The project/DFG Research Grants Programme titled “Characterizing and understanding the reaction condition space for DNA-encoded chemistry with tailored lab automats and advanced statistics” is developing a data-driven approach to optimize and further develop methods in DNA-coded chemistry. As part of this project, Prof. Andreas Brunschweiger in Würzburg has been working closely with Prof. Katja Ickstadt and Prof. Norbert Kockmann in Dortmund, and they are also utilizing the capabilities of DoDaS. Automated laboratory equipment and integrated sensor technology generate large amounts of experimental data to systematically investigate the space of possible reaction conditions. Using statistical methods and a design-of-experiments approach, experiments are planned, data are evaluated, and correlations between conditions and reaction outcomes are analyzed. Building on this, regression models from the field of machine learning are employed to predict suitable reaction conditions that enable high yields and minimal DNA damage. The resulting datasets and models will be bundled into an ML toolbox and, in the long term, will form a data-driven infrastructure for the more efficient development of reactions in DNA-encoded chemistry.