This is part of Accelerate 2024, in partnership with the Acceleration Consortium and Merck KGaA, Darmstadt, Germany.
Code is based on the open-source experimental planner BayBE.
The workshop contains 1 quick start and 4 notebooks containing code examples dealing with the following problems.
Illustrating BayBE's basic components, the searchspace, objective and the
recommender, as well as our utility to generate simulation results for backtesting
different settings.
Categorical encodings are an easy way to introduce domain knowledge, such as chemical intuition into the model. BayBE comes with builtin cheminformatics descriptors for small molecules, particularly useful for all use cases where the choice of substance is a parameter. The idea of encoding can be generalized to any domain of expertise, not just chemistry, and a custom encoding is also demonstrated.
Mixtures are an extremely common use case for Bayesian optimization. This notebook introduces the difference between the traditional, purely number-based mixture representation and slots. We illustrate the challenges arising from the slot-based representation and how to address them with constraints. When created correctly, the slot-based representation can make use of smart encodings and also model the order of addition naturally.
Realizing when a campaign should be stopped can be trivial if the absolute targets were met. However, it is much less clear how to realize that continuing a campaign is unpromising if no convergence to the optimum has happened. Here, we use a simple criterion based on the pointwise probability of improvement to stop a campaign. We will utilize BayBE's custom hook mechanic to inject the check.
Transfer learning in the context of Bayesian optimization denotes situations where data from similar-but-not-quite-the-identical campaigns is available when starting a new optimization. This is a situation prevalent in industry, as data from similar campaigns, such as similar reactions, cell-lines, locations etc., is in principle available but usually untapped.