https://github.com/ivanbuccella/sf2bio

Deep reinforcement learning for de novo drug design: a ReLeaSe method execution on a Docker Environment
https://github.com/ivanbuccella/sf2bio

cuda deep-learning deep-reinforcement-learning docker docker-compose machine-learning nvidia-cuda nvidia-docker reinforcement-learning release release-method

Last synced: about 1 month ago
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Deep reinforcement learning for de novo drug design: a ReLeaSe method execution on a Docker Environment

• Host: GitHub
• URL: https://github.com/ivanbuccella/sf2bio
• Owner: IvanBuccella
• License: mit
• Created: 2023-01-05T11:41:02.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-12T15:29:43.000Z (about 2 years ago)
• Last Synced: 2024-11-12T14:22:54.068Z (3 months ago)
• Topics: cuda, deep-learning, deep-reinforcement-learning, docker, docker-compose, machine-learning, nvidia-cuda, nvidia-docker, reinforcement-learning, release, release-method
• Language: Dockerfile
• Homepage:
• Size: 16.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Deep reinforcement learning for de novo drug design: a ReLeaSe method execution on a Docker Environment

## Citation

This work runs the developed [ReLeaSE](https://github.com/isayev/ReLeaSE/) method on a Docker Environment. Further details about the method can be found in this paper: Mariya Popova, Olexandr Isayev, Alexander Tropsha. _Deep Reinforcement Learning for de-novo Drug Design_. Science Advances, 2018, Vol. 4, no. 7, eaap7885. DOI: [10.1126/sciadv.aap7885](http://dx.doi.org/10.1126/sciadv.aap7885)

## Tutorial Structure

- **[Installation](#installation)**

  - **[Prerequisites](#prerequisites)**

  - **[Repository](#repository)**

  - **[Environment Variables](#environment-variables)**

  - **[Build](#build)**

  - **[Run Docker Services](#run-docker-services)**

## Installation

### Prerequisites

- Linux OS or WSL 2 on a Windows 10 (or higher) machine. If you use WSL 2, follow the [official guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#getting-started-with-cuda-on-wsl).

- A Modern NVIDIA GPU, compatible with [CUDA 11.3](https://developer.nvidia.com/cuda-11.3.0-download-archive).

- Docker and Docker Compose (Application containers engine). Install it from [here](https://www.docker.com).

- The Docker GPU Support enabled on the machine; check it out [here](https://docs.docker.com/compose/gpu-support/).

- The Nvidia Container Toolkit. Install it from [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#install-guide).

Note that you do not need to install the CUDA Toolkit on the host system.

Note that you have to install the NVIDIA drivers on your system.

### Repository

Clone the repository:

```sh

$ git clone https://github.com/IvanBuccella/SF2Bio

$ cd docker

```

### Environment Variables

Set your own environment variables (the jupyter notebook port) by using the `.env-sample` file. You can just duplicate and rename it in `.env`.

### Build

Build the local environment with Docker:

```sh

$ docker-compose build

```

### Run Docker Services

```sh

$ docker-compose up

```

### Enjoy :-)

You can execute an example that uses method by visiting the `http://localhost:${HTTP_PORT}/LogP_optimization_demo.ipynb ` URL.