AI Protein Fitness Landscape Explorer (2D)

AI-driven protein design rarely optimizes one number — a sequence that binds tighter is often less stable or more prone to aggregate. This simulator renders that trade-off as a top-down 2D fitness map: every candidate sequence is a point scored by simplified stability (ΔΔG), binding-affinity (pK_D) and aggregation-propensity models, combined into one fitness brightness. Each "Run cycle" click plays out one round of a design-build-test loop — the AI proposes a new pool of candidates around the current best, scores them, filters out the ones that fail a developability cutoff, and promotes the new peak — while a trade-off scatter and a best-fitness history chart track the search alongside the live readouts.