From the set of Pareto-optimal solutions, we identify promising candidates based on two main criteria: i) solutions with a GS close to the best found (within a specified threshold), and ii) solutions showing exceptional performance in one or more individual scores (gradient, charge, or energy), even if their GS is not optimal. This approach allows us to consider solutions that, while not necessarily having the best GS, could offer significant improvements in describing specific aspects of the enzymatic reactions. The final parameter set is selected primarily based on its optimization scores, with the GS serving as the main criterion while also considering the balance among individual scores. We acknowledge that while overall balance is important, there may be cases where significant improvement in one aspect (e.g., gradients accuracy) could be more valuable for certain applications, even at the cost of slight decreases in other scores.