Executive abstract
What this paper proposes.
GenovaX is Planckchron's AI-assisted, computational (dry-lab) drug-discovery workflow. In this demonstration we ran de novo nanobody (VHH) binder design against hen egg-white lysozyme β a standard, well-characterized structural-biology benchmark target (NOT a Planckchron therapeutic target) β end-to-end on GPU cloud in ~4 minutes, producing a ranked, reproducible shortlist. Every number is a model prediction; none of these molecules has been expressed or assayed.
Methodology
De novo VHH binder generation with an AI-assisted structure prediction and scoring stack (Boltz), followed by developability filtering using Therapeutic-Antibody-Profiler-style sequence/structure heuristics. Ten designs ranked on binding confidence; interactive 3D structures shown for the top two.
Conclusion and boundary
Computational predictions only. Lysozyme is a benchmark target, not a therapeutic program. Advance R7gl as sole lead; hold 33Tc pending liability-removal engineering; keep So7D as a clean backup. Next step: wet-lab validation of the top candidates.



