During each simulation epoch, all candidate variants are evaluated using the scoring function.
Top variants (elitism) and diverse outliers (exploration) are retained for the next generation.
The reward signal fed to the RL agent is derived from StotalS_{\text{total}}, normalized across the population.
Early stopping criteria or thermodynamic thresholds can be set (e.g., discard variants with Sstab<10kcal/molS_{\text{stab}} < -10 \, \text{kcal/mol})
4.C.6. Scientific and Engineering Implications
This scoring framework allows modular plug-ins for other molecular features (e.g., redox activity, thermostability).
It supports multi-objective convergence, avoiding "overfitting" to any one metric.
Enhances interpretability by tracing which components drive fitness at each stage.
Enables more trustworthy AI-designed enzymes for industrial applications, especially in sustainability contexts (e.g., plastic degradation, waste valorization).
Section 5. Case Study: Evolving Synthetic PETase Variants
A. Dummy Dataset with Simulated Mutational Trajectories
