4.C.2. Mathematical Formulation
We define the scoring function S_total as:
Stotal=Sstab+Saff+SdynCmutS_{\text{total}} = \alpha \cdot S_{\text{stab}} + \beta \cdot S_{\text{aff}} + \gamma \cdot S_{\text{dyn}} - \delta \cdot C_{\text{mut}}
Where:
SstabS_{\text{stab}}: Structural or thermodynamic stability score (e.g., G of folding)
SaffS_{\text{aff}}: Substrate binding affinity (e.g., docking energy or KD)
SdynS_{\text{dyn}}: Dynamical coherence score, capturing systemic integration (e.g., folding smoothness, path entropy, or network resilience)
CmutC_{\text{mut}}: Mutation cost penalty (e.g., accumulated destabilizing mutations)
, , , : Tunable weights, adapted during training or simulation
These weights may be adjusted dynamically through reinforcement learning or evolutionary pressure, allowing the system to self-prioritize between stability and function depending on selective context (e.g., enzyme used at high temperature vs. in complex mixtures).
4.C.3. Components Explained
