> ## Documentation Index > Fetch the complete documentation index at: https://docs.revilico.bio/llms.txt > Use this file to discover all available pages before exploring further. # Ensemble Docking across 18 Targets > Ligand ranking from docking into molecular dynamics snapshots, scored against measured affinity for 451 ligands on 18 protein targets. A single crystal structure captures one conformation of a protein, but ligands bind to a moving target. Ensemble docking docks each ligand into several snapshots taken from a molecular dynamics simulation of the target and combines the scores. This page asks whether those scores rank a target's ligands in the order that experiment does, across 18 targets and 451 ligands.> [!NOTE] > > All results are ours. For each target, every ligand was docked into snapshots from a 50 or 100 ns simulation (five snapshots for a 50 ns run, ten for a 100 ns run) and its scores were averaged over the snapshots. Measured affinities are logKa values for the ligands' PDB complexes.## What the comparison showsAveraged over snapshots, the learned CNN affinity ranks ligands better than the plain docking energy: its mean rank correlation across targets is 0.29 against 0.19, it points the right way on 15 targets against 11, and it is ahead on 12 of the 18. Performance varies a lot by target. It is strong on PDE10A (ρ = 0.88), PTP1B (0.73), BRD4 (0.59, on 52 ligands) and PKA (0.52), moderate on about half the panel, and poor on AR, CHK1, EphA2, Factor Xa and GluA2, where it is near zero or negative. A mean of 0.29 is a modest result, and it should be read that way.> [!WARNING] > > The pooled summary in our internal panel table reports a mean ρ of 0.45, because it takes the best of several scoring metrics for each target after looking at the results. That inflates every target, so this page fixes one metric in advance for all of them (the CNN affinity averaged over snapshots) and shows what it actually delivers.## By targetSpearman ρ and Pearson r between each ligand's score and its measured logKa, for the 18 targets, grouped by simulation length. Metric \ \ \
NameLigands
\**Bold:** a value for that beats at least one other method in its row. *Italic, underlined:* the best value in the row. \**-**: no comparable result. Bold is a CNN affinity value that beats the docking energy. Faded rows have fewer than 10 ligands and are left out of the second summary row. Hover a target for its simulation length and affinity range. ## Target by target, ranked## Where learned scoring helpsThe difference in rank correlation for each target, CNN affinity minus docking energy.Spearman ρ of CNN affinity minus docking energy, per targetAhead on , behind on .◀ Docking energy ahead CNN affinity ahead ▶With 10 to 75 ligands per target, differences of a few tenths are within noise.## Score against measured affinityPick a target and a score. Each point is one ligand.## How much the choice of metric matters| Way of choosing the score | Mean Spearman ρ over 18 targets | | - | - | | CNN affinity averaged over snapshots (fixed in advance) | **0.29** | | Docking energy averaged over snapshots (fixed in advance) | 0.19 | | Best of several metrics, picked per target after seeing the results | 0.45 |The last row is not a valid estimate of performance: it uses the answer to choose the method. It is shown only to size the effect.## Reading these results> **Few ligands per target** > > Targets have between 6 and 75 ligands, and most have 15 to 30. A rank correlation over that few points moves a lot with one ligand, and JAK2 (6 ligands) is too small to read. Weight the pattern across targets over any single value.> **Simulation length and target are not separable** > > The 50 and 100 ns runs were done on different targets, so the table cannot say whether the longer simulation helps. It only reports each target at the length it was run.> **Ranking, not absolute affinity** > > The scores are compared with measured affinity by rank and correlation only. They are not calibrated to kcal/mol, and the page makes no claim about absolute accuracy.> **No competing method is shown** > > This page compares two scores from the same docking runs. It does not compare ensemble docking with docking into a single structure or with other programs.> **Possible overlap with training data** > > The CNN scoring model was trained on public protein–ligand data, and these ligands come from the same public archives. We have not checked for overlap, so treat the CNN result as agreement with public data, not a strict test on unseen ligands.## Sources- **Experimental affinities and ligands:** PDB complexes with measured logKa for each of the 18 targets. - **Scores:** our ensemble docking runs, with molecular dynamics snapshots at 50 and 100 ns, scored with the CNN affinity and docking energy of the docking engine. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.