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Insolubility Classification with Accurate Prediction Probabilities Using a MetaClassifier

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Molecular Connectivity in Structure–activity Analysis
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Bayesian Learning for Neural Networks
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Development of Quantitative Structure−Property Relationship Models for Early ADME Evaluation in Drug Discovery. 1. Aqueous Solubility
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
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Generalized Fragment-Substructure Based Property Prediction Method
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Linear and Nonlinear Methods in Modeling the Aqueous Solubility of Organic Compounds
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Random Forest Models To Predict Aqueous Solubility
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Straightforward Recursive Partitioning Model for Discarding Insoluble Compounds in the Drug Discovery Process
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Encyclopedia of computational chemistry

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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