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Improving enrichment rates A practical solution to an impractical problem Noel O’Boyle Cambridge Crystallographic Data Centre [email_address]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Docking – an impractical problem? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Docking – an impractical problem? ,[object Object],[object Object],[object Object],[object Object],Leach, AR; Shoichet, BK; Peishoff, CE.  J. Med. Chem.   2006 ,  49 , 5851
A practical solution Pham, T. A.; Jain, A. N.  J. Med. Chem.   2006,  49 , 5856. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ChemScore scoring function in GOLD ,[object Object],[object Object]
Burial depth scaling (BDS) ,[object Object],[object Object],[object Object],[object Object]
Dataset ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Optimization procedure ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Optimization results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Testing of final equation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Comparison of HB and lipophilic interactions s hbond s lipo
Performance of BDS
1w2g –  thymidylate kinase
1p62 –  deoxycytidine kinase
Performance of BDS
1xm6 –  phosphodiesterase 4B
1hnn –  phenylethanolamine  N -methyltransferase
Conclusions ,[object Object],[object Object]
Acknowledgements ,[object Object],[object Object],[object Object],[object Object],[object Object],BDS will be available in the forthcoming GOLD 4.0 release Email: oboyle@ccdc.cam.ac.uk
Blank
1.09 - - 0.99 0 -40 18.6 f HB  and g L 18.8 None Test Set A 0.52 - - 1.80 105 13 12.5 f HB  and g L   0.70 - - 1.80 - - 14.0 g HB  and g L   - - - 4.98 120 31 13.0 f HB 0.97 126 44 - - - 13.9 f L 2.01 146 64 3.24 162 19 11.5 f HB  and f L - - - - - - 18.6 None S ρ 2 ρ 1 S ρ 2 ρ 1 Training Set Lipophilic function term(s) Hydrogen bond function term(s) Optimized mean rank of actives Receptor density functions used
Molecular weight effect 11.9 20.2 Test Set C 12.6 18.8 Test Set B 12.5 18.6 Training set After scaling Before scaling Mean rank of actives Dataset
 
 
 
Docking – an impractical problem? “ Why does docking remain so primitive that it is unable to even rank-order a hit list? Accurate prediction of binding affinities for a diverse set of molecules turns out to be genuinely difficult.  At its simplest level, this is a problem of subtraction of large numbers, inaccurately calculated, to arrive at a small number. The large numbers are the interaction energy between the ligand and protein on one hand and the cost of bringing the two molecules out of the solvent and into an intimate complex on the other hand. The result of this subtraction is the free energy of binding, the small number we most want to know.” Leach, AR; Shoichet, BK; Peishoff, CE.  J. Med. Chem.   2006 ,  49 , 5851
Astex Diverse Set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Improving enrichment rates

  • 1. Improving enrichment rates A practical solution to an impractical problem Noel O’Boyle Cambridge Crystallographic Data Centre [email_address]
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  • 12. Comparison of HB and lipophilic interactions s hbond s lipo
  • 14. 1w2g – thymidylate kinase
  • 15. 1p62 – deoxycytidine kinase
  • 17. 1xm6 – phosphodiesterase 4B
  • 18. 1hnn – phenylethanolamine N -methyltransferase
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  • 21. Blank
  • 22. 1.09 - - 0.99 0 -40 18.6 f HB and g L 18.8 None Test Set A 0.52 - - 1.80 105 13 12.5 f HB and g L 0.70 - - 1.80 - - 14.0 g HB and g L - - - 4.98 120 31 13.0 f HB 0.97 126 44 - - - 13.9 f L 2.01 146 64 3.24 162 19 11.5 f HB and f L - - - - - - 18.6 None S ρ 2 ρ 1 S ρ 2 ρ 1 Training Set Lipophilic function term(s) Hydrogen bond function term(s) Optimized mean rank of actives Receptor density functions used
  • 23. Molecular weight effect 11.9 20.2 Test Set C 12.6 18.8 Test Set B 12.5 18.6 Training set After scaling Before scaling Mean rank of actives Dataset
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  • 27. Docking – an impractical problem? “ Why does docking remain so primitive that it is unable to even rank-order a hit list? Accurate prediction of binding affinities for a diverse set of molecules turns out to be genuinely difficult. At its simplest level, this is a problem of subtraction of large numbers, inaccurately calculated, to arrive at a small number. The large numbers are the interaction energy between the ligand and protein on one hand and the cost of bringing the two molecules out of the solvent and into an intimate complex on the other hand. The result of this subtraction is the free energy of binding, the small number we most want to know.” Leach, AR; Shoichet, BK; Peishoff, CE. J. Med. Chem. 2006 , 49 , 5851
  • 28.

Editor's Notes

  1. I’m going to explain first of all what I mean by docking as an impractical problem. Then show how negative data is a practical solution to this problem.
  2. It’s sometimes compared to looking for a needle in a haystack. In the picture shown, I think you’ll agree that it’s pretty easy to pick out the needle. However, in the real situation, it’s much more difficult.
  3. I reference here a paper by Pham and Jain which provides an excellent introduction to this idea of using negative data.
  4. Because we want to optimize the scores of docked poses of actives, we used docked poses of the actives as our positive data.
  5. So we’re trying to directly optimise performance in a virtual screen, by ensuring that the scores of the actives are as high as possible with respect to their inactives.