openadmet/logd-chemprop_foundation_minimol-v1

This is a ChemProp model pretrained with minimol-featurized data trained on LogD data from the OpenADMET ExpansionRx blind challenge.

For more information about how this model was trained see this blog post.

Getting Started

Pre-requisites

IMPORTANT NOTE You will need git lfs installed.

Downloading the model

1. Clone the model repo:

git clone https://huggingface.co/openadmet/logd-chemprop_foundation_minimol-v1/

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

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:

1. 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

2. 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 .

3. 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

1. 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 (./logd-chemprop_foundation_minimol-v1) where you've downloaded the model.
2. For CPU only, run:

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

3. For GPU, run:

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

Using the model

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>

We will use this model for inference, aka predict the fraction unbound values 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. You can run this directly in your command line.

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.

In our example, this outputs a file called predictions.csv which will have predicted (the OADMET_PRED columns) of LogD values the model was trained on.