This package is a fork of the code of BenevolantAI https://github.com/BenevolentAI/guacamol_baselines. It enables running generations with several models, for the tasks from the guacamol benchmark , and for another task named "pi3kmtor" described in the paper.

Additionaly, the package enables to run generations with synthetic score as a new constraint. The synthetic accessibility score can be one of the following :

• SA score
• RA score
• SC score
• RScore (Spaya Api)
• RSPred (Predictor of SpayaApi trained on Chembl24).

• Run poetry install to install all the required dependencies.

• Download the guacamol SMILES files with the script found in guacamol_baselines/fetch_guacamol_dataset.sh

• Extract the models.zip file found in the folder synthetic_scorers/RAscore

• Generated smiles are stored in a MongoDB database, so you need to setup a database and set the following environment variables to point to it:

  • MONGO_URL: URI and necessary credentials to the database server
  • DB_STORAGE: Name of the database to be used to store the sampled SMILES (the code will create collections to store the data)

• Finally, if you want to use the RScore score, calculated by the Spaya API, you need to have two more environment variables:

  • SPAYA_API_URL
  • SPAYA_API_TOKEN

These should contain your credentials to use the Spaya API.

The goal directed generations have 2 essentials arguments :

• suite: this value will determine the generations that are going to be launched. A value of a suite correponds to a list of tasks:
  • 'guacamol_paper' launches the generation of guacamol benchamrk tasks with successively no synthetic scores, SA score, RScore and RSPred.
  • 'pi3kmtor_paper' launches the pi3kmtor generation with successively each of the synthetic scorers.
  • 'pi3kmtor' launches only one generation of pi3kmtor, using the synthetic score set in the synth_score variable (see below)
• synth_score : when set, indicates which synthetic scorer to add to the generation. Should be one of RScore, SAscore, SCscore, RAscore, or RSPred. This parameter is only used by the pi3kmtor suite.

Run 10 steps of generation around pi3kmtor dataset, optimizing 4 constraints and using the SA score constraint:

poetry run python -m guacamol_baselines.smiles_lstm_hc.goal_directed_generation --suite pi3kmtor --n_epochs 10 --synth_score SAscore

• The generated molecules are saved in the MongoDB database defined above, in collections named with the synth_score used by each task (eg. benchmark_name+"_"+<synth_score>)

• For the pi3kmtor generations, the differents scores of the molecules are already in the collection.

• You can use the notebook in exploit_results/exploit_results_pi3kmtor.ipynb to analyse the results.