Field of Science

What would be a "non-intuitive" prediction in medicinal chemistry?

Over the last two decades when computer-aided drug design was in development, one of the most common refrains you heard from medicinal chemists about its utility was that it did a poor job predicting  “non-intuitive” structural modifications to molecules. But the term is not always easy to define, and while the charge is often valid, it’s also sometimes unfair since what’s non-intuitive can be highly subjective and constitute a moving target. Also, the bar for non-intuitive ideas can be rather high based on the experience of particular medicinal chemists; it’s not always fair to hold a simple computational method up to the same standards as a chemist with thirty years experience (that’s not exactly what the software has been designed for…).

Nonetheless, it’s rather refreshing to hear modelers level the same charge against themselves, which is perhaps a sign that the entire field is now seeking higher standards than before. I was pleased to hear this sentiment noted several times in the ACS meeting in Boston which I just attended. But the question still stands: What prediction could a modeler make that would be deemed ‘non-intuitive’ or 'novel' by a fairly experienced medicinal chemist? There are at least a few cases that come to mind:

Binding and solution conformation prediction: Chemists are used to looking at 2D structures, and even a highly experienced chemist won’t be able to predict most of the time how a complicated-looking molecule will bind in a protein binding pocket. That is what docking is for, and it's one of the few areas of modeling which can claim a modest but solid degree of success. What is still a non-trivial problem is to predict the ensemble of conformations in solution which converge to a single conformation in the protein pocket. I worked on this problem myself in grad school, and it took up the majority of my half-decade or so spent there. The problem is in both determining the population of solution conformations and estimating the binding energy going from multiple to one conformation, and the general solution is still tedious and complicated.

The prediction of conformational changes in general is a problem that cannot be easily visualized by medicinal chemists without some kind of computational or experimental (especially NMR) support. Subtle structural additions like methyl groups or halogens can sometimes cause significant changes in solution conformational populations, which in turn may impact the binding conformation. Generally speaking it is impossible to understand these effects using intuition alone. Intramolecular hydrogen bonds which can stabilize conformations and improve membrane permeability are also hard to visualize or predict without some kind of computational analysis, especially for larger molecules like macrocycles.

Scaffold hopping: Another attractive idea which is not obvious to medicinal chemists. Scaffold hopping involves essentially locating the binding pharmacophore for a molecule and then finding (ideally) a completely different set of bonds and connectivities that would map on to the same pharmacophore. It is especially useful for transforming ring systems to one another or constraining an acyclic system in a ring. One utility of scaffold hopping is to locate bioisosteres. Computational techniques can be very useful here in principle, although pharmacophore detection can be spotty because of problems with false positives and negatives. Scaffold hopping is not just non-intuitive but is also a boon to getting around intellectual property which is usually every chemist’s nemesis.

Calculating desolvation penalties: An experienced medicinal chemist may be able to look at a compound and make a guess about its size or lipophilicity, but guessing desolvation penalties is intuitively quite hard except in obvious cases (as in the case of a positively or negatively charged group – even then, guessing the sum of all the interactions is challenging). One of the reasons is that desolvation being a point charge-dipole interaction, its energy goes up as the square of the charge (instead of just inversely as in Coulomb's law): small changes in heteroatom distributions can thus have significant and non-obvious effects on solvation/desolvation. 

Unfortunately calculating solvation energies is also still hard in a general sense for computational chemists, but progress continues to be made. One of the most successful predictions of modeling would be a case where a highly charged group compensates for all its desolvation by making perfectly formed hydrogen bonds with a protein - this is a very hard thing to predict as of now. Generally speaking though, desolvation penalties would, at least in principle, fall into the category of things that medicinal chemists wouldn’t often be able to guess.

Calculating strain energies: This is another case where even experienced medicinal chemists may not be able to make intuitive statements. Sometimes it’s obvious in an x-ray crystal structure that ligands are strained (manifested for instance in the form of bent amides, bent phenyl rings or any kind of non-planar conjugated systems). But other times the effects of strain can be invisible to the naked eye. The problem is that bond length changes of as little as tenths of an angstrom can translate into significant strain energies of several kcals/mol, and it is hard if not impossible for even seasoned medicinal chemists to actually see these strain-inducing elements without some kind of calculation. That’s where modeling can help.

Water molecules: We know well by now how complicated the behavior of water molecules in protein binding sites can be. Sometimes the kinds of predictions that modelers make about easily and productively displaced water molecules are rather obvious, such as when they are talking about a water molecule in a nice hydrophobic cavity. The subtle cases are harder to intuitively predict. For instance crystallographic waters may be firmly bound and therefore may have good enthalpy but may still be unhappy and displaceable because of an unfavorable entropy. Similarly as detailed in the link above, water molecules at ligand-solvent interfaces may have unexpected thermodynamic features. Unhappy water prediction methods like WaterMap and SZMAP are promising, but only when they can predict non-intuitive scenarios that are refractory to easy analysis by medicinal chemists.

Data analysis: Generally speaking the word ‘non-intuitive’ may also mask more mundane but useful goals like being able to analyze large amounts of data and suggest useful trends. In fact that’s a task that’s usually quite unsuited to the skills of a medicinal chemist because of its reliance on numbers and statistics and opacity to easy structural visualization.

Feel free to note others in the comments section which I might have left out. Even better are cases where modelers can make suggestions that aren't just non-intuitive but counterintuitive. For instance, if you can predict that a methyl group filling a pocket would lead to a drop in potency (steric reasons? trapped water?) that would be a good counterintuitive prediction. Or if you could predict that cyclization of a molecule would actually increase conformational flexibility because of alleviation of syn-pentane interactions (as I found out in my comparison of cyclic dictyostatin with acyclic discodermolide), that prediction would also fall into the same category. Counterintuitive predictions also provide acid tests of any model because of their emphasis on falsifiability.

I don’t claim that all the goals listed above are well within the purview of molecular modeling. What I am claiming is that there are several challenging tasks on which modeling has started to make inroads. And a good number of these could be called “non-intuitive” or even "counterintuitive". A decade or two down the line I don't think such predictions from modeling will be as rare as we currently think they are, and that's something that we all should look forward to.


  1. @MedChem_DGBrown5:38 AM, August 21, 2015

    Very nicely written blog post! Great topic. I think you addressed many of the big ones. Induced fit pockets would probably be high on my list. We mention them a lot in the context of "maybe we can find an induced fit pocket..", but it's often the last-ditch effort to save a series Also, I like your mention of data analysis. I wonder how often we try to form an H-bond, but lose a log unit or more of potency and walk away from the idea. You might have actually made the new bond, it's just a bit too weak and also penalized by desolvation. A counterinuitive approach would be to pursue the compounds which lost activity, but you know that it should have lost much more activity unless something new has occured in the pharmacophore and needs to be optimized.

  2. Thanks. I think you make some great points about near-hits that are often ignored and which deserve further scrutiny.

  3. Re: desolvation energies.

    I think that the current state is that they are a total bunk. Almost random. To explain something I saw, I *really* wanted to have a good estimate of local hydrophobicity in the protein. This turned out to be more or less not possible. At some point I thought that there was a great approach - counting water density after MD simulations (J. Phys. Chem. B ,118 (6) ,1564-1573 (2014). Alas, its predictions fit experimental proteins roughly 50:50. I can achieve the same success rate simply by calculating area of solvent-exposed carbon and sulfur atoms! (E.g., too crude to be useful).


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