The Bio-Synthetic Unification: Topological Discovery of the Pharmaceutical 'Sweet Spot' via Spectral Entropy Minimization

We present a deterministic framework for drug discovery that unifies biological efficacy and synthetic accessibility into a single topological metric: the Pirolo Unified Score (PUS). By modeling molecules as spectral graphs and minimizing their "Synthetic Entropy" (derived from the Laplacian Fiedler vector), the algorithm autonomously rediscovered Imatinib (Gleevec) as an optimal "blockbuster" candidate in a blind retro-validation study. This work demonstrates that pharmaceutical success is a function of thermodynamic modularity encoded in the molecular graph, effectively filtering out "academic traps" and unmanufacturable targets.