nucleobench 0.1.0

Optimizers from the nucleobench package.

NucleoBench

This is the initial repo for an upcoming paper, NucleoBench: A Large-Scale Benchmark of Neural Nucleic Acid Design Algorithms.

This repo is covered by the MIT license.

This repo is intended to be used in a few ways:

1. Reproducing the results from our paper.
2. Running the NucleoBench sequence designers on custom problems.
3. Using our new designer, AdaBeam, on a custom problem.

To do these, you can clone this repo, use the Docker image (for the benchmark), or use the PyPi package for our designers.

Results

Installation & testing

Once this repo is cloned, you can make the conda/mamba/micromamba environment with:

conda env create -f environment.yml
conda activate nucleobench

To test that you've install NucleoBench, run all the unittests:

pytest nucleobench/

You can also run the integration tests, which require an internet connection:

pytest docker_entrypoint_test.py

Nucleic acid design benchmark.

Running NucleoBench

See the folder recipes for examples of how to run the designer locally.

Building a Docker image

To help deploy NucleoBench to the cloud, we've created a docker container. To build it yourself, see the top of Dockerfile for instructions. One way of creating a docker file is:

docker build -t nucleobench -f Dockerfile .