ENGINE::CORE_COMPUTE
A three-stage pipeline that translates single-cell transcriptomic disease coordinates into synthesizable small-molecule candidates — entirely in silico.
STAGE::01
Screens the NIH LINCS Phase I L1000 dataset (GSE92742) to evaluate recombinant ligands, cytokines, and morphogens. The engine calculates multi-dimensional cell-state transitions using Cosine Similarity, Earth Mover's Distance (EMD), and Mean Squared Error (MSE) to identify vectors that collapse the fibrotic manifold into the regenerative fetal blueprint.
STAGE::02
Reinforcement learning simulates parallelised multi-gene knockouts targeting scale-free master regulatory hubs — collapsing fibrotic trajectories via TGF-β / SMAD pathway suppression.
> hub collapse: SUCCESS
> trajectory shift: −0.83σ
> next iteration... ▋
STAGE::03
Translates the target genetic coordinate vector — the output of the Virtual CRISPR agent — directly into Morgan Fingerprint chemical space. Maps biological reprogramming intent to synthesizable polypharmacology candidates without manual chemical intuition.
Encoding Pipeline