#11thICCS Oliver Koch on an exhaustive assessment of computer-based drug discovery methods by HTS data
We were challenged to test our methods against HTS results. Not a simple benchmarking exercise, but a real life task.
UNC 119A was the target. No inhibitor was known at the start. An unexplored protein target. Was able to do unbiased assessment - no prior knowledge.
Several VS workflows. Created models of the protein. Searches with Ligandscout or MOE followed by docking with GOLD. Also used pharmacophores instead of protein info as well as ligand-based methods.
The HTS campaign was against 143K cmpds. Hitrate of 1.6% (>75% inhibition). Quite high hit-rate. Probably because of huge binding site - everything can go in.
Using DrugScore PPI for hotspot analysis, and also used MD to assess protein flexibility. We combined protein features into a pharmacophore model in an automated fashion.
Automated pharmacophore generation didn't work well - model too big - hard to hit everything, but if features were optional too many things were hit. Rational generation (researcher bias) of models worked better, which focused on different binding site features.
We found different molecules when focusing on different binding site features.
Used Superstar from the @ccdc_cambridge . Knowledge-based pharmacophores based on non-bonded interactions from the CSD. Several probes used, e.g. N+. Good hit rates.
Using the X-ray model versus model (for the protein) had only minor changes in the feature placements, but gave different molecules (some overlap).
Now showing overall results comparing pure docking versus combination approaches. Ensemble docking with GOLD. Docking sometimes improves results over pharmacophore on its own, but not always. May depend on quality of the pharmacophore.
Once you have results, you can use initial results to improve the pharmacophore models in an iterative process. (Shows graph of how results improved over time)
Unfortunately the most active hit was not found by any of our methods. However, the hit rate for those results that were found was good.
Caveats: Only a single target tested. A huge pocket - difficult. Using proprietary screening database that is biased. No known true actives.
Caveats: Only a single target tested. A huge pocket - difficult. Using proprietary screening database that is biased. No known true actives.
Take-home. Huge influence of the researcher actually sitting in front of the computer. Docking or pharmacophore works well, but hard to say which is best.
