httk (version 2.5.0)

load_sipes2017: 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)

Value

data.frame

An updated version of chem.physical_and_invitro.data.

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 chem.physical_and_invitro.data is loaded. Defaults to global environment.

Author

Robert Pearce and John Wambaugh

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).

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

reset_httk

get_cheminfo

Examples

# \donttest{
# 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())
# }