This repository contains code to reproduce the experiments reported in "Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation".

The constrained Bayesian optimization experiments reported in this work use the atlas Python library. You can visit the atlas GitHub repo, documentation, and tutorial notebook for more information.

You can install atlas from source by executing the following commands in your terminal

git clone git@github.com:aspuru-guzik-group/atlas.git
cd atlas
pip install -e .

The directories in this repo each contain code to reproduce specific experiments from our paper

• benchmarks_unknown/: this directory contains the benchmark experiments on analytical surfaces, such as branin, dejong, etc. Categorical/discrete experiments are in subdirectories beginnig with cat-.

• application_hoip/: code to reproduce the inverse design of stable hybrid organic-inorganic halide perovskite materials real-world application.

• application_drug_design/: code to reproduce the (poly)pharmacological design of BCR-Abl fusion protein inhibitors real world application. The single obejctive experiments are in application_drug_design/single_obj, and the multi-objective optimization experiments are located in application_drug_design/multi_obj. application_drug_design/reference-and-data contains the original dataset from Desai et al., along with datasets of IC50 values taken from BindingDB for the KIT and PDGF tyrosine kinase inhibitors. This directory also contains the code to emulate the original BCR-Abl inhibitors dataset (using NGBoost classifier and regressor), as well as the scripts used to fit a MPNN to the KIT and PDGF datasets (using chemprop).

• analysis/atlas_acqf_optimizers/: comparison of constrained acquisition function optimization strategies SLSQP and DEAP/PyMOO (genetic) on analytical surfaces.

• analysis/atlas_ei_ucb_compare/: comparison of EI and UCB acquisiton functions on constrained analytical benchmark functions.

• analysis/atlas_filter_compare/: comparison of mininum-filtered and original FIA and FCA feasibility-aware acquisition functions on analytical surfaces.

Distributed under the MIT license. See LICENSE for more information.

Academic collaborations and extensions/improvements to the code by the community are encouraged. Please reach out to Riley by email if you have questions.

If you utilize the benchmarks experiments in this repository to advance your research project, please cite the following publication.

@misc{hickman_unknown_2023,
     author = {Hickman, Riley and Aldeghi, Matteo and Aspuru-Guzik, Al{\'a}n},
	doi = {},
	language = {en},
	month = ,
	publisher = {ChemRxiv},
	title = {Bayesian optimization with unknown feasibility constraints for experiment planning and chemical design},
	urldate = {2023-},
	year = {2023},
}

atlas is an academic research software. If you use atlas in a scientific publication, please cite the following article.

@misc{hickman_atlas_2023,
    author = {Hickman, Riley and Sim, Malcolm and Pablo-Garc{\'\i}a, Sergio and Woolhouse, Ivan and Hao, Han and Bao, Zeqing and Bannigan, Pauric and Allen, Christine and Aldeghi, Matteo and Aspuru-Guzik, Al{\'a}n},
	doi = {10.26434/chemrxiv-2023-8nrxx},
	language = {en},
	month = sep,
	publisher = {ChemRxiv},
	shorttitle = {Atlas},
	title = {Atlas: {A} {Brain} for {Self}-driving {Laboratories}},
	urldate = {2023-09-05},
	year = {2023},
}