TorsionalDiffusionHPC is a fork of torsional-diffusion, which adds support to run it on HPC systems using Slurm and Singularity.
For more details about torsional-diffusion we refer to the original Github repo and the paper on arXiv.

• Singularity
• Slurm

1. Clone the repository and navigate to it

   git clone https://github.com/Jnelen/TorsionalDiffusionHPC
   

   cd TorsionalDiffusionHPC
   

2. Download the singularity image (~4 GB) to the singularity directory located in the main TorsionalDiffusionHPC directory. The singularity image contains all the necessary packages and dependencies to run DiffDock correctly

   wget --no-check-certificate -r "https://drive.usercontent.google.com/download?id=1Uzx7OqghIqSoNBpZ1_2V76sMvl7XXOS2&confirm=t" -O singularity/TorsionalDiffusionHPC.sif
   

   alternatively, you can build the singularity image yourself using:

   singularity build singularity/TorsionalDiffusionHPC.sif singularity/TorsionalDiffusionHPC.def
   

3. Download one of the trained models to the workdir directory from this shared Drive. I set the default model to use drugs_default, so I recommend installing this one to the workdir directory, however other models are supported as well. make the workdir to download the model to:

   mkdir workdir
   

   download the drugs_default model:

    wget --no-check-certificate -r "https://drive.usercontent.google.com/download?id=1Yez3v0H8trS4jAnrn8vdzt-R7TkM1L_U&confirm=t" -O workdir/drugs_default.zip
   

   unzip the model and remove the zip file:

   unzip workdir/drugs_default.zip -d workdir/
   

   rm workdir/drugs_default.zip
   

4. Run a test example to generate the required (hidden) .npy files. This only needs to happen once and should only take about 5-10 minutes.

   mkdir output
   

   python launch_jobs.py -l data/test.csv -out output/test -j 1
   

I attempted to provide most of the original options implemented in torsional-diffusion, while also keeping things simple. Additionally, I added some useful features (for example compressing the results, removing salts, ...) and scripts which can make general usage easier. Here is a short overview:

• --ligands LIGANDS, -l LIGANDS: The path to and sdf file or a directory of mol(2)/sdf ligand files. Csv and pkl files are also accepted as input. All of these formats are also allowed to have been compressed by gzip (.gz)
• --out_dir OUT_DIR, -out OUT_DIR, -o OUT_DIR: Directory where the output structures will be saved to
• --num_confs NUM_CONFS, -n NUM_CONFS: How many conformers to output per compound. The default value is 10
• --dump_pymol: Save .pdb file with denoising dynamics
• --pre-mmff: Run MMFF on the local structure conformer
• --post-mmff: Run MMFF on the final generated structures
• --no_energy, -ne: Skip Calculating the energies and other metrics
• --particle_guidance {0,1,2,3,4}, -pg {0,1,2,3,4}: Define which type of Particle Guidance you want to use:
  • 0: No particle guidance
  • 1: Permutation invariant minimize recall error
  • 2: Permutation invariant minimize precision error
  • 3: Non-permutation invariant minimize recall error
  • 4: Non-permutation invariant minimize precision error

• --smiles_as_id, -si: Use a molecule's smile as the ID, even if the molecule has a name
• --compress_output, -co: Compress the output pkl files using gzip
• --remove_salts, -rs: Remove salts and fragments from the input molecules
• --random_coords, -rc: Use the "useRandomCoords=True" option when generating initial RDKit conformers (more robust, but slower)
• --random_seed RANDOM_SEED, --seed RANDOM_SEED: Random seed to produce (approximate) deterministic results for identical datasets.

• --jobs JOBS, -j JOBS: Number of jobs to use
• --time TIME, -t TIME, -tj TIME: Max amount of time each job can run
• --queue QUEUE, -qu QUEUE: On which node to launch the jobs. The default value is the default queue for the user. Might need to be specified if there is no default queue configured
• --mem MEM, -m MEM: How much memory to use for each job. The default value is 4GB
• --gpu, -gpu, -GPU, --GPU: Use GPU resources. This will accelerate docking calculations if a compatible GPU is available
• --cores CORES, -c CORES: How many cores to use for each job. The default value is 1

The additional scripts are located in the scripts/ directory. Currently there are two:

• relaunchFailedJobs.py
  Sometimes jobs fail or produce errors. This can especially be annoying when running a large amount of jobs. After all jobs stopped running, but not all jobs finished successfully, you run this script to automatically rerun the jobs that didn't produce a final output.
  Usage: python scripts/relaunchFailedJobs.py <output_directory>
• joinPkls.py
  This script can join all the results from every job back together into one large (compressed) pkl. Additionally, energy csvs will also be joined if they were generated.
  Usage: singularity run singularity/TorsionalDiffusionHPC.sif python scripts/joinPkls.py <output_directory>

MIT