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Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

, , , , , , and . J. Comput. Aided Mol. Des., 21 (5): 269-280 (2007)

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Alpha Shapes Applied to Molecular Shape Characterization Exhibit Novel Properties Compared to Established Shape Descriptors., , , and . Journal of Chemical Information and Modeling, 49 (10): 2231-2241 (2009)Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds., , , , , , and . J. Comput. Aided Mol. Des., 21 (5): 269-280 (2007)Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening., , , and . J. Chem. Inf. Model., 58 (9): 2000-2014 (2018)Cheminformatics., , , , , , , , , and . Commun. ACM, 55 (11): 65-75 (2012)Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study., , , , and . J. Cheminformatics, 13 (1): 39 (2021)Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules., , , , , , and . J. Cheminformatics, (2015)Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling., , , , and . J. Cheminformatics, (2015)Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules., , , , and . J. Cheminformatics, (2015)Computer-aided design of multi-target ligands at A1R, A2AR and PDE10A, key proteins in neurodegenerative diseases., , , , , , , , , and 2 other author(s). J. Cheminformatics, 9 (1): 67:1-67:19 (2017)Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data., , , , , , and . J. Cheminformatics, 15 (1): 56 (December 2023)