröhlich, Holger (2013) Predicting Potent Compounds via Model-Based Global Optimization. Journal of Chemical Information and Modeling, 53 (3) 553-559 doi:10.1021/ci3004682
Vogt, Martin, Bajorath, Jürgen (2007) Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening:  Theory and Screening Trials. Journal of Chemical Information and Modeling, 47 (2) 337-341 doi:10.1021/ci600418u
 
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