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Prediction of partition coefficient based on atom‐type electrotopological state indices

https://doi.org/10.1021/js980266s
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Substituent Constants for Correlation Analysis in Chemistry and Biology.
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Atom/Fragment Contribution Method for Estimating Octanol–Water Partition Coefficients
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Efficient Partition of Learning Data Sets for Neural Network Training
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Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation
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Neural network studies. 1. Comparison of overfitting and overtraining
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Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information
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Assessment of n-Octanol/Water Partition Coefficient: When Is the Assessment Reliable?
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A New Atom-Additive Method for Calculating Partition Coefficients
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Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks
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Aqueous Solubility Prediction of Drugs Based on Molecular Topology and Neural Network Modeling
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Calculation of hydrophobic constant (log P) from .pi. and f constants
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Molecular Size Based Approach To Estimate Partition Properties for Organic Solutes
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Neural Network Modeling for Estimation of the Aqueous Solubility of Structurally Related Drugs
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Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans
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An Electrotopological-State Index for Atoms in Molecules
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Prediction of water–octanol partition coefficients using theoretical descriptors derived from the molecular surface area and the electrostatic potential
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Comparison of Reliability of log P Values for Drugs Calculated by Several Methods.

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