Free energy simulation methods such as free energy perturbation (FEP) have been around for a while and, back in the late eighties when my Pharma career started, they were being touted for affinity prediction in drug discovery. The methods never really caught on in the pharma/biotech industry and there are a number of reasons why this may have been the case including the compute-intensive nature of the calculations and the level of expertise required to run them. This is not to say that nobody in pharma/biotech was using the methods. It’s just that the capability was not widely-perceived to give those who had it a clear advantage over their competitors. Also there are other ways to use protein structural information in lead optimization and I’ve already written about the importance of forming molecular interactions with optimal binding geometry but without incurring conformational/steric energy penalties. Nevertheless, being able to predict affinity accurately would be high on every drug discovery scientist’s wish list.
A recently published study appears to represent a significant step forward and I decided to take a closer after seeing it Pipelined and reviewed. The focus of the study is FEP and a number of innovations are described including an improved force field, enhanced sampling and automated work flow. The quantity calculated in FEP is ΔΔG° which is a measure of relative binding affinity and this is typically what you want to predict in lead optimization. We say ΔΔG° because it’s the difference between two ΔG° values which might, for example, be a compound with an unsubstituted phenyl ring and the corresponding compound with a chloro substituent at C3 of that aromatic ring. When we focus on ΔΔG we are effectively assuming that it is easier to predict differences in affinity than it is to predict affinity itself from molecular structure and this is a theme that I've touched on in a previous post. Readers familiar with matched molecular pair analysis (MMPA 1 | 2 | 3 | 4 | 5 ) will see a parallel with FEP which I failed draw when first writing about MMPA although the point has been articulated in subsequent publications (1 | 2). Of course FEP has been around a lot longer than MMPA so it’s actually much more appropriate to describe the latter as the data-analytic analog of the former.
As with MMPA, the rationale is that it is easier to predict differences in the values of a quantity than it is to predict values of the quantity directly from molecular structure. The authors state:
“In drug discovery lead optimization applications, the calculation of relative binding affinities (i.e., the relative difference in binding energy between two compounds) is generally the quantity of interest and is thought to afford significant reduction in computational effort as compared to absolute binding free energy calculations”
This study does appear to represent the state of the art although I would like to have seen the equivalent of Figure 3 (plot of FEP-predicted ΔG° versus experimental ΔG°) for the free energy differences which are the quantities that are actually calculated. I would argue that Figure 3 is somewhat misleading because some of the variation in FEP-predicted ΔG° is explained by variation in the reference ΔG° values. That said, the relevant information is summarized in Table S2 of the supporting information and the error distribution for the relative binding free energies (ΔΔG°) is shown in Figure S1.
One perception of FEP is that it becomes more difficult to get good results if the perturbation is large and the authors note:
“We find that our methodology is robust up to perturbations of approximately 10 heavy atoms”
Counting atoms is not the only way to gauge the magnitude of a perturbations. It’d also be interested to see how robustly the methodology handles perturbations that involve changes in ionization state and whether ΔΔG°values of greater magnitude are more difficult to predict than those of smaller magnitude. Prediction of affinity for compounds that bind covalently, but reversibly, to targets like cysteine proteases would probably also be feasible using these methods. Something I've wondered about for a few years is what would happen if the aromatic nitrogen that frequently accepts a hydrogen bond from the tyrosine kinase hinge was mutated into an aromatic carbon. If the resulting loss of affinity for this structural transformation was as small as some seem to believe it ought to be then it would certainly open up some 'patent space' in what is currently a bit of a log jam. You can also see how FEP might be integrated with MMPA in a lead optimization setting by using the former to predict the effects of structural modifications on affinity and the latter to assess the likely impact of of these modifications on ADME characteristics like solubility, permeability and metabolic stability.
So lots of possibilities and this is probably a good place to leave it for now.