content: # REINVENT4 TOML input example for transfer learning
  #
  # Focus a given model towards a set of input SMILES.  This can also be used to
  # start from scratch from and an untrained prior.
  
  
  run_type = "transfer_learning"
  device = "cuda:0"  # set torch device e.g. "cpu"
  tb_logdir = "tb_TL"  # name of the TensorBoard logging directory
  json_out_config = "json_transfer_learning.json"  # write this TOML to JSON
  
  
  [parameters]
  
  num_epochs = 3  # number of steps to run
  save_every_n_epochs = 3  # save checkpoint model file very N steps
  batch_size = 50
  num_refs = 100  # number of reference molecules randomly chosen for similarity
                  # set this to zero for large datasets (>200 molecules)!
  sample_batch_size = 100  # number of sampled molecules to compute sample loss
  # Uncomment one of the comment blocks below.  Each generator needs a model
  # file and possibly a SMILES file with seed structures.
  
  ## Reinvent
  #input_model_file = "priors/reinvent.prior"
  #smiles_file = "TL_reinvent_100.smi"  # read 1st column
  #output_model_file = "TL_reinvent.model"
  #validation_smiles_file = "TL_reinvent_100.smi"  
  
  ## Mol2Mol
  input_model_file = "priors/mol2mol_scaffold_generic.prior"
  smiles_file = "compounds.smi"  # read 1st column
  output_model_file = "TL_Mol2Mol.model"
  validation_smiles_file = "validation_compounds.smi"  # read 1st column
  
  # Define the type of similarity and its parameters
  pairs.type = "tanimoto"
  pairs.upper_threshold = 1.0
  pairs.lower_threshold = 0.7
  pairs.min_cardinality = 1
  pairs.max_cardinality = 199
  
  ### It may not make much sense to use TL for Lib/Linkinvent because
  ### both methods use constraints which may leave only a small portion of
  ### the molecule to be optimizable
  ## LibInvent
  #input_model_file = "priors/libinvent.prior"
  #smiles_file = "TL_libinvent_100.smi"  # read first 2 columns: scaffold, R-groups
  #validation_smiles_file = "TL_libinvent_100.smi"  # read first 2 columns: scaffold, R-groups
  #output_model_file = "TL_libinvent.model"
  #
  ## LinkInvent
  #input_model_file = "priors/linkinvent.prior"
  #smiles_file = "TL_linkinvent_100.smi"  # read first 2 columns: warheads, linker
  #output_model_file = "TL_linkinvent.model"
  #validation_smiles_file = "TL_linkinvent_100.smi"  # read first 2 columns: warheads, linker
  