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Ligand-Based Virtual Screening with Co-regularised Support Vector Regression.

, , , , and . ICDM Workshops, page 261-268. IEEE Computer Society, (2016)

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Co-Regularised Support Vector Regression., , , , and . ECML/PKDD (2), volume 10535 of Lecture Notes in Computer Science, page 338-354. Springer, (2017)From Activity Cliffs to Activity Ridges: Informative Data Structures for SAR Analysis., , and . J. Chem. Inf. Model., 51 (8): 1848-1856 (2011)Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map., , , and . J. Chem. Inf. Model., 52 (8): 1962-1969 (2012)Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds., , and . J. Chem. Inf. Model., 57 (4): 710-716 (2017)Introducing the 'active search' method for iterative virtual screening., , , and . J. Comput. Aided Mol. Des., 29 (4): 305-314 (2015)Introduction of an Information-Theoretic Method to Predict Recovery Rates of Active Compounds for Bayesian in Silico Screening: Theory and Screening Trials., and . J. Chem. Inf. Model., 47 (2): 337-341 (2007)Small Area-Statistik: Methoden und Anwendungen., , and . AStA Wirtschafts und Sozialstatistisches Arch., 6 (3-4): 149-191 (2013)Fatigue assessment of arc welded automotive components using local stress approaches: Application to a track control arm, , and . International Journal of Fatigue, (2011)Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity., , , and . J. Comput. Aided Mol. Des., 29 (7): 595-608 (2015)Design of chemical space networks on the basis of Tversky similarity., , , and . J. Comput. Aided Mol. Des., 30 (1): 1-12 (2016)