This repository contains some tutorials and general resources for the usage of BayBE, the Bayesian Optimization Framework developed by Merck KGaA, Darmstadt, Germany.
NOTE: This repository is updated regularly, and the resources and tutorials here can and will change significantly over time. It might not always reflect the newest versions of BayBE. To verify which version of BayBE is being used, refer to the individual files.
This repository contains marimo notebooks demonstrating the capabilities of BayBE. Marimo is an open-source reactive notebook for Python.
To view and use any of the notebooks, execute marimo edit path/to/notebook.py. This opens marimo in your
browser and enables you to run and execute the notebook.
- Create a python environment using a version of python compatible with BayBE, e.g. via
mamba create --yes --name baybe-resources python=3.13
mamba activate baybe-resources
if you use mamba.
2. Install the additional dependencies from requirements.txt via pip install -r requirements.txt. Note that this installs all optional dependencies for BayBE as well as for all tutorials to avoid the need of having to re-install example-specific additional dependencies.
- Martin Fitzner (Merck KGaA, Darmstadt, Germany), Contact, Github
- Adrian Šošić (Merck Life Science KGaA, Darmstadt, Germany), Contact, Github
- Alexander Hopp (Merck KGaA, Darmstadt, Germany) Contact, Github
Copyright 2022-2025 Merck KGaA, Darmstadt, Germany and/or its affiliates. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.