Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 3 Next »

1. Generation of substituent list

  • Removed phenyls with ortho-substituents

  • Filter: cyclic substituents with (1) zero rotatable bond (2) # rings =1 or acyclic substituents with # rotatable bonds <2

  • Combined Roche, Coverage, Pfizer, Bayer: 361 substituents (Acyclic aliphatic: 183, 2. aliphatic rings: 100, 3. 6-membered aromatic rings:50, 4. 5-membered aromatic rings:28)

    • Roche set

      • Acyclic aliphatic: 61, 2. aliphatic rings: 21, 3. 6-membered aromatic rings:5, 4. 5-membered aromatic rings:11

    • Coverage set

      • Acyclic aliphatic: 76, 2. aliphatic rings: 2, 3. 6-membered aromatic rings:6, 4. 5-membered aromatic rings:2

    • Pfizer set

      • Acyclic aliphatic: 24, 2. aliphatic rings: 9, 3. 6-membered aromatic rings:6, 4. 5-membered aromatic rings:7

    • eMolecules set (okay not to include eMolecules set?)

      • aliphatic chain: 148, 2. aliphatic rings: 75, 3. 6-membered aromatic rings:90, 4. 5-membered aromatic rings:51

    • Bayer set

      • Acyclic aliphatic: 116, 2. aliphatic rings: 86, 3. 6-membered aromatic rings:42, 4. 5-membered aromatic rings:16

2. Generation of molecule set

  • Using 361 substituents, generated 59086 molecules (align by mol weights )

3. Curation

3.1. Remove similar molecules using MACCS keys fingerprints and Check coverage of torsion parameters

(1) list molecules matching to each torsion parameter

(2) using MACCS keys fingerprints, cluster each molecule list into ~20 clusters and pick center molecule from each cluster to generate subset of list with around 20 molecules for each torsion parameter

(3) check coverage of torsion parameter (missing torsions)

→ now running

  • (1) Want to include all substituents in the final torsiondrive dataset (2) while choosing one from each cluster, currently choose center one(one with the largest sum of similarity indices) → constantly choose certain substituents? *Random selection?

3.2. Internal H bond forming mols : Better SMIRKS needed. How to consider spatial arrangement of 1-n chain

(1) Test filtering w/ oversimplified SMIRKS

(2) More specific SMIKRS patterns

  • Filter  [n,N,o,O,F]([H])[!#1][!#1]~!@[!#1;r]([#7X2;r])

    • # molecules matched : 1430 (out of 59086)

    • Right hand side mols dont seem to form internal H bond

  • Filter [n,N,o,O,F]([H])[!#1]~!@[!#1]~!@[!#1;r]([#7X2;r])

    • # molecules matched : 1060 ( <2 % of total)

    • How to exclude right mol?


TODO (2021-04-01)

  • 1. Remove ortho substituents from substituent list, add ones with meta/para substituents
  • 2. Remove similar molecules using MACCS keys fingerprints
  • 3. Check coverage of torsion parameters → generate a draft of molecule set (~3000 entries)
  • 4. Addition of intra H bond filter : by using SMIKRS pattern matching
  • 5. Check the coverage of problematic substituents, which showed large discrepancies in Pavan’s 1.3.0 benchmarks
  • 6. Range of WBOs of each training data subset, a list of scans training a certain torsion parameter
  • 7. addition of double bond rotating torsion scans

damn installation

1. conda create --name constructure -c conda-forge -c openeye -c omnia pydantic openeye-toolkits cmiles ipykernel python=3.8

2. python setup.py develop (constructure)

3. python setup.py install (fragmenter)

4. conda install -c conda-forge pyyaml

* Additionally openforcefield has been installed

  • No labels