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mol_opt: A Benchmark for Practical Molecular Optimization


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This repository hosts an open-source benchmark for Practical Molecular Optimization (PMO), to facilitate the transparent and reproducible evaluation of algorithmic advances in molecular optimization. This repository supports 25 molecular design algorithms on 23 tasks with a particular focus on sample efficiency (oracle calls). The preprint version of the paper is available at https://arxiv.org/pdf/2206.12411.pdf

News

We release a lightweight mol_opt in https://github.com/wenhao-gao/mol-opt It can be installed by pip and be used within three lines of code.

Installation

conda create -n molopt python=3.7
conda activate molopt 
pip install torch 
pip install PyTDC 
pip install PyYAML
conda install -c rdkit rdkit 

We recommend to use PyTorch 1.10.2 and PyTDC 0.3.6.

Then we can activate conda via following command.

conda activate molopt 

29 Methods

Based the ML methodologies, all the methods are categorized into:

  • virtual screening
    • screening randomly search ZINC database.
    • molpal uses molecular property predictor to prioritize the high-scored molecules.
  • GA (genetic algorithm)
    • graph_ga based on molecular graph.
    • smiles_ga based on SMILES
    • selfies_ga based on SELFIES
    • stoned based on SELFIES
    • synnet based on synthesis
  • VAE (variational auto-encoder)
    • smiles_vae based on SMILES
    • selfies_vae based on SELFIES
    • jt_vae based on junction tree (fragment as building block)
    • dog_ae based on synthesis
  • BO (Bayesian optimization)
    • gpbo
  • RL (reinforcement learning)
    • reinvent
    • reinvent_selfies
    • reinvent_transformer
    • graphinvent
    • moldqn
    • smiles_aug_mem
    • smiles_bar
  • HC (hill climbing)
    • smiles_lstm_hc is SMILES-level HC.
    • smiles_ahc is SMILES-level augmented HC.
    • selfies_lstm_hc is SELFIES-level HC
    • mimosa is graph-level HC
    • dog_gen is synthesis based HC
  • gradient (gradient ascent)
    • dst is based molecular graph.
    • pasithea is based on SELFIES.
  • SBM (score-based modeling)
    • gflownet
    • gflownet_al
    • mars

time is the average rough clock time for a single run in our benchmark and do not involve the time for pretraining and data preprocess. We have processed the data, pretrained the model. Both are available in the repository.

assembly additional package time requires_gpu
screening - - 2 min no
molpal - ray, tensorflow, ConfigArgParse, pytorch-lightning 1 hour no
graph_ga fragment joblib 3 min no
smiles_ga SMILES joblib, nltk 2 min no
stoned SELFIES - 3 min no
selfies_ga SELFIES selfies 20 min no
graph_mcts atom - 2 min no
smiles_lstm_hc SMILES guacamol 4 min no
smiles_ahc SMILES 4 min no
selfies_lstm_hc SELFIES guacamol, selfies 4 min yes
smiles_vae SMILES botorch 20 min yes
selfies_vae SELFIES botorch, selfies 20 min yes
jt_vae fragment botorch 20 min yes
gpbo fragment botorch, networkx 15 min no
reinvent SMILES pexpect, bokeh 2 min yes
reinvent_transformer SMILES pexpect, bokeh 2 min yes
reinvent_selfies SELFIES selfies, pexpect, bokeh 3 min yes
smiles_aug_mem SMILES reinvent-models==0.0.15rc1 2 min yes
smiles_bar SMILES reinvent-models==0.0.15rc1 2 min yes
reinvent_selfies SELFIES selfies 3 min yes
moldqn atom networks, requests 60 min yes
mimosa fragment - 10 min yes
mars fragment chemprop, networkx, dgl 20 min yes
dog_gen synthesis extra conda 120 min yes
dog_ae synthesis extra conda 50 min yes
synnet synthesis dgl, pytorch_lightning, networkx, matplotlib 2-5 hours yes
pasithea SELFIES selfies, matplotlib 50 min yes
dst fragment - 120 min no
gflownet fragment torch_{geometric,sparse,cluster}, pdb 30 min yes
gflownet_al fragment torch_{geometric,sparse,cluster}, pdb 30 min yes

Run with one-line code

There are three types of runs defined in our code base:

  • simple: A single run for testing purposes for each oracle, is the defualt.
  • production: Multiple independent runs with various random seeds for each oracle.
  • tune: A hyper-parameter tuning over the search space defined in main/MODEL_NAME/hparam_tune.yaml for each oracle.
## specify multiple random seeds 
python run.py MODEL_NAME --seed 0 1 2 
## run 5 runs with different random seeds with specific oracle 
python run.py MODEL_NAME --task production --n_runs 5 --oracles qed 
## run a hyper-parameter tuning starting from smiles in a smi_file, 30 runs in total
python run.py MODEL_NAME --task tune --n_runs 30 --smi_file XX --other_args XX 

MODEL_NAME are listed in the table above.

Multi-Objective Optimization

Multi-objective optimization is implemented in multiobjective branch. We use "+" to connect multiple properties, please see the command line below.

python run.py MODEL_NAME --oracles qed+jnk3  

Hyperparameters

We separate hyperparameters for task-level control, defined from argparse, and algorithm-level control, defined from hparam_default.yaml. There is no clear boundary for them, but we recommend one keep all hyperparameters in the self._optimize function as task-level.

  • running hyperparameter: parser argument.
  • default model hyperparameter: hparam_default.yaml
  • tuning model hyperparameter: hparam_tune.yaml

For algorithm-level hyperparameters, we adopt the stratforward yaml file format. One should define a default set of hyper-parameters in main/MODEL_NAME/hparam_default.yaml:

population_size: 50
offspring_size: 100
mutation_rate: 0.02
patience: 5
max_generations: 1000

And the search space for hyper-parameter tuning in main/MODEL_NAME/hparam_tune.yaml:

name: graph_ga
method: random
metric:
  goal: maximize
  name: avg_top100
parameters:
  population_size:
    values: [20, 40, 50, 60, 80, 100, 150, 200]
  offspring_size:
    values: [50, 100, 200, 300]
  mutation_rate:
    distribution: uniform
    min: 0
    max: 0.1
  patience:
    value: 5
  max_generations:
    value: 1000

Contribute

Our repository is an open-source initiative. To update a better set of parameters or incldue your model in out benchmark, check our Contribution Guidelines!

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