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Classification Tree Models for the Prediction of Blood−Brain Barrier Passage of Drugs

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What this work says

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

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Handbook of Molecular Descriptors

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CODES/Neural Network Model: a Useful Tool for in Silico Prediction of Oral Absorption and Blood‐Brain Barrier Permeability of Structurally Diverse Drugs

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Classification and regression trees

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In silico ADME modelling: prediction models for blood–brain barrier permeation using a systematic variable selection method

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Separation methods for estimating octanol–water partition coefficients

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Determination of solute lipophilicity, as log P(octanol) and log P(alkane) using poly(styrene–divinylbenzene) and immobilised artificial membrane stationary phases in reversed-phase high-performance liquid chromatography

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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1

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Correlation and prediction of a large blood–brain distribution data set—an LFER study

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Brain/blood distribution described by a combination of partition coefficient and molecular mass

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Handbook of Chemometrics and Qualimetrics: Part B

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Application of hydrogen bonding calculations in property based drug design

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Predicting Blood−Brain Barrier Permeation from Three-Dimensional Molecular Structure

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Theoretical Calculation and Prediction of Brain–Blood Partitioning of Organic Solutes Using MolSurf Parametrization and PLS Statistics

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Computational models to predict blood–brain barrier permeation and CNS activity

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Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)

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Evaluation of boosted regression trees (BRTs) and two‐step BRT procedures to model and predict blood‐brain barrier passage

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Classification Tree Models for the Prediction of Blood—Brain Barrier Passage of Drugs.

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Boosted regression trees, multivariate adaptive regression splines and their two-step combinations with multiple linear regression or partial least squares to predict blood–brain barrier passage: A case study

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