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In Silico Prediction of Buffer Solubility Based on Quantum-Mechanical and HQSAR- and Topology-Based Descriptors

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

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A Temperature-Dependent Quantum Mechanical/Neural Net Model for Vapor Pressure
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Support Vector Machines for the Estimation of Aqueous Solubility
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Enhanced 3D-Databases: A Fully Electrostatic Database of AM1-Optimized Structures
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QM/NN QSPR Models with Error Estimation: Vapor Pressure and LogP
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Prediction of Aqueous Solubility of a Diverse Set of Compounds Using Quantitative Structure−Property Relationships
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The size of molecules
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Adaptive pattern recognition and neural networks

What points at this work

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

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In Silico ADMET Traffic Lights as a Tool for the Prioritization of HTS Hits
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