Development of a Ligand Knowledge Base

Natalie Fey

Crystal Grid Workshop

Southampton, 17th 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 structuraland electronic parametersfor known and unknown

TM complexes

LigandKnowledge

Base

Part 1: “Unusual” Geometries

statistical analysis of results

apply outlier criteria

DFT geometryoptimisation

Query CSD for structural pattern

Main Geometry / Trends

Outliers

Optimised Geometries

Crystal Structureand DFT agree

Crystal Structureand DFT disagree

compare withcrystal structures

Automatic

Part 1: “Unusual” GeometriesCrystal Structureand DFT agree

Structure Report

Note in database,may confirm by DFT

Additional results,add to database

Flag for detailed investigation

Why outlier?

Comment aboutstructure?

Yes No

Further calculations

Value Added

Part 1: “Unusual” GeometriesCrystal Structureand DFT disagree

Structure Report

Revised Calculations

Crystal Structureand DFT agree

Crystal Structureand DFT disagree

Note in database

Flag for detailed investigation

Problem with Calculation

Problem withStructure

Why?

Comment aboutstructure?

Yes No

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-[RuCl2(2,6-(CH3)2C6H3NC)2]

Ru

Cl Cl

N N

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:

P P

av. P-R, CSD

1.891.871.851.83

av.

P-R

, ca

lcu

late

d

1.96

1.94

1.92

1.90

1.88

1.86

Cy3

tBu3

Bu3

iPr3

Pr3Et3

Me3

Examples Stage 2:

Solid State Rh-P Distance (Rh(I), CN=4)

2.275

2.325

2.375

2.425

2.275 2.325 2.375 2.425

experimental

predicted

Predicted Value

2.442.402.362.322.28

Re

sid

ua

l

.003

0.000

-.003

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: