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Cresset: 25 year of Fields
 

Cresset: 25 year of Fields

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Mark Mackey takes a humourous look at Cresset's origins and the lessons we had to learn to get where we are today.

Mark Mackey takes a humourous look at Cresset's origins and the lessons we had to learn to get where we are today.

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    Cresset: 25 year of Fields Cresset: 25 year of Fields Presentation Transcript

    • 25 Year of Fields: What Have we Learned? Mark Mackey
    • CressetBiologically relevant method for comparing molecules Bioisosteres Bioisosteric groups
    • How did we get there?A glorious tale of intrigue skullduggery sex deception
    • How did we get there?A glorious tale of unbelievably expensive graphics hardware phosphodiesterases molecular electrostatics almost no enrichment sex at all graphs Fortran 77
    • How did it all start?“Some Italians in „73 or „74 did 2D plots of ESP”Harel Weinstein (1982ish) 2D vectors on 5-HTDHFR work at Wellcome mid-80s
    • SK&F> COSMIC modelling package> Modelling PDE III inhibitors (Davis, Warrington, Vinter, JCAMD 1987, 1(2), 97)
    • Promotion at SK&F1988 All science ceased as Andy was promoted to head of IT1989 All science started again as Andy was fired as head of IT
    • Lesson 1Not all brilliant scientists make brilliant managers
    • Cambridge and Consulting1990 – Jeremy Sanders and Chris HunterThis led to the development of a full force fieldalong the same lines (Vinter, JCAMD 1994, 8, 653-668)
    • Lesson 2To get good answers using fields, you need good fields
    • Publication at last!“Multiconformationalcomposite molecular potentialfields in the analysis of drugaction. I. Methodology andfirst evaluation using 5-HTand histamine action asexamples”J. G. Vinter and K. I. Trollope,JCAMD 9 (1995) 297-307
    • The critics‟ verdict? “Incomprehensible”“Multiconformational composite molecular potential fields in the analysis of drug action. II” has yet to appear.
    • Lesson 3 If you write papers that people can‟t read, they don‟t read them“Molecular Field Extrema as Descriptors of Biological Activity: Definitions and Validation” T.Cheeseright, M. Mackey, S. Rose and A. Vinter, JCIM 2006, 46, 655-676Critics‟ verdict: “Mostly incomprehensible”.
    • James Black Foundation and Napp> Field analysis now gave good(ish) qualitative results> Quantitation was a problem
    • Original idea > Align and score purely on the position and size of the field points > Define a „pseudo-Coulombic‟ potential between field points: size( fp1)  size( fp 2) E fp1 fp 2  dist offset
    • Original idea > Align and score purely on the position and size of the field points > Define a „pseudo-Coulombic‟ potential between field points: size( fp1)  size( fp 2) E fp1 fp 2  dist offset
    • Problems: Different well widths
    • Problems: Different well widths> Not really soluble with a field point representation– this is some of the information we „throw away‟ going to a field minimum-based representation> Unfortunately, this leads to less-than-optimal results> Tried ellipsoidal field points etc but it didn‟t help much
    • New idea – field sampling> For a given field point in molecule A, instead of estimating what the field would be at the corresponding point in B from the positions of its field points, why not calculate directly? A B
    • New idea – field sampling E A B   size( fp fp A A )  FB ( position ( fp A )) A B
    • New idea – field sampling E A B   size( fp fp A A )  FB ( position ( fp A )) E A  B  EB  A  2 E AB E AB  S AB 2 E AA EBB A B
    • Advantages> The entire „true‟ field is used in the calculation > Potential well widths implicitly included> Fast to calculate > Only a few field values need to be calculated> Samples fields at biologically-relevant points> Gauge-invariant
    • Lesson 4 Field Points aren‟t enough You need the field as well
    • More development> Changed the vdW field > Used to be scaled by visible surface area, calculated 13C NMR constants and other stuff> Added the hydrophobic field> Improved methods for generating initial alignments > Field permutations > Monte Carlo > Grid-sampled Monte Carlo > Greedy clique matching
    • Cresset!> Cresset founded in November 2001> Business plan: 1. Condense field points into fingerprints 2. Stuff in Oracle 3. $$$$$
    • FieldPrintsInitial testing showed brilliant results 100 90 80 70 % Hits Retrieved 60 Actual 50 Perfect Random 40 30 20 10 0 0 10 20 30 40 50 60 70 80 90 100 % Database Retrieved
    • FieldPrintsLater testing showed insipid results
    • Lesson 5 If the experiment works, never repeat it Ok, not really
    • FieldPrintsWhy did it look OK earlier? Actives Decoys • Large • Small • Positively charged • Neutral Surprise! FieldPrints can tell the difference!
    • Lesson 6 Testing virtual screening methods is hard. Really hard. Even when you know how hard it‟s going to be, it‟s harder than that. See “Benchmarking Sets for Molecular Docking”, Huang et al. J. Med. Chem., 2006, 49(23), 6789-6801 “What do we know and when do we know it?”, Nicholls, JCAMD, 2008, 22(3) 239-255 “FieldScreen: Virtual Screening using Molecular Fields”, Cheeseright et al. JCIM, 2008 48(11) 2108-2117“Better than Random? The Chemotype Enrichment Problem”, Mackey and Melville, JCIM, 2009 49(5), 1154-62 and more
    • So where did we end up?> FieldPrints didn‟t work very well > But the full field similarity algorithm did (T. Cheeseright, M. Mackey, J. Melville, J. G. Vinter. (2008) FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set J. Chem. Inf. Model. 48, 2108) > Used on ~100 virtual screening projects so far > ~80% success rate
    • Lesson 7 See Lesson 4* Sometimes you have to learn lessons twice *“Field points aren’t enough: you need the field as well”
    • Other uses for field similarity> FieldAlign > Small-scale alignments and similarity scoring > Useful for SAR
    • Other uses for field similarity> FieldStere - Use field similarity to score bioisosteric replacements > Avoids fragment scoring limitations > Allows for electronic influence of replacing a moiety on the rest of the molecule and vice versa > Allows for neighbouring group effects
    • Other uses for field similarity H N+ N O OH O N H HO O O 3 CCR5 actives O FieldTemplater N N N+ FN N H FF O Use Fields to cross compare the actives F Understand the pharmacophore - a detailed Field map of N activity H HF H N N + N N O H Employ the template in FieldAlign, FieldScreen, FieldStere
    • Other uses for field similarity > Field-based QSAR 9 9 Training Set (1) 8.5 8 Test Set (1) 8 Residuals (Train) 7 7.5 Residuals (Test) 6Predicted Activity 7 5 Electrostatics 6.5 4 6 3 5.5 2 5 1 4.5 0 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 Activity RMSE 0.19, PRESS 0.51, RMSEpred 0.64 Sterics
    • And more research> Current field similarity algorithm works well> But could do better > Improved force field (XED FF3) > Formal charges > Dielectric/solvent attenuation > Clipping > Up/downweighting different regions of the fields > Use the protein to determine which parts of the field are relevant
    • Lesson 8 Even when it‟s good, it could be better. There‟s always more research to do
    • Lesson 9 If you didn‟t want to listen to me waffle on, you should never have let me begin
    • Acknowledgements> Andy (of course)> Tim Cheeseright> James Melville> Rob Scoffin> Brian Warrington> Lots of other people
    • 25 Year of Fields: What Have we Learned? Mark Mackey