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# Logging configuration | ||
experiment_name: acegen | ||
agent_name: reinvereinforcnt | ||
log_dir: results | ||
logger_backend: null # csv, wandb, tensorboard, or null | ||
seed: 101 | ||
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# Environment configuration | ||
num_envs: 128 # Number of smiles to generate in parallel | ||
total_smiles: 10_000 | ||
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# Scoring function | ||
molscore: MolOpt | ||
molscore_include: ["Albuterol_similarity"] | ||
custom_task: null # Requires molscore to be set to null | ||
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# Fix the beginning of the generated molecules | ||
prompt: null # e.g. c1ccccc | ||
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# Architecture | ||
model: gru # gru, lstm, or gpt2 | ||
# The default prior varies for each model. Refer to the README file in the root directory for more information. | ||
# The default vocabulary varies for each prior. Refer to the README file in the root directory for more information. | ||
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# Optimizer configuration | ||
lr: 0.0001 | ||
eps: 1.0e-08 | ||
weight_decay: 0.0 | ||
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# Reinforce configuration | ||
sigma: 120 | ||
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# Data replay configuration | ||
replay_buffer_size: 100 | ||
experience_replay: True | ||
replay_batch_size: 10 |
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# Logging configuration | ||
experiment_name: acegen | ||
agent_name: reinvent | ||
log_dir: results | ||
logger_backend: null # csv, wandb, tensorboard, or null | ||
seed: 101 | ||
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# Environment configuration | ||
num_envs: 128 # Number of smiles to generate in parallel | ||
total_smiles: 10_000 | ||
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# Scoring function | ||
molscore: MolOpt | ||
molscore_include: ["Celecoxxib_rediscovery"] | ||
custom_task: null # Requires molscore to be set to null | ||
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# Promptsmiles configuration | ||
promptsmiles: c1(C)ccc(*)cc1.NS(=O)(=O)(*) | ||
promptsmiles_optimize: True | ||
promptsmiles_shuffle: True | ||
promptsmiles_multi: False | ||
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# Architecture | ||
model: gru # gru, lstm, or gpt2 | ||
# The default prior varies for each model. Refer to the README file in the root directory for more information. | ||
# The default vocabulary varies for each prior. Refer to the README file in the root directory for more information. | ||
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# Optimizer configuration | ||
lr: 0.0001 | ||
eps: 1.0e-08 | ||
weight_decay: 0.0 | ||
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# Reinforce configuration | ||
sigma: 120 | ||
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||
# Data replay configuration | ||
replay_buffer_size: 100 | ||
experience_replay: True | ||
replay_batch_size: 10 |
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# Logging configuration | ||
experiment_name: acegen | ||
agent_name: reinvent | ||
log_dir: results | ||
logger_backend: null # csv, wandb, tensorboard, or null | ||
seed: 101 | ||
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||
# Environment configuration | ||
num_envs: 128 # Number of smiles to generate in parallel | ||
total_smiles: 10_000 | ||
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||
# Scoring function | ||
molscore: LibINVENT_Exp1 | ||
molscore_include: ["DRD2_SelRF_SubFilt_DF"] | ||
custom_task: null # Requires molscore to be set to null | ||
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# Promptsmiles configuration | ||
promptsmiles: N1(*)CCN(CC1)CCCCN(*) | ||
promptsmiles_optimize: True | ||
promptsmiles_shuffle: True | ||
promptsmiles_multi: False | ||
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||
# Architecture | ||
model: gru # gru, lstm, or gpt2 | ||
# The default prior varies for each model. Refer to the README file in the root directory for more information. | ||
# The default vocabulary varies for each prior. Refer to the README file in the root directory for more information. | ||
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# Optimizer configuration | ||
lr: 0.0001 | ||
eps: 1.0e-08 | ||
weight_decay: 0.0 | ||
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