[WIP] Bayer Optimization Data Set Conformer Reduction

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 larger chemical space for our May release force field.

• The Bayer set contains 5054 molecules that are large flexible pharmaceutically relevant molecules ranging from 12-30 heavy atoms.

Aim:

• Limit the number of conformers in a patented data set from Bayer for optimization data set.

  • Reduce data set to ~3 conformers/molecule.

Problem:

• Current fingerprint & clustering methods result in 525 molecules & 16,242 conformers.

• Hypothesized contributors to large number of conformers:

  • Large molecule size

  • Excessive rotatable bonds

Approach:

1. Try numerous size filtering strategies for molecule size that try to preserve chemical diversity and measure # of molecules and conformers.

2. If #1 is not successful, move onto rotatable bond filtering.

3. if #1 & #2 are not successful, move onto Fragmentation.

Experimental notes:

Clustering method: DBSCAN eps = 0.3, min_samples = 4

Fingerprint method: MACCS (supported by previous experiments from Hyesu Jang)

Conclusion: