#11thICCS Willem Jespers on behalf of EB Lenselink on Lessons learned in benchmarking VS for polypharmacology
VS has been quite successful - hit rates often exceed 10%. DB sizes getting quite big. ZINC 800M, Enamine REAL > 300M.
On average drugs hit six protein targets in the body, but designed to hit just one. Oncology they try to hit multiple targets within one disease type to avoid resistance. 3.4% success rate of drug in oncology.
Rational polypharmacology: design/find mols for targets with known synergy. There is an increasing availability of data (genomics, bioacts, 3d structs) that can help to find these synergies.
"DREAM challenge" - dialog for reverse eng assessments and methods. Not for profit, open source, translational med. Fun to compete in an open challenge but also useful. Spent 3/4 on challenge using our in-house workflows.
DREAM challenge was VS for polypharm. A whole bunch of FDA approached drugs for anticancer kinase inhibs. The organisers gave 3 targets and 4 antitargets. Find inhibitors.
Started with ZINC15 db "in stock". 5 compounds to be chosen for the challenge, and these would be tested. Shows workflow of filters involving docking with Glide, etc, etc.
Janssen biosignatures. Github project_BBDD. JK Wegner et al submitted. Logistic models trained on different features. Data fusion of info from different sources, e.g. exptal endpoints.
Proteochemometrics. Use available data (e.g. ChEMBL, ExCAPE-DB, Eidogen). Combine cmpd descriptors and protein descriptors. RF model. Filtered data on various properties, e.g. MW, activity. 643K datapoints on 371 kinases.
PCM random search (sklearn) to improve RF model by reducing the number of descriptors (?) to avoid overfitting. Shows default versus random search optimised RF model. Clear but not major improvement across the board.
Structure-base docking. For all actives, generating decoys with DUD-E. Docked on available xtal structures. Glide (sp) -docking, XP-redocked the top 10% but did not improve the enrichment. Used combined BEDROC and ... ROC.
Interaction fps. Deng, CREDO, Elements, SYBYL, SPLIF, Glide, 2D, SPLIF+. We found SPLIF to work very well for separating actives from inactives.
GM Sastry JCIM 2013, "Z2" ensemble (?). This worked best when docking.
Metadynamics. Take ligand, apply RMSD bias to force it out, 5 replicas of 10ns with MD. Persistence score - how many interactions are reproduced. Also a pose score.
Compared docking versus docking+metadynamics. (ed:missed results - I think it improved them)
Shows video indicating that one of the nice docking results, starts moves out of the active site when apply MD. So, not so good actually.
Metadynamics can improve affinity ranking. Further worked needed though.
How to combine all these models. Confidence weighing of models. Which do they trust the most. Day of visual inspection by the team. Lots of shouting. Best part of the project. Slection of top 5. Results will be announced in Oct. About 30 groups involved.
Lessons learned? Participation in challenge very useful, time constraint good, data is everything, random search can improve hyperparameters, tailor VS protocol per target, metadynamics can (partly) rescuedocking ranking. We are developing and using these methods in Leiden today.
"Bart would like to thank me. I'd like to thank him."
