Contributors: David Mobley, Lee-Ping Wang, Hyesu Jang, Jeff Wagner, Chris Bayly, Josh Horton, Chaya Stern, Jessica Maat
Background: The Open Force Field Initiative is working on developing optimization training data sets via a fingerprint and clustering method. The aim of this project is to pull chemically diverse molecules from a range of data sets to survey a large chemical space for our May release force field.
Aim: The aim of this sub-experiment is to limit the number of conformers in a patented data set from Bayer.
Problem: The Bayer set contains large flexible drug molecules that range from 12-30 heavy atoms. Current fingerprint & clustering methods result in 525 molecules & 16,242 conformers. We need to reduce data set to ~3 conformers/molecule.
Approach:
After I get a reasonably sized set of optimized molecules (through a size filtering method or rotatable bond filtration), I will submit these.
I will use Fragmentation as a second data set iteration or if I am unable to achieve 3 confs/mol in step 1. I will report how fragmentation affects conformer generation. I hypothesize this will reduce # of conformers because smaller resulting molecules.
Experimental notes:
Method | # of molecules | # of conformers | notes |
|---|---|---|---|
Conclusion: