Installation Guide

Installation Guide#

Install dependencies#

Nearl requires AmberTools (specifically PyTraj, the python wrapper of CPPTRAJ), OpenBabel, and RDKit, for its functionality (we tested with Python 3.9). PyTorch is the deep learning framework used for pre-built ML models. Note that you may need to manually modify the pytorch-cuda version to match your CUDA version. The following command creates a new Python environment named nearl_env with the required dependencies, assuming micromamba is the package manager in use. You can replace micromamba with conda or mamba if necessary.

git clone https://github.com/miemiemmmm/Nearl
cd Nearl
micromamba env create -f requirements.yml

Install Nearl#

The development and tests are performed on Linux(Ubuntu), and the software is not guaranteed to work on other operating systems. You can install Nearl using one of the following methods:

Installation from GitHub: Install directly from the repository.

pip install git+https://github.com/miemiemmmm/Nearl

Installation from Source: Clone the repository and install Nearl from the source.

git clone https://github.com/miemiemmmm/Nearl
cd Nearl
pip install .

Note

To correctly compile GPU code, ensure that the CUDA_COMPUTE_CAPABILITY is set appropriately for your GPU. For older devices, adjust the CUDA_COMPUTE_CAPABILITY to match the CUDA architecture. The current default value is sm_80.

Optional dependencies#

Optional dependencies are available and can be obtained from their respective repositories: