1. Comb-e-day e-models Dr Jonathan W Essex University of Southampton

2. e-models Comb-e-chem –End-to-end model: Experiment DataDerived results Pattern searching Broad conclusions NCS Other databasesCalculations Statistical analysis

3. Solubility Prediction Critical property for the pharmaceutical industry Solid-state structure from X-ray diffraction –Structural analysis for patterns (Jeremy Harvey) Calculation of single-molecule properties Calculation of bulk properties Statistical modelling to yield predicted solubilities

4. Solubility Prediction Optimisation of the statistical model requires lots of experimental data Database –Store known experimental data Many values, different sources, different conditions Some data missing! More data arriving all the time New types of data arriving –Store results from different modelling studies –Store and recover workflows –RDF and triple store (Kieron Taylor)

5. Solubility Prediction Calculations –Automatic update of statistical models as new data become available –How and where are these calculations performed? Condor? United Devices? Larger grid? –E-learning (Robert Gledhill & Sarah Kent) –Protein conformational change (condor and small dedicated cluster) (Christopher Woods)

6. Investigating conformational change Difficult to investigate the conformational change experimentally, as P-NtrC is short-lived Simulate the NtrC protein and encourage the conformational change on the computer

7. Solubility Prediction Update existing statistical models using more modern methods –Collaborations between statisticians and chemists –50 % improvement in model predictions for solvation free energies (Ralph Mansson) –E-learning (Dan Grove)