Development of a Ligand Knowledge Base Natalie Fey Crystal Grid Workshop Southampton, 17 th September 2004

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.

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.

Ligand Knowledge Base Mine Structural Databases (e.g. CSD) Compile systematic structural information about TM complexes Computational Chemistry (e.g. DFT) Calculate structural and electronic parameters for known and unknown TM complexes Ligand Knowledge Base

Part 1: “Unusual” Geometries statistical analysis of results apply outlier criteria DFT geometry optimisation Query CSD for structural pattern Main Geometry / Trends Outliers Optimised Geometries Crystal Structure and DFT agree Crystal Structure and DFT disagree compare with crystal structures Automatic

Part 1: “Unusual” Geometries Crystal Structure and DFT agree Structure Report Note in database, may confirm by DFT Additional results, add to database Flag for detailed investigation Why outlier? Comment about structure? YesNo Further calculations Value Added

Part 1: “Unusual” Geometries Crystal Structure and DFT disagree Structure Report Revised Calculations Crystal Structure and DFT agree Crystal Structure and DFT disagree Note in database Flag for detailed investigation Problem with Calculation Problem with Structure Why? Comment about structure? YesNo Value Added Further calculations Additional results, add to database

Example – 4-coordinate Ruthenium  Main geometry: tetrahedral (14 structures)  2 square-planar cases: YIMLEL, QOZMEX  YIMLEL: cis-[RuCl 2 (2,6-(CH 3 ) 2 C 6 H 3 NC) 2 ]

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:

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.

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.

Examples  Stage 1 Conformers: e.g. P(o-tolyl) 3 Method verification:

Examples  Stage 2: Solid State Rh-P Distance (Rh(I), CN=4)

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.

Acknowledgements  Guy Orpen, Jeremy Harvey  Athanassios Tsipis, Stephanie Harris  Ralph Mansson (Southampton)  Funding: