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openadmet/
pxr-chemeleon-v1

Name: pxr-chemeleon-v1
Availability: InStock

chemistry

biology

apache-2.0

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We have updated our microsomal clearance model with the latest available public data from our OpenADMET PXR Blind Challenge. This is model is a single task CheMeleon model trained on pEC50 data generated by Octant for PXR.

It is a no split model, meaning it has been trained with no data allocated to validation and test sets and with just a training set of 1.0.

See instructions below to test out this updated model!

Getting Started

Pre-requisites

IMPORTANT NOTE You will need git lfs installed.

Downloading the model

Clone the model repo:

git clone https://huggingface.co/openadmet/pxr-chemeleon-v1/

Change to the repo directory. Ensure you have git lfs installed for the repo and get the large model files:

git lfs install
git lfs pull

Option A: Running the model locally

We highly recommend you have the Anvil framework from openadmet-models installed in an environment (called openadmet-models) for ease of use and full utilization of OpenADMET's models. The installation instructions can be found here and also below:

You can install openadmet-models via our GitHub package. If you want the latest development version, clone the repository and install in editable mode:

git clone git@github.com:OpenADMET/openadmet-models.git

Set up an environment using the provided files in devtools/conda-envs.

cd openadmet-models/
conda env create -f devtools/conda-envs/openadmet-models.yaml
conda activate openadmet-models
pip install -e .

If you want to use GPU acceleration, ensure you have the appropriate CUDA toolkit installed and use the openadmet-models-cuda.yaml file instead:

conda env create -f devtools/conda-envs/openadmet-models-gpu.yaml
conda activate openadmet-models
pip install -e .

Option B: Running the model with Docker

Alternatively, you can also use Docker to spin up a containerized pre-installed environment to run openadmet-models. Just be sure you are mounting the correct folder (./pxr-chemeleon-v1) where you've downloaded the model.
For CPU only, run:

docker run -it --user=root --rm  \
    -v ./pxr-chemeleon-v1:/home/mambauser/model:rw \
    all ghcr.io/openadmet/openadmet-models:main

For GPU, run:

docker run -it --user=root --rm  \
    -v ./pxr-chemeleon-v1:/home/mambauser/model:rw \
    --runtime=nvidia 
    --gpus 
    all ghcr.io/openadmet/openadmet-models:main

Using the model

We will use this model for inference, aka predict the pEC50s of a set of molecular compounds unseen to the model. For demonstration purposes, we have provided a small subset of compounds from a ZINC deck in the file compounds_for_inference.csv.

The generic command to run our inference pipeline is:

bash

openadmet predict \
    --input-path <the path to the data to predict on> \
    --input-col <the column to of the data to predict on, often SMILES> \
    --model-dir <the anvil_training directory of the model to predict with> \
    --output-csv <the path to an output CSV to save the predictions to> \
    --accelerator <whether to use gpu or cpu, defaults to gpu>

You can run this directly in your command line, OR you can use the bash script we've provided, run_model_inference.sh.

For our working example, this command becomes:

bash

openadmet predict \
    --input-path compounds_for_inference.csv \
    --input-col OPENADMET_CANONICAL_SMILES \
    --model-dir anvil_training/ \
    --output-csv predictions.csv \
    --accelerator cpu

You can easily substitute your own set of compounds, simply modify the --input-path and --input-col arguments for your specific dataset. If you want to use a GPU (reccomended) substitute accelerator gpu in the above.

In our example, this outputs a file called predictions.csv which will have predicted (the OADMET_PRED columns) pEC50 values for the PXR target:

OADMET_PRED_chemprop-chembl_pchembl_value_mean,
OADMET_STD_chemprop-chembl_pchembl_value_mean

NOTE In this example, the standard deviation (OADMET_STD) columns are empty because uncertainty cannot be estimated unless training an ensemble of models. For further details, visit our docs.