CD and MD

1. CD and MD

2. What’s my problem with MD? • Its development has been manifestly unscientific • Its answers (numbers, trajectories, minima) are as unreliable (or more) than simpler methods • Yet its manifest societal advantages- “physics”, movies, CPU time, complexity, jargon- lead to cognitive dissonance (hopeful thinking) concerning its actual value to drug discovery

3. CD: Cognitive Dissonance Wikipedia:- Cognitive dissonance theory explains human behavior by positing that people have a bias to seek consonance between their expectations and reality. According to Festinger, people engage in a process he termed "dissonance reduction," which can be achieved in one of three ways: lowering the importance of one of the discordant factors, adding consonant elements, or changing one of the dissonant factors. This bias sheds light on otherwise puzzling, irrational, and even destructive behavior. Lowering importance- Actually agreeing (numerically) with experiment Adding consonance- “It’s an idea generator” Changing the dissonance- Reparameterizing (+ Effort Justification Paradigm)

4. AM I CD? • Came from Barry’s Lab (the Great PB MD Wars) • Don’t sell MD (perhaps I’m jealous) Why should you believe me? -Don’t write/ need grants -Don’t need tenure -PB is not a significant OE income stream -Been observing MD for > 25 years -I hired an MD guy (who I sent to China!) -I manifestly want this to be a better industry

5. Also.. • The fastest PB- DelPhi, ZAP • The fastest surfacing algorithms- GRASP, ZAP • The fastest 3D shape alignment- ROCS, FastROCS • The fastest conformer generator- OMEGA • The fastest, non-stochastic docker- FRED • The fastest (accurate) Surface Area, RMSD, AM1, protein pka, proton placement.. • If I wanted to do MD, mine would rock • I believe the effort/reward ratio is (way) too low

6. How Galileo Transformed Science Think something up • Resolution • Demonstration • Experiment See if it matches available evidence Think of a new experiment to test it (to differentiate from old theories)

7. A Galilean Value Scale for Experiments • Retrospective Data that shapes the theory • MD, Most of molecular modeling, economics • Prospective without Controls • Rich Friesner, Xavier Barril • Unanticipated Retrospective Data • SAMPL solvation energies • Prospective designed with NULL model Controls • Bertrand Garcia Moreno, protein pKa Collective • Lyall Isaacs, SAMPL host-guest • Prospective to distinguish from Best-of-Class Controls • Nobody Better

8. A Galilean Value Scale for Experiments • Retrospective Data that shapes the theory • MD, Most of molecular modeling, economics • Prospective without Controls • Rich Friesner, Xavier Barril • Unanticipated Retrospective Data • SAMPL solvation energies • Prospective designed with NULL model Controls • Lyall Isaacs, SAMPL host-guest • Bertrand Garcia Moreno, protein pKa Collective • Prospective to distinguish against Best-of-Class Controls • Nobody Vast Majority Better

9. Prospective Without Controls • Surgeons coming up with new procedures • Osteoarthritis & Arthroscopic knee surgery • US Foreign policy • Just do something, claim success when it works, bury it when it doesn’t • Anecdotal stories • The “hot hand” phenomena • I did “X”, it worked.

10. I did “X”, it worked Two chief fallacies (i) Fallacy of Composition -What else did you actually do (ii) Fallacy of Selection -File Drawer effect (False Positives) -Parameterization (implicit or explicit) to the result (False Negatives)

11. Fallacy of Composition • Method X, e.g. MD, is but one part of a multipart process (filtering, chemists inspection, database bias)- success is claimed for X alone • The same procedure with X replaced with a different method is never done/ presented

12. Example of Composition Error • We predicted affinity with MM/QM and “It Worked” • Was QM getting you anything? • Did you do MM with QM-level charges, multipoles? MM alone? A scoring function?

13. Example of Composition Error • We used a polarizable force field and got these results for the (SAMPL4) host-guest systems. “It Worked”, so polarization worked. • Did you also try it without polarization? With better quality charges? With equivalent CPU time but without polarization (more sampling)?

14. Example of Composition Error • We ran MD for a bit, looked at how the ligands wiggled and designed six drugs (Christopher Bayly & others at Merck Frosst) • Did you compare to MM? To other simple heuristics? Without any chemists input? • It’s not “Science” until someone else does it

15. Fallacy of Selection:The Tanimoto of TruthTM An Event Happened An Event Didn’t Reality Predictions ToT = Events that happened and were predicted Events predicted or happened

16. The Tanimoto of Truth The Tanimoto of Truth An Event Happened An Event Didn’t Reality Predictions Published Especially by Academia

17. The Tanimoto of Truth The Tanimoto of Truth An Event Happened An Event Didn’t Reality Predictions “File Drawer” False Positives Especially by Industry

18. The Tanimoto of Truth The Tanimoto of Truth An Event Happened An Event Didn’t Reality Predictions False Negatives- Parameterize till publishable Especially by Academia

19. The Tanimoto of Truth The Tanimoto of Truth An Event Happened An Event Didn’t Reality Predictions True Negatives- Not sexy, “Hempel’s Ravens” Largely ignored by Academia & Industry

