content: # REINVENT4 TOML input example for sampling
  #
  
  
  run_type = "sampling"
  device = "cuda:0"  # set torch device e.g. "cpu"
  json_out_config = "_sampling.json"  # write this TOML to JSON
  
  
  [parameters]
  
  # Uncomment one of the comment blocks below.  Each generator needs a model
  # file and possibly a SMILES file with seed structures.
  
  ## Reinvent: de novo sampling
  model_file = "priors/reinvent.prior"
  
  ## LibInvent: find R-groups for the given scaffolds
  #model_file = "priors/libinvent.prior"
  #smiles_file = "scaffolds.smi"  # 1 scaffold per line with attachment points
  
  ## LinkInvent: find a linker/scaffold to link two fragments
  #model_file = "priors/linkinvent.prior"
  #smiles_file = "warheads.smi"  # 2 warheads per line separated with '|'
  
  ## Mol2Mol: find molecules similar to the provided molecules
  #model_file = "priors/mol2mol_medium_similarity.prior"
  #smiles_file = "mol2mol.smi"  # 1 compound per line
  #sample_strategy = "beamsearch"  # multinomial or beamsearch (deterministic)
  #temperature = 1.0 # temperature in multinomial sampling
  #tb_logdir = "tb_logs"  # name of the TensorBoard logging directory
  
  ## Pepinvent
  #model_file = "priors/pepinvent.prior"
  #smiles_file = "pepinvent.smi"
  #sample_strategy = "beamsearch"  # multinomial or beamsearch (deterministic)
  #temperature = 1.0 # temperature in multinomial sampling
  #tb_logdir = "tb_logs"  # name of the TensorBoard logging directory
  
  output_file = 'sampling.csv'  # sampled SMILES and NLL in CSV format
  
  num_smiles = 157  # number of SMILES to be sampled, 1 per input SMILES
  unique_molecules = true  # if true remove all duplicatesd canonicalize smiles
  randomize_smiles = true # if true shuffle atoms in SMILES randomly
  