A new year and a new title for the blog which will now just be ‘Molecular Design’. I have a number of reasons for dropping fragment-based drug discovery (FBDD) from the title but first want to say a bit about molecular design because that may make those reasons clearer. Molecular design can be defined as control of the behavior of compounds and materials by manipulation of molecular properties. The use of the word ‘behavior’ in the definition is very deliberate because we design a compound or material to actually do something like bind to the active site of an enzyme or conduct electricity or absorb light of a particular wavelength. A few years ago, I noted that molecular design can be hypothesis-driven or prediction-driven. In making that observation, I was simply articulating something that many would have already been aware of rather than presenting radical new ideas. However, I was also making the point that it is important to articulate our assumptions in molecular design and be brutally honest about what we don’t know.
Hypothesis-driven molecular design (HDMD) can be thought of as a framework in which to establish what, in the interest of generality, I’ll call ‘structure-behavior relationships’ (SBRs) as efficiently as possible. When we use HDMD, we acknowledge that is not generally possible to predict the behavior of compounds directly from molecular structure in the absence of measurements for structurally related compounds. There is an analogy between HDMD and statistical molecular design (SMD) in that both can be seen as ways of obtaining the information required for making predictions even though the underlying philosophies may differ somewhat. The key challenge for both HDMD (and SMD) is identifying the molecular properties that will have the greatest influence on the behavior of compounds and this is challenging because you need to do it without measured data. An in depth understanding of molecular properties (e.g. conformations, ionization, tautomers, redox potential, metal complexation, uv/vis absorption) is important when doing HDMD because this enables you to pose informative hypotheses. In essence, HDMD is about asking good questions with informative compounds and relevant measurements and the key challenge is how to make the approach more systematic and objective. One key molecular property is something that I’ll call ‘interaction potential’ and this is important because the behavior of a compound is determined to a large extent by the interactions of its molecules with the environments (e.g. crystal lattice, buffered aqueous solution) in which they exist.
Since FBDD is being dropped from the blog title, I thought that I’d say a few words about where FBDD fits into the molecular design framework. I see FBDD as essentially a smart way to do structure-based design in that ligands are assembled from proven molecular recognition elements. The ability to characterize weak binding allows some design hypotheses to be tested without having to synthesize new compounds. It’s also worth remembering that the FBDD has its origins in computational chemistry ( MCSS | Ludi | HOOK ) and that an approach to crystallographic mapping of protein surfaces was published before the original SAR by NMR article made its appearance. My own involvement with FBDD began in 1997 and I focused on screening library design right from the start. The screening library design techniques described in blog posts here ( 1 | 2 | 3 | 4 | 5) and a related journal article have actually been around for almost 20 years although I think they still have some relevance to modern FBDD even if they are getting a bit dated. If you’re interested, you can find a version of the SMARTS-based filtering filter software that I was using even before Zeneca became AstraZeneca. It’s called SSProFilter and you can find source code (it was built with the OEChem toolkit) in the supplemental information for our recent article on alkane/water logP. So why drop FBDD from the blog title? For me, molecular design has always been bigger than fragment-based molecular design and my involvement in FBDD projects has been minimal in recent years. FBDD is increasingly becoming mainstream and, in any case, dropping FBDD from the blog title certainly doesn’t prevent me from discussing fragment-based topics or even indulging in some screening library design should the opportunities arise.
As some readers will be aware, I have occasionally criticized some of the ways that things get done in drug discovery and so it’s probably a good idea to say something about the directions in which I think pharmaceutical design needs to head. I wrote a short Perspective for the JCAMD 25th anniversary issue three years ago and this still broadly represents my view where the field should be going. Firstly we need to acknowledge the state of predictive medicinal chemistry and accept that we will continue to need some measured data for the foreseeable future. This means that, right now, we need to think more about how collect the most informative data as efficiently as possible and less about predicting pharmacokinetic profiles directly from molecular structure. Put another way, we need to think about drug discovery in a Design of Experiments framework. Secondly, we need to look at activity and properties in terms of relationships between structures because it’s often easier to predict differences in the values of a property than it is to predict the values themselves. Thirdly, we need to at least consider alternatives to octanol/water for partition coefficient measurement.
There are also directions in which I think we should not be going. Drug discovery scientists will be aware of an ever-expanding body of rules, guidelines and metrics (RGMs) that prompts analogy with The Second Law. Some of this can be traced to the success of the Rule of 5 and many have learned that is a lot easier to discover metrics than it is to discover drugs. If you question a rule, the standard response will be, “it’s not a rule, it’ a guideline” and your counter-response should be, “you’re the one who called it a rule”. Should you challenge the quantitative basis of a metric, which by definition is supposed to measure something, it is likely that you will be told how useful it is. This defense is easily outflanked by devising a slightly different metric and asking whether it would be more or less useful. Another pattern that many drug discovery scientists will have recognized is that, in the RGM business, simplicity trumps relevance.
Let’s talk about guidelines for a bit. Drug discovery guidelines need to be based on observations of reality and that usually means trends in data. If you’re using guidelines, then you need to know the strength of the trend(s) on which the guidelines are based because this tells you how rigidly you should adhere to the guidelines. Conversely, if you’re recommending that people use the guidelines that you’re touting then it’s very naughty to make the relevant trends appear to be stronger than they actually are. There are no winners when data gets cooked and I think this is a good point at which to conclude the post.
So thanks for reading and I’ll try whet your appetite by saying that the next blog post is going to be on PAINS. Happy new year!