LigateProject / Computer-Aided-Drug-Design-workflow-HyperQueue

Ligate LiGen/Gromacs CADD workflow

This repository contains a HyperQueue workflow that implements a LiGen virtual screening + docking pipeline, and also a CADD pipeline that adds integration with GROMACS.

Both pipelines use HyperQueue to build and execute a task graph.

Installation

There are several external dependencies required to run the workflows. They can be installed in two ways, with a Dockerfile or natively on the target system.

Before installing the dependencies, you have to download AmbertTools23.tar.bz from https://ambermd.org/GetAmber.php (registration is required to download it), and put it into the deps directory.

To use the Python packages, you should have Python 3.10 or 3.11.

Docker installation

Install Docker and run:

$ docker build -t cadd .

You can convert the resulting image to Singularity/Apptainer if you need to execute it on an HPC cluster.

Native installation

Before starting to set up anything, you should have at least the following packages available:

• C/C++ compiler
• CMake
• CPython development headers (python-dev)
• MPI implementation (for compiling mpi4py)
  • Preferably OpenMPI, AmberTools seems to have some issue with MPICH
  • For example libopenmpi-dev

You will then need to install several dependencies. You can examine the Dockerfile to see how it installs these dependencies on Ubuntu 22.04.

Steps 3 - 5 are only needed for the CADD pipeline.

1. Create a virtual environment

   $ python3 -m venv venv
   $ source venv/bin/activate
   (venv) $ python3 -m pip install -U setuptools wheel pip 
   (venv) $ python3 -m pip install uv

2. Install Python dependencies

   (venv) $ uv pip sync requirements.txt

3. Install native dependencies

   (venv) $ python3 env.py install

   • The installation step will generate an env.sh file, which you should load before using this package (and before executing the check-env command):

   (venv) $ source env.sh

   • You can also run the scripts in the deps directory manualy, in the same order as in the Dockerfile.
4. Check if everything has been installed correctly

   (venv) $ python3 main.py check-env

Usage

LiGen

1. Prepare the input dataset. It needs to consists of a .pdb protein file, a .mol2 probe file, and a .smi file containing ligands.
2. Get access to an Apptainer file containing LiGen tools (it is proprietary)
3. Prepare a YAML file that configures the workflow parameters. Here is an example:

   data:
     protein_pdb: <path>
     probe_mol2: <path>
     smi: <path>
   max_molecules_per_smi: <number>

   The max_moleculer_per_smi parameter specifies the number of ligands per HyperQueue task. A number such as 10 is a reasonable default. The paths are resolved relative to the directory from which the script is executed (step 4.).
4. Execute the workflow.

   (venv) $ python3 cadd.py ligen <workdir> <params-file> <ligen-container> [--dock] [--local-cluster]

   • workdir will store intermediate files and outputs of the workflow.
   • params-file is a path to a YAML with workflow parameters (step 3).
   • ligen-container is a path to the LiGen apptainer image (step 2).
   • --dock specifies whether docking should also be performed. Without it, only virtual screening is performed.
   • --local-cluster specifies whether a new HyperQueue cluster should be created. If unset, the code will try to connect to an existing HyperQueue instance on the local node.