Conformer Generation using RDKit

Jean-Paul Ebejer
1st RDKit User General Meeting – London, 2012

Who am I ?
 3rd year D.Phil. student at the University of Oxfordo Marie Curie EU research project on Malaria between
industry (InhibOx Limited) and academia (Oxford Protein
Informatics Group) Background in computer science and bioinformatics
 RDKit mailing list subscriber since 8th October 2010
(some bug reports filed, one patch)
 Topics started in mailing list:
88
JP250Others
2What is a conformer ?
 A molecule can take on many different shapes
 Conformational space may be very large, andis a function of number of rotatable bonds

Chemical Component Identifier- QUS

3

Why is this important ? (i) In 3D ligand-based virtual screening
Query Molecule

Target1

Target2

Target34

Why is this important ? (ii) In structure-based virtual screening (internallyin docking programs)

Thrombin (1PPB), with binding inhibitor 0G6

Same receptor, with different conformer of same
5
inhibitor (0G6)

What is conformer generation used for?
 Virtual screening Shape-based similarity searches (e.g. volumeoverlap)
 Pharmacophore modelling Quantitative structure-activity relationship(3D QSAR)

6

In general, how does it work ? (i) Systematic approach: Change torsion angles of
all rotatable bonds by a small amount• But for large molecules this is infeasible (generates toomany states) Stochastic approach: Use of random algorithms
such as distance geometry, Monte Carlosimulation and genetic algorithms to permute
torsion angles

7In general, how does it work ? (ii)
 Use of statistically derived data from PDB
and CSD to determine most common
angles between different atom types
 Typically clean up structures with a force
field to avoid steric clashes and strainedstructureso Usually time consuming step8

How does RDKit generate conformers?
 Uses distance geometry
 Main ideas:o Molecule’s distance bounds matrix is calculated based on
the connection table and a set of rules
dbcabdmin. distancecamax. distanceo Generate a random distance matrix which satisfies thesebounds
o 3D Coordinates produced from these distances(embedding)o Different random distance matrices give rise to different
conformers
9For more details: Greg Landrum: RDKit Manual: Getting Started with the RDKit in Python.Section 1.3.5.

What did we do?
 Reviewed four freely available tools: Balloon,
Confab, Frog2 and RDKit together withcommercially available MOE
 We are interested in three measures:o Ability to generate experimentally determined
structureo Diversity of conformers generatedo Speed of conformer generation

10

We need a molecule test set
 Test set of 708 molecules,
made from a previous
validation study of anothercommercially available
product (OMEGA) and small
molecules from the Astex
Diverse Set
 These molecules are highresolution structures (< 2Å)
taken from the PDB and CSD
 Drug-like distributions formolecular weight, heavy
atoms and rotatable bonds¥¥ Oprea, T. I. Property distribution of drug-related chemical databases. Journal of Computer-Aided Molecular Design 2000, 14, 251–264,

10.1023/A:1008130001697.

11

Methods
 Generated SMILES with stereochemistry for 708
molecules to be used as inputo Confab does not accept SMILES input, so generated
initial structure with openbabel (–gen3d option)o We do not want initial 3D geometry of reference
structure to bias any of the conformer generation
tools

 Confab does not generate a specific number ofconformers

o Generates >10,000 conformers for 11 molecules;
median number of conformers is 92.5
o Picked 10, 50 and 100 random conformers where theconformational models exceeded these numbers
12Ability to re-generate the crystallographic
structure (i) Generated 10, 50 and 100 conformers for every tool For every molecule in dataset found minimum RMSD toexperimental structure

13Ability to re-generate the crystallographicstructure (ii) As expected, it is harder to reproduce the experimental
conformation of the more flexible molecules

14Diversity of conformers generated

15

The need for speed
 Frog2 fastest tool by an order of magnitude, followed by RDKit
when generating up to 100 conformers
 MOE faster when generating 300 conformers (not shown)16

In RDKit, energy minimisation is needed toimprove results!

No minimisation
UFFMMFF94

17

Conformer generation in 14 lines

18

Deciding for a value of n Function of rotatable bonds of molecules Ran an experiment generating 10, 50, 100,200, 300, 400, 500, 1000 conformers for ourtest set and partitioned results by rotatablebonds
 Trade-off between RMSD accuracy to crystal
structure and number of conformers togenerate
19Things to watch out for (i) RDKit allows to remove very similar
conformers from the ensemble(pruneRmsThresh)o But this only works before energy optimization ofthe molecule!

 Clustering needed again after ensemble isgeneratedo After UFF energy minimization some moleculeswill fall in the same locations in conformer space20

Clustering Algorithm
1. Using RDKit, generate n conformers in set Cgen
2. Energy minimization (using the UFF force field) is performedon every conformer. The conformer list is sorted byincreasing energy value and the lowest energy conformer(the first conformer in the list), clow, is recorded.
3. Remove clow from Cgen and add it to Ckeep
4. For each conformer, c, in Cgen, compute the RMSD between cand each conformer in Ckeep If any RMSD value is smaller than a fixed threshold, dmin,
discard c as we already have a representative of that pointin conformational space.
 Otherwise add c to Ckeep
21

Things to watch out for (ii) Planarity of secondary and tertiary amides
 More of a RDKit UFF optimization
implementation defect Possible solution is to add force field distanceconstraints

Out-of-plane (poor)

Planar secondary amide (good)22

Things to watch out for (iii) Very rarely, conformer generation fails, e.g.

 Sometimes trying a different random seed
fixes this Bug report filedo Fixed in revision 1952, useignoreSmoothingFailures=True when thishappens

23

Conclusions
 Used RDKit to generate over 200 million
conformers
o It works!

 Selecting a conformer generation tool
depends on other factors as wello e.g. ability to explore energetic landscape,integration in cheminformatics workflow etc.

 Open source tools offer a viable alternative tocommercial, closed source, proprietary
software24

Future work
 Multi-threaded conformer generationo Most time spent in energy minimizationo Each conformer independent of the other, could
be minimized in separate thread Move towards a knowledge based
approacho Use common fragments in the CSD/PDB

25

Future work
 Multi-threaded conformer generationo Most time spent in energy minimizationo Each conformer independent of the other, could
be minimized in separate thread Move towards a knowledge based
approacho Use common fragments in the PDB

26

Acknowledgements
 RDKito Greg Landrum, Nathan Brown

 InhibOx Limitedo Paul Finn, Garrett Morris Oxford Protein Informatics Group, University ofOxford
o Charlotte Deane Financial Support
o Marie Curie Fellowship27