Computational Chemistry: From Theory to Practice
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Computational Chemistry: From Theory to Practice

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This is the talk I gave when I interviewed for my first role at Boehringer Ingelheim Pharmaceuticals, Inc.

This is the talk I gave when I interviewed for my first role at Boehringer Ingelheim Pharmaceuticals, Inc.

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Computational Chemistry: From Theory to Practice Computational Chemistry: From Theory to Practice Presentation Transcript

  • Computational Chemistry: From Theory to Practice 6th December 2007 David C. Thompson
  • Overview An introduction to computational chemistry – Which method, where, and why? A novel 3D QM-based descriptor (perhaps?) Computational chemistry for drug design – Fragment-based de novo design
  • Some background, and some theory
  • The Problem  Motivation: Top 20 best-selling drugs in America had sales of ~ $65bn in 2005[1]  New drug development costs are in excess of $800M[2]  Roughly 10K structures are made and tested for every new drug reaching the market[3][1] The Best-Selling drugs in America, IMS health, 2006[2] The Tufts Center for the study of drug development[3] Boston Consulting Group, 2005
  • The Solution Solve the Schrödinger equation: H" = E" Ψ determines all properties of the system!
  • Unfortunately…“The underlying physical laws necessaryfor the mathematical theory of a large part ofphysics and the whole of chemistry are thuscompletely known, and the difficulty is onlythat the application of these laws leads toequations much too complicated to besoluble.” – P. A. M. Dirac (1929)
  • The Solution - DFT? The electron density, ρ, can be derived from Ψ And, it turns out that all properties of a system can be derived from ρ – ρ is a function of 3 variables – Ψ is a function of 4N variables This is great, right? – Sure, but didn’t I tell you? In getting this far, I made a functional which contains all of the “confusion”, and I don’t rightly know what it looks like. . .
  • Accuracy vs. Speed Accuracy Speed Ph.D105-6 EMM 104 EHF 102 EDFT 101 E PD1 PD2 EDFT can be improved but we need to understand the physics of how “electrons get along”: Ec=E-EHF
  • Gas phase water: An example  A DFT calculation takes ~9s  An “Exact” calculation[4] took 150h, 250Gb of memory, and 800Gb of disk[4] G. K.-L. Chan and M. Head-Gordon, J. Chem. Phys. 118, 8551 2003
  • Gas phase water: An example Water has 10 electrons The 1A4Q receptor has ~104 valence electrons A full quantum mechanical calculation is just not practical
  • The Hospital that ate my Wife. . . Information theoretic properties of a model system: Sr = " $ # (r) ln[ #(r)]dr S p = " $ % (p) ln[% (p)]dp ST = Sr + S p Doesn’t Sr look a little familiar? !
  • A novel descriptor?  Continuous form of a measure used in molecular similarity: S = "# pi ln[ pi ] i  Could we use Sr as a measure of similarity?  Moreover, could Sr be a 3D QM-based ! structural descriptor? – Literature search has shown that this has not been considered before (I think)[5][5] M. Karelson, “Quantum-chemical descriptors in QSAR”, in Computational MedicinalChemistry for Drug Discovery, P. Bultnick et al, Eds., (New York, Dekker, 2003), pp 641-667
  • A novel descriptor?  We want to make this useful – But we still have the problem of finding ρ in a timely fashion  Why don’t we approximate ρ? – We construct a pro-molecular density from a sum of fitted s-Gaussians[6] "(r) # " Mol (r) = % "$ (r) = % % c$i exp(&$i (r & R$ ) 2 ) $ $ i  Turns out that this isn’t as bad as you might think[7]! P. Constans and R. Carbó, J. Chem. Inf. Sci. 35, 1046 1995 [6] [7] J. I. Rodriguez, D. C. Thompson, and P. W. Ayers Unpublished data
  • Homebrew quantum mechanics  All of this has been done on my iMac at home  Molecular integrations performed using the Becke/Lebedev grids in PyQuante[8]  Co-opted graduate students into doing MathCad checks for me. . .[8] Python Quantum Chemistry - http://pyquante.sourceforge.net/
  • Homebrew quantum mechanics H1 Rz H2
  • Homebrew quantum mechanics Molecule Sr H2O -7.42 H2S 3.94 Benzene -27.09Cyclohexane (chair) -35.94 Perhaps Sr isn’t that discriminatory? Plan B - Sr (r) = " #(r)ln[ # (r)]
  • And that might look like. . .
  • Summary Introduced a novel, 3D, quantum mechanics based structural descriptor – Its utility, if any, will be further examined Feedback is encouraged
