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A Consensus Neural Network-Based Technique for Discriminating Soluble and Poorly Soluble Compounds
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1521-3773(19991004)38:19<2894::aid-anie2894>3.0.co;2-fopenalex-snapshot
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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inferred← citeshttps://doi.org/10.1016/j.chemolab.2004.09.005openalex-snapshot
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