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Solubility prediction by recursive partitioning.

Xiaoyang Xia·Edward Maliski·Janet Cheetham·Leszek Poppe
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Abstract

PURPOSE: To build and test a computational model for predicting small molecule solubility, to improve the cost-effectiveness of the selection of vendor compounds suitable for nuclear magnetic resonance (NMR) screening. METHODS: A simple recursive partitioning decision tree-based classification model was generated utilizing "off-the-shelf" commercial software from Accelrys Inc., with a training set of 1992 compounds based on a series of calculated topologic and physical properties. The predictive ability of the decision tree was then assessed by employing it to classify a test set of 2851 vendor compounds, and the classification was subsequently used to guide the purchase of 686 compounds for the purpose of NMR screening. RESULTS: When the decision tree was used to guide purchasing, the percentage of "acceptable" compounds suitable for NMR screening doubled compared with the use of a simple cLogP cutoff, improving the successful selection rate from 25% to 50%. CONCLUSIONS: A simple recursive partitioning decision tree may successfully be used to improve cost-effectiveness by reducing the wastage associated with the unnecessary purchase of vendor compounds unsuitable for NMR screening because of insolubility.

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Computer SimulationCost-Benefit AnalysisDecision Making, Computer-AssistedMagnetic Resonance SpectroscopyModels, ChemicalMolecular StructurePharmaceutical PreparationsQuantitative Structure-Activity RelationshipSoftwareSolubilityJournal ArticleResearch Support, Non-U.S. Gov't

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Xiaoyang Xia, Edward Maliski, Janet Cheetham, Leszek Poppe. 2003. Solubility prediction by recursive partitioning.. https://doi.org/10.1023/a%3A1026195503465

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