honda2023.qspr {httk} | R Documentation |
Predicted Caco-2 Apical-Basal Permeabilities
Description
Honda et al. (2023) describes the construction of a machine-learning quantitative structure-property relationship (QSPR )model for in vitro Caco-2 membrane permeabilites. That model was used to make chemical-specific predictions provided in this table.
Usage
honda2023.qspr
Format
An object of class data.frame
with 14033 rows and 5 columns.
Details
Column Name | Description | Units |
DTXSID | EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) | |
Pab.Class.Pred | Predicted Pab rate of slow (1), moderate (2), or fast (3) | |
Pab.Pred.AD | Whether (1) or not (0) the chemical is anticipated to be withing the QSPR domain of applicability | |
CAS | Chemical Abstracts Service Registry Number | |
Pab.Quant.Pred | Median and 95-percent interval for values within the predicted class's training data moderate (2), or fast (3) | 10^-6 cm/s |
References
Honda GS, Kenyon EM, Davidson-Fritz S, Dinallo R, El Masri H, Korol-Bexell E, Li L, Angus D, Pearce RG, Sayre RR, others (2025). “Impact of gut permeability on estimation of oral bioavailability for chemicals in commerce and the environment.” ALTEX-Alternatives to animal experimentation, 42(1), 56–74. doi:10.14573/altex.2403271.
See Also
[Package httk version 2.6.1 Index]