Field of Science

Free Energy Perturbation (FEP) methods in drug discovery: Or, Waiting for Godot

For interested folks in the Boston area it's worth taking a look at this workshop on Free Energy Perturbation (FEP) methods in drug design at Vertex from May 19-21. The list of speakers and topics is quite impressive, and this is about as much of a state-of-the-art discussion on the topic as you can expect to find in the area.

If computational drug discovery were a series of plays, then FEP might well be the "Waiting for Godot" candidate among them. In fact I would say that FEP is a textbook case of an idea that, if it truly works, can truly transform the early stages of drug discovery. What medicinal chemist would not want to know the absolute free energy of binding of his molecules to a protein so that he can actually rank known and unknown compounds in order of priority? And what medicinal chemist would not want to know exactly what she should make next?

But that's what medicinal chemists have expected from modelers ever since modeling started to be applied realistically to drug discovery, and I think it's accurate to say that it's good they haven't held their breath. FEP methods have always looked very promising because they aim to be very rigorous, bringing the whole machinery of statistical mechanics to bear on a protein-ligand system. The basic goal is "simple": you calculate the individual  energies of the protein and the drug - in explicit water - and then you calculate the energy of the bound system. The difference is the free energy of binding. Problem solved.

Except, not really. Predicting relative free energies is still a major challenge, and predicting absolute free energies is asking for a lot. The major obstacle to the application of these methods for decades was considered to be the lack of enough computing power. But if you really thought that was the major obstacle then you were still a considerable way off. Even now there seems to be a belief that given enough computing power and simulation time we can accurately calculate the free energy of binding between a drug and a target. But that's assuming that the fundamental underlying methodology is accurate, which is a big assumption.

The "fundamental underlying methodology" in this case mainly refers to two factors: the quality of the force field which you use to calculate the energy of the various components and the sampling algorithm which you use to simulate their motions and exhaustively explore their conformations. The force fields can overemphasize electrostatic interactions and can neglect polarization, and the sampling algorithms can fail to overcome large energy barriers. Thus both these components are imperfectly known and applied in most cases, which means that no amount of simulation time or computing power is then going to be sufficient. It's a bit like the Polish army fighting the Wehrmacht in September 1939; simply having a very large number of horses or engaging them in the fight for enough time is not going to help you win against tanks and Stukas.

These problems have all been well recognized however; in fact the two most general issues in any modeling technique are sampling and energy calculation. So parts of this month's workshop are aimed exactly at dissecting the factors that can help us understand and improve sampling and scoring.

The end goal of any applied modeling technique of course is how good it is at prediction. Not surprisingly, progress on this front using FEP has been rather thin. In fact FEP is the quintessential example of a technique whose successes have been anecdotal. Even retrospective examples, while impressive, are not copious. One of the problems is that FEP works only when you are trying to predict the impact of very tiny changes in structure on ligand affinity; for instance the impact of changing a methyl group on a benzene ring to a hydroxyl group. The trouble is that the method doesn't work even for these minor changes across the board; there are projects where a CH3--->OH change will give you quantitative agreement with experiment and there are cases where it will result in error bars large enough to drive a car through them. 

But anecdotes, while not being data, are still quite valuable in telling us what may or may not work. Computing power may not solve all our problems but it has certainly given us the opportunity to examine a large number of cases and try to abstract general rules or best practices for drug discovery. We may not be able to claim consistent successes for FEP right now, but it would help quite a lot even if we know what kinds of systems it works best for. And that, to me, is as good an outcome as we could expect at this time.

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