Development of a Ligand Knowledge Base

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Development of a Ligand Knowledge Base

• Natalie Fey
• Crystal Grid Workshop
• Southampton, 17th September 2004

2
Overview

• Ligand Knowledge Base
• Synergy of Database Mining and Computational
  Chemistry
• Part 1 How computational chemistry can add value
  to database mining results.
• Part 2 How database mining can inform a ligand
  knowledge base of calculated descriptors.

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Ligand Knowledge Base

• Aims
• Collect information about ligands and their (TM)
  complexes
• Database mining.
• Computational chemistry
• Exploit networked computing and data storage
  resources e-Science.
• Use data
• Interpretation of observations.
• Predictions for new ligands.

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Ligand Knowledge Base
Ligand Knowledge Base
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Part 1 Unusual Geometries
Automatic
statistical analysis of results
apply outlier criteria
DFT geometry optimisation
compare with crystal structures
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Part 1 Unusual Geometries
Crystal Structure and DFT agree
Value Added
Why outlier?
Structure Report
Comment about structure?
Yes
No
Note in database, may confirm by DFT
Flag for detailed investigation
Further calculations
Additional results, add to database
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Part 1 Unusual Geometries
Crystal Structure and DFT disagree
Value Added
Why?
Structure Report
Comment about structure?
Problem with Calculation
Yes
No
Revised Calculations
Problem with Structure
Crystal Structure and DFT agree
Further calculations
Crystal Structure and DFT disagree
Flag for detailed investigation
Additional results, add to database
Note in database
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Example 4-coordinate Ruthenium

• Main geometry tetrahedral (14 structures)
• 2 square-planar cases YIMLEL, QOZMEX
• YIMLEL cis-RuCl2(2,6-(CH3)2C6H3NC)2

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4-coordinate Ruthenium

• DFT result

• Use as CSD query, any TM
• SIVGAV Pd
• Supported by structural arguments
• short Ru(II)-Cl, Ru-CNR.
• correct range and geometry for Pd.
• Run DFT with Pd

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Part 2 P-donor LKB

• Range of DFT-calculated descriptors for
  monodentate P(III) ligands and TM complexes.
• Capture steric and ?/?-electronic properties.
• Identification of suitable statistical analysis
  approaches
• Interpretation.
• Prediction.

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Part 2 P-donor LKB

• Role of database mining
• Stage 1 Database generation.
• Inform input geometries (conformational freedom).
• Verification of chosen theoretical approach.
• Stage 2 Database utilisation.
• Supply experimental data for regression models.
• Confirmation of calculated trends.

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Examples

• Stage 1
• Conformers
• e.g. P(o-tolyl)3

• Method verification

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Examples

• Stage 2

Solid State Rh-P Distance (Rh(I), CN4)
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Conclusions

• Synergy of approaches allows to add value to
  structural databases.
• Computational chemistry can be used to verify
  solid state geometries.
• Can exploit e-Science resources to add value on a
  large scale.
• Utility of large databases for structural
  chemistry of transition metal complexes.
• Computational requirements.
• Statistical analysis.

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Acknowledgements

• Guy Orpen, Jeremy Harvey
• Athanassios Tsipis, Stephanie Harris
• Ralph Mansson (Southampton)
• Funding