(Q51924209)

17 April 2018

title

Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach. (English)

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17 April 2018

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Caterina Ghio

3

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Riccardo Leonardi

4

object named as

Riccardo Leonardi

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17 April 2018

author name string

Armida Di Fenza

1

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17 April 2018

Giuliano Alagona

2

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Alessandro Giolitti

5

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17 April 2018

Andrea Madami

6

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publication date

30 January 2007

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17 April 2018

Journal of Computer - Aided Molecular Design

published in

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17 April 2018

volume

21

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17 April 2018

issue

4

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17 April 2018

page(s)

207-221

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17 April 2018

Toward an optimal procedure for variable selection and QSAR model building.

cites work

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Predicting blood-brain barrier permeation from three-dimensional molecular structure.

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Training feedforward networks with the Marquardt algorithm.

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In silico approaches for predicting ADME properties of drugs

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A human colonic cell line sharing similarities with enterocytes as a model to examine oral absorption: advantages and limitations of the Caco-2 model

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12 December 2020

Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks

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12 December 2020

Studying the explanatory capacity of artificial neural networks for understanding environmental chemical quantitative structure-activity relationship models.

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Prediction of human intestinal absorption of drug compounds from molecular structure.

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Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability

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Prediction of Caco-2 cell permeability using a combination of MO-calculation and neural network.

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A computational procedure for determining energetically favorable binding sites on biologically important macromolecules

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Drug-like properties and the causes of poor solubility and poor permeability

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10.1007/S10822-006-9098-3

Identifiers

DOI

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17 April 2018

journals/jcamd/FenzaAGLGM07

DBLP publication ID

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DBLP Dataset 2021-01-02

28 January 2021

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17 April 2018