Presentation on theme: "Stephanie Harris Crystal Grid Workshop Southampton, 17 th September 2004 Development of Molecular Geometry Knowledge Bases from the Cambridge Structural."— Presentation transcript:
Stephanie Harris Crystal Grid Workshop Southampton, 17 th September 2004 Development of Molecular Geometry Knowledge Bases from the Cambridge Structural Database
Molecular Geometry Knowledge Bases Library of chemically well-defined geometric information Limited user input Rapid retrieval of statistical data Cambridge Structural Database Stored geometric information for ~300,000 structures Search using Conquest Substructure search, user input required
Molecular Geometry Knowledge Base: Mogul Bond lengths, valence angles and torsion angles Compiled from the CSD Published bond length tables: Organic and metal containing structures Published late 1980s Compiled from CSD of ~50,000 structures Cannot be accessed by computer programs Applications Model building Refinement restraints Structure validation Comparative values
Mogul 1.0 Whole molecule input Graphical (cif, SHELX, mol2 files) or command-line interface Integration with client applications, e.g. Crystals Quick, automatic retrieval of statistical data, histogram distributions, CSD structures Search Algorithm All non-metal fragments in the CSD coded Set of keys code chemical environments Fragments with identical keys are chemically identical Use hierarchical search tree Generalised searching if insufficient hits
Mogul Search. S1. C7 Search
Metal – Ligand Bond lengths To be considered: Ligand type: Carboxylate Metal Oxidation State: Co(II) Metal coordination number: 6 Ligand trans: Oxygen ligand Spin State? Co-O bond length?
Method Analysis of M-L bond lengths. For a range of metal and ligand types identify factors which influence M-L bond lengths and evaluate their importance. For a defined Metal-Ligand group sub-divide bond length distribution to produce chemically meaningful datasets: Unimodal distributions. Reasonably small sample standard deviations. From hand-crafted examples develop an algorithm to produce a molecular geometry knowledge base for metal complexes.
Data Tree Metal-Ligand Group Bin A1 Sharpened distributions Smaller sample standard deviations Bin A2 Bin B2Bin B3Bin B1Bin B4 Bin C1Bin C2
1.Ligand, L 2.Coordination mode of ligand 3.Effective Metal Coordination Number 4.Metal Oxidation State 5.Metal clusters and cages 6.Spin state 7.Jahn-Teller effect 8.Metal coordination geometry 9.Ligand trans to L Criteria Influencing M-L Bond Lengths
Ligand Template Library Ligand Non-metal atom or fragment bonded to a metal. Two ligands are the same if they have same connectivity (topology) and stereochemistry. Method All ligands in CSD to be classified. Classify according to contact atom coordinated to metal. Ligands with multiple contact atoms can be present in more than one ligand group. e.g. SCN -
Cambridge Structural Database Approximately 22,000 formulae Approximately 780,000 ligands No. of occurrences of unique formulae in CSD Total Number of Ligands Number of formulae 550,000 (70%) – ,263 (14%) – 9976,000 (10%) – 945,700 (6%)18,937 Ligand Template Hierarchy Exact ligand templates (724) R-substituted templates (Hs replaced with innocent R groups) Generic templates (ALL ligands classified)
Cobalt Carboxylate Bond Lengths Co-O (Å) No. of Frags. Co-O: 1.929(62) Å 619 Fragments
1.929(62) Å Co(III)Co(II) 2.049(58) Å1.904(20) Å 2.073(42) Å1.904(20) Å 2.074(32) Å 1.910(15) Å 1.895(17) Å
Chlorides Fe-Cl 2.242(68) Å 2.189(24) Å 2.166(84) Å High Spin 2.225(29) Å Fe(II)L 5 py Pyridines e.g. Fe (spin state) Cu(II)-OH (225) Å Copper complexes (Jahn-Teller effect) Standardisation of Cu connectivity Tertiary phosphines, Carbon-ligands
Metal-Ligand Knowledge Base 1.CSD data adjustment: Standardisation of metal connections Assignment of metal as part of a metal cluster Assignment of metal oxidation state 2.Classification of ligands by ligand template library 3.Perform algorithm on all possible M-L fragments to produce knowledge base
Metal-Ligand Group From ligand template library: Generic or more specific e.g. Carboxylates: Algorithm:
Metal-Ligand Group Division on Oxidation State Metal Clusters Division on Metal effective coordination numberDivision on spin and Jahn-Teller effect Only for particular metals, oxidation states and coordination numbers. Not found for all ligand types. Not searchable in CSD. Flag users, effects evident by: bimodal histogram, high SSD, outliers.
Metal-Ligand Group Metal Clusters Division on Oxidation State Division on Metal effective coordination number Division on spin and Jahn-Teller effect Division on Metal coordination geometry E.g. 4-coordinate geometry: Tetrahedral, square planar, disphenoidal
Metal-Ligand Group Metal Clusters Division on Oxidation State Division on Metal effective coordination number Division on spin and Jahn-Teller effect Division on Metal coordination geometry Divide on trans ligand to L Final Ligand division More specific ligand e.g. alkyl carboxylate
Generalised Searching No hits or insufficient number of hits. Allows the retrieval of data on related fragments. Hierarchical search tree structure Move up to a higher, less specific level of data tree. Order of algorithm important. Should order of criteria be changed? Should order depend on M-L group? E.g. Should oxidation state always be the first main division?
Conclusions Pre-processing of structural data from the CSD to construct molecular geometry knowledge bases. Knowledge bases to contain chemically well-defined datasets. Limited user input required. Quick, automatic retrieval of statistical data, distributions. Efficient analysis of large number of chemical fragments. Outliers, high SSD? Further Analysis – Computational Chemistry. Further development to include extra chemical information e.g. computational data.
Acknowledgements Bristol University: Guy Orpen Natalie Fey X-Ray Crystallography Group Cambridge Crystallographic Data Centre: Robin Taylor Frank Allen Ian Bruno Greg Shields