build_inference_script.yaml

# run arguments:
method: diffdock # the method for which to score predictions - NOTE: must be one of (`diffdock`, `fabind`, `dynamicbind`, `neuralplexer`, `flowdock`, `rfaa`, `chai-lab`, `boltz`, `alphafold3`, `vina`, `ensemble`)
vina_binding_site_method: p2rank # the method to use for Vina binding site prediction - NOTE: must be one of (`diffdock`, `fabind`, `dynamicbind`, `neuralplexer`, `flowdock`, `rfaa`, `chai-lab`, `boltz`, `alphafold3`, `p2rank`)
ensemble_ranking_method: consensus # the method to use for ensemble ranking - NOTE: must be one of (`consensus`, `ff`)
dataset: astex_diverse # the dataset to use - NOTE: must be one of (`posebusters_benchmark`, `astex_diverse`, `dockgen`, `casp15`)
repeat_index: 1 # the repeat index which was used for inference
cuda_device_index: 0 # the CUDA device index to use for inference (for all methods except AutoDock-Vina)
output_script_dir: ${oc.env:PROJECT_ROOT}/scripts/inference # the directory in which to save the output script
pocket_only_baseline: false # whether to perform a pocket-only baseline for the PoseBusters Benchmark set - NOTE: not applicable only to `tulip`
v1_baseline: false # whether to perform the V1 baseline for DiffDock
no_ilcl: false # whether to use model weights trained with an inter-ligand clash loss (ILCL) for the CASP15 set - NOTE: only applicable to `neuralplexer`
relax_protein: false # whether to relax the protein structure before scoring - NOTE: currently in an experimental state
export_hpc_headers: true # whether to insert high-performance computing (by default, SLURM) headers into the output script
verbose: false # whether to print verbose (e.g., invalid configuration) output
# sweep arguments:
sweep: false # whether to build all combinations of method-dataset run scripts
methods_to_sweep: [
    "diffdock",
    "dynamicbind",
    "neuralplexer",
    "rfaa",
    # "chai-lab_ss",
    "chai-lab",
    # "boltz_ss",
    "boltz",
    # "alphafold3_ss",
    "alphafold3",
    "vina",
  ] # the methods to sweep
vina_binding_site_methods_to_sweep: ["p2rank"] # the Vina binding site prediction methods to sweep
ensemble_ranking_methods_to_sweep: ["consensus"] # the ensemble ranking methods to sweep - NOTE: must be one of (`consensus`, `ff`)
datasets_to_sweep: ["posebusters_benchmark", "astex_diverse", "dockgen", "casp15"] # the datasets to sweep
num_sweep_repeats: 3 # the number of repeats to run for each method-dataset sweep (if the method is a generative method)