Saturday, 7 April 2018


I first became aware of Louis Hammett during the third term of my first year as an undergraduate at the University of Reading. Hammett was a pioneer in physical-organic chemistry and is widely regarded as one of the founders of that field. He would have been 124 today and was less than a year younger than Christopher Ingold, another pioneer in the field. Hammett passed away in 1987 at the age of 92 (here is an excellent obituary).

Today Hammett is remembered primarily for the parameters that describe electronic interactions between aromatic rings and their substituents. He also introduced linear free energy relationships which form the basis of classical QSAR. These days, QSAR has evolved away from its origins in physical-organic chemistry into what many call machine learning and parameters have become less physical (and considerably more numerous). Hammett's work provided an early lesson to wannabe molecular designers in how to think about molecules.

Jens Sadowski and I introduced matched molecular pair analysis (MMPA) in a chapter of a cheminformatics book that was conceived and edited by my dear friend (and favorite Transylvanian) Tudor Oprea. Here's a photo of Tudor and me at an OpenEye meeting (I think CUP II in 2001) during which our props (Tudor is wearing a PoD cape) were provided by the session chair (the formidable Janet Newman who intimidates proteins to the extent that they 'voluntarily' crystallize).

Now you might be wondering what MMPA has to do with Hammett. The short answer is that our book chapter included a table of what are effectively substituent constants for aqueous solubility and these have Hammett's fingerprints all over them. The longer answer is that Hammett introduced the idea of associating parameters with structural relationships (e.g. X is chloro analog of Y) between compounds. This is an important idea because much pharmaceutical design is focused on understanding and predicting the effects of structural modifications on the activity and properties of compounds. One rationale for this focus is the belief that it is easier to predict differences (e.g. relative affinity) in chemical behavior between structurally-related compounds than it is to predict chemical behavior directly from molecular structure.

At first, I didn't see the deeper connection between Hammett's work and pharmaceutical design. The main focus of our book chapter was preparing chemical structures in databases for virtual screening so the full extent of Hammett's influence on MMPA was not immediately recognized. As is often the case, we think we've discovered something really new only to find out later that somebody had been thinking along similar lines many years before. 

Happy 124th birthday, Louis Hammett.  

Sunday, 1 April 2018

The maximal quality of molecular interactions

There is a lot more to drug design than maximization of affinity and the key to successful design is actually that drugs form high quality interactions with their targets. Before the epiphany of ligand efficiency, measurement of interaction quality was a very inexact science. Ground-breaking research from the Budapest Enthalpomics Group (BEG) now puts the concept on a firm theoretical footing by unequivocally demonstrating that individual interactions can be localized on the affinity-quality axis in a unique manner that is completely independent of the standard state definition.

The essence of this novel approach is that, in addition to to its contributions to enthalpy and entropy of binding, each molecular interaction will now be awarded points for the artistic elements of the contact between ligand and target. This industry-leading application of Big Data uses the Blofeld-Auric Normalized Zeta Artificial Intelligence (BANZAI) algorithm to score aesthetic aspects of molecular interactions. This revolutionary machine learning application uses variable-depth, convolutional networks to model the covariance structure of the reduced efficiency tensor. Commenting on these seminal and disruptive findings the institute director, Prof. Kígyó Olaj, noted that "the algorithm is particularly accurate for scoring synchronization of vibrational modes and is even able to determine whether or not a hydrogen bond has made deliberate use of the bottom of the pool to assist another hydrogen bond during the binding routine".