openadmet/pxr-chemeleon-baseline

In this repo, we have our baseline model. It is a single task CheMeleon model trained on pEC50 data curated from ChEMBL 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.

Getting Started

Downloading the model

1. clone the model repo:

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

2. 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

3. You are now ready to use the model!

Using the model

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.

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-baseline) where you've downloaded the model.

With Docker

If you're using a gpu, run:

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

Otherwise, for cpu only:

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

Using the model

We will use this model for inference or, to predict the pIC50s of a set of molecular compounds unseen to the model. You can do this either inside the docker container as per the instructions above, or if you have installed openadmet-models on your own computer, you can use the appropriate environment.

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:

    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:

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.