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Computational Chemistry Robots
 

Computational Chemistry Robots

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describes how to design and implement a protocol for high-through put computation

describes how to design and implement a protocol for high-through put computation

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    Computational Chemistry Robots Computational Chemistry Robots Presentation Transcript

    • Computational Chemistry Robots ACS Sep 2005 Computational Chemistry Robots J. A. Townsend, P. Murray-Rust, S. M. Tyrrell, Y. Zhang [email_address]
      • Can high-throughput computation provide a reliable “experimental” resource for molecular properties?
      • Can protocols be automated?
      • Can we believe the results?
    • Aspects of complete automation
      • Humans must validate protocols rather than individual data
      • Low rates of error must be addressed
      • Users should know the rates of error and degree of conformance
    • Approaches to conformance
      • Explore limits of job behaviour (times, convergence, etc.)
      • Analyse reproducibility
      • Vary and analyse effects of parameters and algorithms
      • Compare output with other “measurements” of same quantity
    • The overall view molecules computation dissemination
    • The overall view molecules computation dissemination Check results
    • Components of System
      • Workflow for management of jobs (Taverna)
      • Natural Language Processing based parsing of outputs (JUMBOMarker)
      • Pairwise comparison of data sets (R)
      • Analysis of mean and variance
      • Detection and analysis of outliers
    • Computing the NCI database MOPAC PM5 a a MOPAC PM5 – collaboration with J.J.P. Stewart
    • Protocol Log Files Parse System Crashes Science Errors Analysis Pathological Behaviour Statistics Other Science Disseminate Results Unsuitable Data Program Crashes Inform Developer
    • Taverna
      • Workflow programs allow a series of small tasks to be linked together to develop more complex tasks
      • Open Source
      • myGRID, eScience
      • European Bioinformatics Institute
      • University of Manchester
    • An Example Taverna Workflow
    • Parsing Log Files to CML Coordinates Molecular Formula Calculation Type Point Group Dipole Total Energy Computational Chemistry Log Files
    • CompChem Output Coordinates Energy Levels Vibrations Coordinates Energy Level Vibration CML File CMLCore CMLCore CMLComp CMLSpect Input/jobControl General Parsers
    • Dissemination of results LOG FILE CML FILE HUMAN DISPLAY WWMM* Server and DSpace Outside world JUMBOMarker NLP-based log file parser * World Wide Molecular Matrix
    • InChI: IUPAC International Chemical Identifier
        • A non-proprietary unique identifier for the representation of chemical structures.
        • A normal, canonicalised and serialised form of a chemical connection table.
        • InChI FAQ: http://wwmm.ch.cam.ac.uk/inchifaq/
    • Proteus molecules * Calculation JUNK Cured by MOPAC * Proteus was a shape changing ocean deity
    • Proteus molecules Calculation Input JUNK
    • How do we know our results are valid? Computational Method 1 Computational Method 2 Experiment
    • J.J.P. Stewart’s example Calculated  H f – Expt  H f
    • GAMESS MOPAC results GAMESS a 631G* B3LYP Log Files a Project with Kim Baldridge and Wibke Sudholt
    • Protocol Log Files Parse System Crashes Science Errors Analysis Pathological Behaviour Statistics Other Science Disseminate Results Unsuitable Data Program Crashes Inform Developer
    • Repeat runs, different methods Multiple runs give same final structure from same input Changing memory allocation doesn’t make a difference
    • Pathological behaviour - Early detection 100 min 631G*, B3LYP 200 min 15 min 631G*, B3LYP 10080 min divinyl ether trans-Crotonaldehyde Z matrix
    • Times to run jobs
    • Analysis of different computational methods Mean - Overall difference Normality - Distribution of values Outliers - Unusual molecules? Variance - Spread of the data, depends on both distributions. (standard deviation)
    • Probability Plot (Normal QQ plot)
    • Mean of distribution (Approx - 0.03 Å ) Range over which sample distribution is approximately normal Outliers Probability Plot (Normal QQ plot) S.D. 0.020 Å
    • All bonds*  r (MOPAC – GAMESS) / Å * Excludes bonds to Hydrogenc
    • All bonds*  r (MOPAC – GAMESS) / Å Good agreement Nearly normal Outliers S.D. 0.005 Å * Excludes bonds to Hydrogenc
    • 2- Bad molecules and data usually cause outliers Na P O O H H
    • Mean  r (M - G) / Å Standard Error of the Mean / Å All values given to 3 significant figures   C N O F S Cl C -0.006 0.020 -0.010 -0.014 -0.040 -0.037 0.000 0.000 0.000 0.001 0.001 0.001 N   0.006 -0.037   -0.055     0.001 0.001   0.009   O     -0.087   -0.070       0.004   0.014  
    •  r CC bonds (M - G) / Å
    •  r CC bonds (M - G) / Å Good agreement Nearly normal Outliers S.D. 0.013 Å JUNK
    • Selection of molecules with C C  r (M - G) > 0.05 Angstroms
    • Y = 0.0277 X – 0.0061 Non aromatic C C bonds adjacent to CF n
    •  r NN bonds (M - G) / Å
    • Good agreement Nearly normal Kink S.D. 0.022 Å  r NN bonds (M - G) / Å
    • Density plot of  r NN bonds (M - G) / Å
    • LEFT RIGHT Density plot of  r NN bonds (M - G) / Å
    • Most common fragments found in Left set but not Right set C(sp 3 ) C(sp 3 ) (sp 3 ) S(sp 2 ) N(ar) N (ar) C(sp 2 ) S(sp 2 ) N(ar) N (ar) C(sp 2 ) Or
    • GAMESS Log Files Comparison of theory and experiment CIF* CIF* CIF* CIF* CIF* CIF 2 CML * CIF: Crystallographic Information File
    • Reading Acta Crystallographica Section E
    • All bonds*  r (Cryst. – GAMESS) / Å Single molecules, no disorder * Excludes bonds to Hydrogenc
    • All bonds*  r (Cryst. – GAMESS) / Å Single molecules, no disorder Mean  r - 0.011 Å Nearly normal Outliers S.D. 0.014 Å * Excludes bonds to Hydrogenc
    •  r CC bonds (C – G) / Å
    • Mean  r - 0.01 Å Nearly normal S.D. 0.009 Å  r CC bonds (C – G) / Å
    •  r CO bonds (C – G) / Å
    • Good agreement Nearly normal Outliers ? S.D. 0.011 Å  r CO bonds (C – G) / Å
    •  r = +0.08 Å Chemistry can cause outliers H movement
    • Conclusions
      • Protocols can be automated
      • Machines can highlight unusual behaviour,
      • geometries and distribution of results for
      • humans to consider
      • Computational programs can provide high
      • quality “experimental” molecular properties
    • Thanks J.J.P. Stewart Kim Baldridge Wibke Sudholt Simon Tyrrell Yong Zhang Peter Murray-Rust Unilever
    • Questions Homepage: http://wwmm.ch.cam.ac.uk InChI FAQ: http://wwmm.ch.cam.ac.uk/inchifaq R: http:// www.r-project.org Taverna: http://taverna.sourceforge.net/ MOPAC 2002: http://www.cachesoftware.com/mopac/ GAMESS: http:// www.msg.ameslab.gov/GAMESS/GAMESS.html