  • Some background, and some practice
  • Project involvement Detailed analysis of in-house high-throughput virtual screening protocol − Detailed curation of large data set of protein-ligand complexes Late-stage discovery project support − Lead optimization − Lead generation Fragment-based de novo design
  • Fragment-based de novo design: The problem at hand Search space of new molecular entities is essentially infinite – The number of chemically feasible, drug like molecules ~1060-10100 Such a large space cannot be searched exhaustively De novo design offers a broad exploration of chemical space – The range of molecules generated is only limited by the heuristics of the de novo design program
  • Ligand Efficiency High ligand efficiency area #G RT ln(IC50 ) LE = " $" N N Low ligand efficiency area !R. Carr et al., Drug Discov. Today, 10, 987 2005
  • Project requirements Exploit potential gaps in literature If possible use in-house chemical equity Modular design Efficient deployment strategy
  • De novo design: Link or Grow? LINK GROWG. Schneider et al., Nature Reviews Drug Discovery, 4, 649 2005
  • CONFIRM O O- OH d A pre-prepared bridge library is searched using the atom type of the connection points, and the distance d as a search query Bridge library db Bridges that match the search query d are attached to the fragments O O- OH + N N Complete molecules are prepared for docking – enumeration of … O tautomers, isomers, and ionization states N H O O- OH Prepared molecules are docked into the target binding site
  • Bridge Libraries Bridge library derived from  Application of filters corporate database − Molecular Weight≤ 3 rot. bonds ≤ 4 rot. bonds • <200 MW − No. of rotatable bonds Lib3 Lib4 • ≤3 • ≤4 OMEGA Expansion  Conformational expansion with OMEGA Lib3E Lib4E – 4 bridge libraries • Lib3 → Lib3E • Lib4 → Lib4E
  • CONFIRM: Novelty  Bridges come from molecules within the Wyeth CORP database: – Bridges obtained “…from a given ring scaffold by removing all of the atoms, except acyclic linker atoms, between pairs of ring systems, and the anchor atoms on the ring system.” [9]  Similar to CAVEAT[10], however: – We do not use orientation of bonds, but location of atoms (vector vs. scalar) – CAVEAT searches 3D databases looking for suitable molecular frameworks to satisfy the vector pairs • We already have well defined positions of small molecule binders[9] R. Nilakantan et al., J. Chem. Inf. Mod. 46(3), 1069-1077 2006[10] G. Lauri, and P. A. Bartlett, J. Comp.-Aided. Mol. Design 8(1), 51-66 1994
  • CONFIRM: Test Sets Taken from the curated data set of protein-ligand complexes – High crystallographic resolution ≤ 2.2Å – Two well resolved fragment moieties connected via a bridge – Both fragments interact with spatially disparate regions of the protein PDB Ascension RMSD/Å Resolution/Å Code SP XP 1SRJ 1.80 1.19 0.95 1A4Q 1.90 0.27 0.29 1YDR 2.20 0.40 0.43 1FCZ 1.38 0.30 0.43
  • CONFIRM: 1SRJ example- O O OH N N Bridge 3.7Å Fragment 1 Fragment 2 1SRJ X-ray Structure (green carbons) CONFIRM XP Pose (orange carbons)
  • CONFIRM: 1A4Q example -O O Bridge O N NH2 O HN O Fragment 1 5.9Å Fragment 2 No. of No. with 1A4Q X-ray Structure (green carbons)Library Unique Fragment 1 and Hits 2 RMSD < 2Å CONFIRM XP Pose (orange carbons) Lib4 274 84Lib4E 370 154
  • CONFIRM: 1MTU example Important for binding – we wish to keep this fragmentSearch bridge library for suggestions for bridging atoms Use ROCS to search for alternative groups to go here
  • CONFIRM: 1MTU example Search Lib4E with distance query of 5Å – 2852 bridges Search Lead-like database using ROCS and this query: X O N HN Use Combo score, only keep top 100 Use CONFIRM to enumerate, prepare, and dock
  • CONFIRM: 1MTU example
  • CONFIRM: 1MTU example
  • CONFIRM: 1MTU example
  • Summary Following comprehensive literature search, multiple algorithms for linking/growing fragments developed Final linking approach, dubbed ‘CONFIRM’, uses in- house chemical equity Modular design, allowed for rapid: − Implementation − Testing − Analysis and modification Publication completed, submitted to . . . Currently exploring use on drug discovery projects
  • Acknowledgments Computational Chemistry Group at Wyeth Research Cambridge Dr. Christine Humblet Prof. K. D. Sen Prof. P. W. Ayers – J. S. M. Anderson – J. I. Rodriguez