In this study we expands this concept to demonstrate the de novo design of whole ligands for transition metal catalysts at the example of the Suzuki reaction. While ML models trained on pre-existing data have proven effective in high-throughput screening, their use for unrestricted de novo design is challenging since their predictive performance on truly out of domain samples deteriorates. Another approach, relying on the correlation between geometric descriptors such as bond-lengths, cone angles or sterimol parameters and the thermodynamics of the catalytic sytem has been shown to be effective, yet it appears challening when moving beyond only one binding motif. Therefore, we evaluate the performance of a catalysts by calculation of DFT level thermodynamic descriptors, instead of using ML models or (semi-empirical) QM calculated geometric descriptors. This distinctive approach expands the domain of our de novo design, allowing for the exploration of chemical space beyond the confines of ML model training or QSAR correlations.