REINVENT4

About:

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. REINVENT uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

This repository provides a Python-based tool to access the inference functions of REINVENT4 via a REST API. Once the API is set up, use the OpenAD Toolkit to easily access the inference functions of REINVENT4.

More information on REINVENT4 can be found at:

• https://github.com/MolecularAI/REINVENT4
• https://link.springer.com/article/10.1186/s13321-024-00812-5

More information on the OpenAD Toolkit and OpenAD Service Utilities:

• https://accelerate.science/projects/openad
• https://github.com/acceleratedscience/open-ad-toolkit
• https://github.com/acceleratedscience/openad_service_utils

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Deployment Options

• Deployment locally using a Python virtual environment

• Deployment locally via container

• Deployment locally via compose

• Deployment On OpenShift

• Deployment via Sky Pilot

Note: All of these deployment options will allow you to access SMI-TED functions using the OpenAD Toolkit. You may want to first install the Toolkit in its own Python environment before proceeding with deploying the SMI-TED utility.

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Deployment locally using a Python virtual environment

You will need a Python level of 3.11 & to follow these installation directions:

1. Create and activate a Python virtual environment, and install REINVENT4 per the "Installation" instructions at this site (ignore the requirement to use Python 3.10):
   https://github.com/MolecularAI/REINVENT4
   

2. Install the OpenAD Service Utilities in the new virtual environment with the following command:
   pip install git+https://github.com/acceleratedscience/openad_service_utils.git
   

3. Clone this repo into a new directory:
   git clone https://github.com/acceleratedscience/openad_reinvent4
   

4. Change directory to openad_reinvent4.
   

5. Start the server with the following command:
   python app.py
   

6. Open a new terminal session.

7. In the new terminal session start the OpenAD Toolkit:
   openad
   

8. At the OpenAD Toolkit command line execute the following command to create a new service for accessing the local server started in step 7:
   catalog model service from remote 'http://127.0.0.1:8080/' as props
   

9. At the OpenAD Toolkit command line execute the following command to view the available commands:
   props ?

Deployment locally via container

Prerequisites: Make sure you have Docker and the Docker Buildx plugin installed on your system.

1. Clone this repo into a new directory:
   git clone https://github.com/acceleratedscience/openad_smi_ted
   

2. To build the Docker image, change directory to openad_smi_ted then start the build with the following command:
   docker build -t smi-ted-app .
   

3. After the build is complete, execute the following command to run the container and have the server available on port 8080: docker run -p 8080:8080 smi-ted-app

4. Open a new terminal session and start the OpenAD Toolkit:
   openad
   

5. At the OpenAD Toolkit command line execute the following command to create a new service for accessing the local server started in step 3:
   catalog model service from remote 'http://0.0.0.0:8080/' as sm
   

6. At the OpenAD Toolkit command line execute the following command to view the available commands:
   sm ?

Deployment On OpenShift

Install Helm Chart

helm install smi-ted ./helm-chart

Start a new build

oc start-build smi-ted-build

Wait for the build to complete

LATEST_BUILD=$(oc get builds | grep 'smi-ted-build-' | awk '{print $1}' | sort -V | tail -n 1)

oc wait --for=condition=Complete build/$LATEST_BUILD --timeout=15m

Run request test (pod may take some time to initialize so curl request may fail. try again.)

curl "http://$(oc get route smi-ted-openad-model -o jsonpath='{.spec.host}')/health"

Deployment locally via Compose

run on the command line `mkdir -p ~/.openad_models`

Note:
Initially downloading models may take some time, this will be prompted by your first request. To pre-load models you can run the following

mkdir -p ~/.openad_models/properties/molecules && aws s3 sync s3://ad-prod-biomed/molecules/smi_ted/ /tmp/.openad_models/properties/molecules/smi_ted --no-sign-request --exact-timestamps
it does require installing the AWS cli which can be found here..

https://docs.aws.amazon.com/cli/latest/userguide/getting-started-quickstart.html

then using Podman or Docker run the following in the same directory as the compose.yaml file:

step 1:

(podman or docker) compose create

step 2:

(podman or docker) compose start

the service will start on poert 8080 change this in the compose file if you wish it to run on another port.

Step 3:

In openad run the following command catalog model service from remote 'http://127.0.0.1:8080/' as sm

Notes

• The container used is https://quay.io/ibmdpdev/openad_smi_ted:latest

• You can use the compose.yaml file rather than download the entire repository

https://github.com/acceleratedscience/openad_smi_ted/blob/main/compose.yaml

Deployment via Sky Pilot

Support for skypilot on AWS is coming soon