openadmet/pxr-chemeleon-baseline

!!!DISCLAIMER: This is a baseline model trained on publicly available data. While we've done our best to curate the data, the model performance is quite poor. Proceed with caution.

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.

Due to the sparsity of PXR pEC50s on ChEMBL, this is a pretty terrible model. To be used as a baseline ONLY.

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.