Ersilia Model Hub Identifier: eos39dp
4.3K
Pharmacokinetics Profiler (PhaKinPro) predicts the pharmacokinetic (PK) properties of drug candidates. It has been built using a manually curated database of 10.000 compounds with information for 12 PK endpoints. Each model provides a multi-classifier output for a single endpoint, along with a confidence estimate of the prediction and whether the query molecule is within the applicability domain of the model.
This model was incorporated on 2024-05-03.Last packaged on 2025-10-10.
eos39dpphakinproAnnotationProperty calculation or predictionAnyAnyADMECompound118FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| hs_15min | float | high | Probability of hepatic stability <=50% at 15 min |
| hs_30min | float | high | Probability of hepatic stability <=50% at 30 min |
| hs_60min | float | high | Probability of hepatic stability <=50% at 60 min |
| mhl_subcellular | float | high | Probability of sub-cellular hepatic Half-life <= 30 minutes |
| mhl_tissue | float | high | Probability of tissue hepatic Half-life <= 30 minutes |
| rc_01 | float | high | Probability of renal clearance below 0.10 ml/min/kg |
| rc_05 | float | high | Probability of renal clearance below 0.50 ml/min/kg |
| rc_1 | float | high | Probability of renal clearance below 1.00 ml/min/kg |
| bbb_permeability | float | high | Probability of permeating the blood-brain barrier |
| cns_activity | float | high | Probability of exhibiting central nervous system activity |
10 of 18 columns are shown
LocalExternalAMD64, ARM645023972413.88Computational Performance (seconds):
28.9818.91138.31Peer reviewed2024This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a MIT license.
Notice: Ersilia grants access to models as is, directly from the original authors, please refer to the original code repository and/or publication if you use the model in your research.
To use this model locally, you need to have the Ersilia CLI installed. The model can be fetched using the following command:
# fetch model from the Ersilia Model Hub
ersilia fetch eos39dp
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos39dp
# generate an example file
ersilia example -n 3 -f my_input.csv
# run the model
ersilia run -i my_input.csv -o my_output.csv
# close the model
ersilia close
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Content type
Image
Digest
sha256:bed073e97…
Size
893.5 MB
Last updated
about 1 year ago
docker pull ersiliaos/eos39dpPulls:
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