Saturday, 1 April 2017

A concentration of scoring functions

Researchers at The Hungarian Institute Of Thermodynamics have published a number of seminal articles on the interplay of enthalpy and entropy in areas ranging from physical chemistry to socioeconomics. For example, the cause of World War 1 (also known as 'The Great War' although I doubt whether any of its participants thought that it was that great) was traced to a singularity in the Habsburg Partition Function. In a nutshell, the problem was shown to be a surfeit of the wrong type of entropy (which led to Franz Ferdinand's driver getting lost) coupled with a deficit in the right type of entropy (which would have prevented Gavrilo Princip's bullets from finding their targets). However, it is unlikely that any amount of the right type of entropy could have saved the hapless Maximilian I of Mexico, who generously volunteered to be Emperor only to be shot by the ungrateful Mexicans.

The most recent study from BEG (Budapest Enthalpomics Group) is little short of sensational. Unfortunately it's not available online and the poor fax quality, coupled with my rudimentary grasp of Hungarian, have made the going hard. The essence of this seminal study is that the performance of scoring functions can be significantly improved by including the concentration unit (in which affinity is expressed) as a parameter in the fitting process. The casual observer of virtual screening may have wondered why scoring functions are trained with affinity but validated by enrichment. By treating the concentration unit as a parameter in the fitting process, the authors were able to achieve unprecedented accuracy of prediction and the phone call from Stockholm would seem to be a foregone conclusion. Commenting on these seminal findings, Prof. Kígyó Olaj, the director of the Institute said, "Now we no longer need to use ROC plots to mask feeble correlations between predicted and measured affinity".     

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