Matched Molecular Pair Analysis in Drug Discovery

http://www.sciencedirect.com/science/article/pii/S1359644613000937

I'm very pleased to see our review of Matched Molecular Pair Analysis (MMPA) in Drug Discovery in print (link above). We were invited to write this article just at the start of building our new business around Knowledge Based Design - the application of the output from MMPA. We are grateful to the editor for giving us this opportunity and have tried to fill the gap in literature between the review from Ed and Andrew (Graeme Robb and Dan Warner too) back in 2010, but more importantly highlight where the science needs to go. There is so much to do - the science produces a wealth of information, almost too much to take in by the medicinal chemist (or design team) trying to make a decision - it needs simplifying or conversion to visual means (we have a plan - trust us!). But the biggest area to work on it the context problem - we drive this as chemists by alway thinking about 'adding' a group, increasing size - changing a hydrogen (or unsubstituted position) into a group, and tend to think of fluoro, methyl, methoxy, cyano - what about changing your existing group into something else - for example di-methyl amino into cyclopropyl-methyl amino? Now that will improve your hERG! We illustrate this example in the paper. As a result of these, we think we don't have enough date and more specific changes like the context of environment of the group (e.g. electron poor or rich aromatic ring). 'Big Data' we believe is the answer - the more data we can put in and analyse, by the algorithms to auto find match pairs, the more statistically robust the context dependant chemical design rules we can find. With Dan Warners and Steve St. Galleys WizePairZ this will capture information out to four atoms in all directions SO take a chloro replaced by nitrile on an aromatic ring. With four atoms out we could distinguish between a phenyl ring and a pyridine ring which could make all of the difference (read the paper Papadatos/Gillet describe this really well with group examples). So 'Big Data', we make the case for the pooling of data from big pharma to bring together enough to make this happen....

Predicting Human Serum Albumin binding

http://pubs.acs.org/doi/pdf/10.1021/ci3006098

I have enjoyed reading this morning the publication of Hall, Jorgensen and Whitehead on Automated Ligand- and Structure-Based Protocol for in Silico Prediction of Human Serum Albumin Binding (J. Chem. Inf. Model, ASAP, DOI:10.1021/ci3006098). I have battled a couple of times with acidic compounds which had high albumin binding. This paper was a good summary of several of the approaches to QSAR models and combinations docking and the tricky problem of site selection (3 principle bind site, 3 others and a couple of structures with another 2 - 8 in total). I am pleased they have made the work flow available at a KNIME module with Schroedinger plug ins. They used Induced Fit Docking (IFD) to place molecules into the respective sites with some success by the look of it. I must admit docking Warfarin in does look a nightmare with two lysines and a arginine to bind to.
As we are interested in Knowledge Based Design from Matched Molecular Pair Analysis (MMPA) these sort of QSAR models and docking approaches have a synergy. Can the output of MMPA, the power to suggest molecules to make that will improve a property, be combined with a prediction to improve further the decision of what to make next with high probability of what the make next?