The Basic Technology Research Programme

1. Proof of Concept Studies & Consortia Building Networks The Basic Technology Research Programme

2. Background • Cross research council endeavour • administered by EPSRC • Funding for research to create a new technology • Change the way we do science • Underpin the future industrial base

3. Background • 15 research projects funded up to April 2003 • Total funding for this period - £41M • To support large, long term, high risk, high impact research consortia • Encourage investigation of speculative ideas

4. Background • Two levels of funding • One year start up • Full grant up to five years • Two types of start up funding • Proof of concept • Consortia building networking

5. Proof of Concept Studies • One year funding up to £100K • Research to investigate feasibility of developing the new technology • Output – a business case for the next step of investigation to be submitted in May 2004 • Basic Technology Programme • Existing Research Council initiatives • DTI programmes

6. Consortia Building Networks • Involvement of the users of the new technology at a very early stage • Funding to form networks & hold workshops

7. ParaSurf – in silico Screening Technology • Basic Technology Funding for October 2003 to September 2004 • Proof of concept • Consortia building networking • Academic partners • University of Portsmouth • University of Erlangen • University of Southampton • University of Oxford • University of Aberdeen

8. ParaSurf – Proof of Concept Research Programme • Development of techniques to describe irregular solids & surfaces • Development of projection & pattern recognition techniques for non-planar colour-coded surfaces • spherical harmonics, molecular topology • Conformational analysis • Rigid body dynamics incorporating surface features • rigid parts of molecule treated as anisotropic solids linked by rotatable bonds • Investigate how best to generate prediction models using surface properties that define a low dimensional chemical space • QSAR, pattern recognition, artificial intelligence, analysis of surfaces • Bench marking using Grid computing

9. ParaSurf – Proof of Concept Research Programme

10. Potential applications of the in silico screening technology • High throughput virtual docking • Physical property mapping • ADMET prediction • Long time-period simulation techniques • Crystallisation and solubility • Prediction of tautomers • Chemical reactivity and metabolism

12. Letchworth, 16th March 2004 ParaSurf Progress Report

13. Main Areas • Molecular Surfaces and Property Calculation • RGB Encoding & Pattern Recognition • Conformational Analysis • Rigid Body Molecular Dynamics • Analysis of Variables & QSAR models • Grid Computing • Consortium Building

14. Datasets Small Consensus Set of 74 Drug Molecules (diverse) QSAR set (31 CoMFA steroids) Medium WDI subset (2,400 comps) Harvard Chembank dataset (2,000 comps) Large WDI (50,000) Maybridge (50,000)

15. Example Molecule Allopurinol

16. Surface Definition & Local Property Calculation

17. Calculations 3D co-ordinates from CORINA QM calculations with VAMP Local Properties and surfaces from ParaSurf

18. ParaSurf v1.0 Surfaces Isodensity Surfaces Shrink Wrap Marching Cube Surfaces fit to Spherical Harmonics Properties MEP, LIE, LEA and LP Encoded at points on the surface Encoded as Spherical Harmonic Expansions

19. Small molecule

20. RGB Encoding & Pattern Recognition

21. RGB Encoding Each Local Property encoded as a colour LIE encoded on Red channel LEA encoded on Green Channel LP encoded on Blue Channel

22. Allopurinol RGB Surface

23. RGB Encoding Alternative Encoding LIE LEA Absolute value of MEP

24. Allopurinol RGB Surface

25. Conformational Analysis

26. Conformational Analysis Efficient All Atom MD analysis (DASH) Treated as time series (not Cluster Analysis) Scales linearly with simulation length No need for arbitrary choice of number of clusters Can be analysed using Markov Chain methodology

27. MD studies of Rosiglitazone

29. Rigid Body Molecular Dynamics

30. Rigid body molecular dynamics Well founded methodology e.g. CNS / XPLOR (Axel T. Brunger, Stanford University) Idea is to use rigid groups to model flexibility: In the ligand and the protein binding site. Allows time-steps of 10fs to 20fs.

31. QSAR models

32. Distribution of Properties

33. LIE LEA LP MEP LIE 1 0.44 0.26 0.39 LEA 0.44 1 0.58 0.47 LP 0.26 0.58 1 -0.1 MEP 0.39 0.47 -0.1 1 Correlation Matrix

34. Descriptors 34 descriptors based on Normal Distribution Principal Components Spherical Harmonic Co-efficients

35. Maximum value of the local ionization energy Minimum value of the local ionization energy Mean value of the local ionization energy Range of the local ionization energy Variance in the local ionization energy Descriptors for LIE

36. Other Descriptors Moments Order 1 – Mean Order 2 – Variance Order 3 – Skewness Order 4 – Kurtosis Overlapping Gaussians Derived from previous work on MD analysis

37. QSAR models Models derived from Local Properties Surface Integral Model for Solvation Energy RMS Error ~ 0.75 Kcal Drug Likeness SOMs trained on WDI (drugs) & Maybridge (general) Parameters from PC of Local Property Descriptors Medium sized datasets superimposed on SOMs

40. GRID Computing

41. GRID Computing ParaSurf compiled on SGI IRIX Windows Linux (SUSE) IBM AIX Future Platforms SUN Solaris GRID enabling at Portsmouth (Mark Baker), Southampton and Oxford.

42. Provisional Timings SGI R10k, 256MB VAMP ~ 30s/compound ParaSurf ~ 10s/compound Intel 1.8 Xeon/ AMD Athlon XP-2000+ ParaSurf ~ 2s/compound SGI FUEL Workstation R14K ParaSurf ~ 2s/compound

43. Conclusions

44. Conclusions • Properties can be calculated • Properties can be RGB encoded • Properties are local • Properties can be used for QSAR models

46. Computer vision methods for comparing molecular surfaces • Comparing and recognising 3D objects is an active research area in robotics and AI. • Fast methods have been developed for database indexing. • Rotationally invariant descriptors of 3D objects are possible.

47. Pattern matching on molecular surfaces • Can we recognise similar surfaces? • Can we recognise similar surfacefragments? • Can we identify the most similar surface to our target? • How do we compare field descriptors on the molecular surface?

48. Rotationally invariant 3D object descriptors • Internal coordinates e.g. a distance matrix. • Energy distributions based on the spherical harmonics. • The spherical harmonic coefficients. • Radial integration, radial scanning, and invariant moments.

49. Surface comparison Two different approaches: • Using spherical harmonic molecular surfaces [J. Comp. Chem. 20(4) 383-395; Ritchie and Kemp 2000; University of Aberdeen]. • Partial molecular alignment via local structure analysis [J. Chem. Inf. Comput. Sci. 40(2) 503-512 ; Robinson, Lyne and Richards 1999; University of Oxford].

50. An example grid of surface points A grid is placed on a ParaSurf surface in order to reduce the number of surface points from 4038 to 55.