Installation

Prerequisites

• Install Pytorch: https://pytorch.org/TensorRT/tutorials/installation.html

• Install Pytorch Geometric: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html

Install Command:

pip install olorenchemengine[full]

Here are some common error messages and solutions: https://oloren-ai.notion.site/Oloren-ChemEngine-Installation-FAQ-f2edec771a7f4350af5fdc361d494604

Install Live Version from GitHub Command

pip install --upgrade "olorenchemengine[full] @ git+https://github.com/Oloren-AI/olorenchemengine.git"

Docker

Alternatively, you can also run OCE from one of our docker images. After cloning the repo, just run:

docker build -t oce:latest -f docker/Dockerfile.gpu . # build the docker image
docker run -it -v ~/.oce:/root/.oce oce:latest python # run the docker image

Replace “.gpu” with “.cpu” in the docker path if you want to run the project in a dockerized environment.

Basic Usage

We have an examples folder, which we’d highly reccomend you checkout–1A and 1B in particular–the rest of the examples can be purused when the topics come up.

Notice

Maintaining and developing Oloren ChemEngine requires a lot of resources. As such, we would like to log for each evaluated model the model hyperparameters, the model performance metrics and a unique, non-identifying hash of the dataset. These logs are used to improve our models. Below is a representative example of such a log:

{dataset_hash: "149eae5c763afcc14f6355007df298b05f4a51c6a334ea933fbe7fc496adb271",

metric_direction: null,

metrics: "{"Average Precision": 0.9479992350277128, "ROC-AUC": 0.7450549450549451}",

name: "BaseBoosting 1zpI0dIb",

params: "{"BC_class_name": "BaseBoosting", "args": [[{"BC_class_name": "RandomForestModel", "args": [{"BC_class_name": "DescriptastorusDescriptor", "args": ["morgan3counts"], "kwargs": {"log": true, "scale": null}}], "kwargs": {"bootstrap": true, "criterion": "entropy", "max_features": "log2", "n_estimators": 2000, "max_depth": null, "class_weight": null}}, {"BC_class_name": "RandomForestModel", "args": [{"BC_class_name": "DescriptastorusDescriptor", "args": ["morganchiral3counts"], "kwargs": {"log": true, "scale": null}}], "kwargs": {"bootstrap": true, "criterion": "entropy", "max_features": "log2", "n_estimators": 2000, "max_depth": null, "class_weight": null}}, {"BC_class_name": "RandomForestModel", "args": [{"BC_class_name": "DescriptastorusDescriptor", "args": ["morganfeature3counts"], "kwargs": {"log": true, "scale": null}}], "kwargs": {"bootstrap": true, "criterion": "entropy", "max_features": "log2", "n_estimators": 2000, "max_depth": null, "class_weight": null}}, {"BC_class_name": "RandomForestModel", "args": [{"BC_class_name": "DescriptastorusDescriptor", "args": ["rdkit2dnormalized"], "kwargs": {"log": true, "scale": null}}], "kwargs": {"bootstrap": true, "criterion": "entropy", "max_features": "log2", "n_estimators": 2000, "max_depth": null, "class_weight": null}}, {"BC_class_name": "RandomForestModel", "args": [{"BC_class_name": "OlorenCheckpoint", "args": ["default"], "kwargs": {"log": true, "num_tasks": 2048}}], "kwargs": {"bootstrap": true, "criterion": "entropy", "max_features": "log2", "n_estimators": 2000, "max_depth": null, "class_weight": null}}]], "kwargs": {"log": true, "n": 1, "oof": false, "nfolds": 5}}"}

The dataset hash is created with the following code:

import joblib

dataset_hash = joblib.hash(X) + joblib.hash(y)

This means that we log no therapeutics-related data whatsoever. We just log hashes of model performance.

If you would still prefer a logging-free version, please fill out the following form to obtain a version with all logging code excised: https://y09gl0qf49q.typeform.com/to/brGMidJ0.

We also require contributor agreements for all versions of Oloren ChemEngine.