Drug Discovery Simulator | mysimulator.uk

Screening Pool

Binding-affinity cutoff -9.0 kcal/mol

Pool size 120 candidates

Hit rate

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Optimization rounds

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Best ΔG found

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Best K_d

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How it works

Each candidate molecule is assigned a random binding free energy ΔG against the target's pocket, drawn from a distribution centered a little above the cutoff. A molecule "binds" when its ΔG is more negative than the cutoff you set — the more negative the energy, the tighter and closer to the pocket it orbits. Everything else drifts in loose, wide orbits as a rejected candidate. This is exactly the trade-off a real screening campaign faces: tighten the cutoff and the hit rate drops, but the survivors are stronger binders.

ΔG = -RT·ln(K)
K_d = exp(ΔG / RT),  RT ≈ 0.593 kcal/mol at 298 K

Binding-affinity cutoff — the ΔG threshold a candidate must beat to count as a hit; real screens tighten this as they move from primary screening toward a clinical lead.
Pool size — how many candidate molecules are screened at once; regenerates the pool with a fresh random draw.
Run optimization round — simulates one cycle of medicinal-chemistry lead optimization: it shifts the whole pool's affinity distribution toward stronger (more negative) binding, the way iterative structural tweaks improve a lead compound.
Reset pool — draws a brand-new, unoptimized pool at round zero.

Real-world relevance: in an actual campaign this loop — screen, tighten selection, optimize, rescreen — is repeated over months against thousands to millions of real compounds before a handful advance to preclinical testing.

Bound (hit) Rejected Target pocket

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