20. The Tanimoto of Truth The Tanimoto of Truth • “Similarity” methods, Docking, Machine Learning • All are judged by some kind of ToT • Quantification for MD ‘events’? Never. • MD is mostly uncontrolled, anecdotal & unscientific Psychology, Philosophy, Social Dynamics Underlying Physics, Examination of Successes

21. Molecular Dynamics:Types of Applications 1) Global sampling- thermodynamic averages -FEP etc. Absolute or Relative Energies 2) Simulate time evolution (movies) -D.E. Shaw, Vijay Pande- Mechanism 3) Local sampling (thermally accessible barriers) -Bayly & co., WaterMap, MM/PBSA. Qualitative Assessment

22. Thermodynamic energies and Fables of Physics “We all know that if we had the perfect force field and simulated for an infinite time, we’d get the right answer”- Woody Sherman, ACS San Francisco, March 24th, 2010 pKa, Tautomers Finite temperature, MD & Stat Mech Ergoticity? The illusion of a ‘perfect” ForceField (that ≠ QM)

23. Typical FF Thinking: Polarization • Polarization is tricky • But it makes dipoles bigger, e.g. water • 1.85D (vacuum)  2.5~2.6D (condensed phase) • So therefore increase charges by ~15% • E.g. use HF-6-31G* • Now molecules are roughly correct

24. Polarization of Dipoles -|+ -|+ D - + -|+ -|+ - + -  + - + - + - E0 Favorable Epol -|+ +|- D - - -|+ +|- - - -  - - - - - - E0 Unfavorable Epol

25. Scaling vs Polarization Scaling dipoles can only be accurate on average (with parameterization) not locally!

26. Ah, but then there’s AMOEBA EPIC Quantum mechanics PID AMOEBA (“PB”!) (Jean-Francois Truchon) Kim Sharp: JF

27. Applications: cation-p Acetylcholinesterase JF

28. Hydrogen Bonds: Formamide dimer “Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations” A. V. Morozov, T. Kortemme, K. Tsemekhman and D. Baker, PNAS, Volume 101, page 6946, 2004.

29. Geometry optimizations starting fromthe Baker MP2 minimum

30. Geometry optimizations starting fromthe Baker MP2 minimum

31. Geometry optimizations starting fromthe second MP2 minimum

32. Geometry optimizations starting fromthe second MP2 minimum

33. Ah, but then there’s AMOEBA *CCSD/aug-cc-pVTZ

34. Fitting to the electron density Denny Elking, Tom Darden

35. Or…… Increase Dipole from 1.85D to 2.56D

36. Details, Details.. 1) Just incorporate Volume Terms (PB) 2) And all those other terms: - Exchange interactions - VdW anisotropy - pKa & Tautomers - Cross-terms between valence and non-bonded - Three (N) body terms…. Eventually it’ll be right! Woody’ll be right. Inconceivable it can’t ever be right. (Wolynes)

37. Concrete MD Examples • Binding Energies- Shirts - Also Solvation (Simpler system) • Protein Trajectories- Shaw - Also Peptides (Simpler systems) • “Minimization” – Shoichet - Is a simple system

38. FKBP-12 Unanticipated Retrospective Data?

39. FKBP-12 Again

40. FKBP-12 Yet Again Retrospective Data that shapes the theory

41. Contributions to Affinity VdW Desolvation Entropy Discrete Waters Coulombic Polarization Buried Area

42. Correlations to Affinity Shape Buried Area Entropy Polarization VdW Coulombic Discrete Waters Desolvation Electrostatics

43. E.g. VdW Train on 17 HIV-1 Protease Inhibitors 1) Minimization (MM2X) 2) pIC50=-0.15*Einter-8.1 Prospectively used on 16 more

44. E.g. Coulombic Coulombic Interaction Brown & Muchmore, JCIM, 2007, (47) 4 Urokinase

45. E.g. Buried Area “Fast and Accurate Predictions of Binding Free Energies using MM-PBSA and MM-GBSA”Rastelli, G., Del Rio, A.,Degliesposti,G., Sgobba, M. J. Comp. Chem. Vol 31, #4, pg 797-810 Buried Area MM-PBSA DHFR

46. My observation over 20 years • For congeneric series, something basic often correlates, sometime well (VdW, Coulombic) • For non-congeneric usually nothing works • If something works for non-congenerics, it’s usually something basic (mass, buried area)

47. Simpler System: Solvation

48. SAMPL4: 50 Solvation Energies My PB Method Best MD QM + Specific Group-wise Parameterization

49. Structural basis for modulation of a G-protein-coupled receptor by allosteric drugs- D. E. Shaw • Where they bind • - Confirmed by mutagenesis • 2) A surprise in how they bind • -pi-charge interactions • -not charge-charge • 3) Cause of allostery: • Charge • Binding pocket width • -Confirmed by synthesis

50. IMHO • Where they bind • - Confirmed by mutagenesis • 2) How they bind • -pi-charge interactions • -not charge-charge • 3) Cause of allostery: • Charge • Binding pocket width • -Confirmed by synthesis Docking with Glide did almost as well. Confirmation is WEAK. 2) THIS IS NOT A SURPRISE! 3) (i) Already known & follows charge multiplicity exactly. (ii) –ONE CMPD (better than most!)