| load_sipes2017 {httk} | R Documentation |
Load CLint and Fup QSPR predictions from Sipes et al 2017.
Description
This function returns an updated version of
chem.physical_and_invitro.data
that includes quantitative
structure-property relationship (QSPR) predictions from Simulations Plus'
ADMET predictor as
used in Sipes et al. 2017, included in sipes2017.
Usage
load_sipes2017(overwrite = FALSE, target.env = .GlobalEnv)
Arguments
overwrite |
Only matters if load.image=FALSE. If overwrite=TRUE then existing data in chem.physical_and_invitro.data will be replaced by any predictions in Sipes et al. (2017) that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way. |
target.env |
The environment where the new
|
Details
Because Clint and Fup are the only measurements required for many HTTK models,
changing the number of chemicals for which a value is available will change
the number of chemicals which are listed with the get_cheminfo
command. Use the command reset_httk to return to the initial
(measured only) chem.physical_and_invitro.data (for all
parameters).
Value
data.frame |
An updated version of
|
Author(s)
Robert Pearce and John Wambaugh
References
Sipes, Nisha S., et al. "An intuitive approach for predicting potential human health risk with the Tox21 10k library." Environmental Science & Technology 51.18 (2017): 10786-10796.
See Also
Examples
# Count how many chemicals for which HTTK is available without the QSPR:
num.chems <- length(get_cheminfo())
print(num.chems)
# For chemicals with Sipes et al. (2017) Clint and Fup QSPR predictions,
# add them to our chemical information wherever measured values are
# unavailable:
load_sipes2017()
# Here's a chemical we didn't have before (this one is a good test since the
# logP is nearly 10 and it probably wouldn't work in vitro):
calc_css(chem.cas="26040-51-7")
# Let's see how many chemicals we have now with the Sipes et al. (2017)
# predictions data loaded:
length(get_cheminfo())
# Now let us reset the chemical data to the initial version:
reset_httk()
# We should be back to our original number:
num.chems == length(get_cheminfo())