HB Donor Fragment Selection Themes

[This post was updated on 11-Dec-2024 to reflect the publication of the 'HBDs in drug design' preprint as the K2022 article]

In this post I’ll look at a couple of fragment selection themes with a hydrogen bond donor (HBD) focus. The material has been taken from the recent ‘HBDs in drug design’ preprint (HBD3; this would subsequently be published as the K2022 article) which introduced the term ‘hydrogen bond donor-acceptor asymmetry’ and suggested that we need to think differently about HBDs and hydrogen bond acceptors (HBAs) in drug design. One example of these hydrogen bond donor-acceptor asymmetries is that HBAs are typically more strongly solvated than HBDs in aqueous media and this is especially relevant to lead optimization (as shown in the graphical abstract for HBD3 below). 

However, this post is about fragment selection, rather than fixing ADME, and so I’ll say something about differences between HBDs and HBAs in the context of binding to targets. Let’s suppose that you’d like to exploit an HBD in the binding site of your target. All you need to do is place an HBA at a point in space where it can form a good hydrogen bond (taking care to address issues like steric footprint and conformational energy) and you’ve got it sorted. However, life is not quite so simple if you’re trying to exploit an HBA in the binding site because the HBD (e.g., amide NH) that you present to it will almost invariably be accompanied by an HBA (e.g., amide carbonyl O). In contrast, it is relatively easy to design an HBA (e.g., pyridine N) into a ligand structure that is not accompanied by an HBD.   

In HBD3, I describe the HBA that accompanies pretty much every neutral HBD as ‘co-occurring’. The problem for designers is that the co-occurring HBA, which is likely to come with a larger desolvation penalty than that for the HBD, needs to be accommodated and this places constraints on design. It’s also more difficult to achieve ‘line-of-sight’ access with HBDs than is the case for HBAs (you’re likely to need line-of-sight access when targeting a polar atom at the bottom of a relatively narrow binding pocket). The following figure should give you a better idea of what I’m getting at and let’s assume that we’re trying to donate an HB to HBA sitting at the bottom of a narrow and otherwise non-polar binding pocket. Although each of the three structures has appropriate geometry for line-of-sight access, things are not likely to end well if you try to exploit this line-of-sight access in a real-life design situation.

Let’s start with the phenol and, although not pertinent to this discussion, it’s worth mentioning that hydroxyl groups are prone to conjugation in phase 2 metabolism (drugs get hydroxylated in phase 1 metabolism in order to facilitate clearance). Donation of an HB by a ligand hydroxyl to a target HBA also brings the hydroxyl oxygen (the co-occurring HBA) into proximity with the molecular surface of the target. This increases the likelihood of an energetic penalty resulting from desolvation of the phenolic oxygen. One subtle point is that donation of an HB by the phenolic hydroxyl increases the HB basicity of the oxygen which effectively increases the energetic cost of desolvating it.

The co-occuring HBA of the primary amide is an even bigger problem than for phenol because the high polarity of the carbonyl oxygen means that it carries a large desolvation penalty (bad news if you’re trying to hit an HBA at the bottom of a narrow and otherwise non-polar binding pocket). If this is not enough of a problem, you also need to worry about desolvation penalties associated with the second HBD (the primary amide has two HBDs and methyl-capping will take out the one that you need for hitting that HBA at the bottom of the binding pocket). As Lady Bracknell might have observed, “One desolvated polar atom may be regarded as a misfortune; to lose solvation of two polar atoms looks like carelessness”.

The last of the trio of structures is pyrazole linked at C4 and this avoids problems that might result from biasing the tautomeric preference. Pyrazole is a great warhead if you’re targeting a proximal HBD and HBA (as is the case when trying to hit a kinase hinge). However, pyrazole’s HBA may become a liability when trying to hit the HBA at the bottom of that otherwise non-polar binding pocket. Why not just take out pyrazole’s HBA, you might ask? The problem is that pyrroles are very electron rich and tend to be quite reactive.  One tactic is to move the co-occurring HBA from the ring to the linker (1 and 2) in a way that makes the linker electron-withdrawing and pray for a less destabilizing contact between the co-occurring HBA and the binding site. Alternatively, you can take out the co-occurring HBA and modify the linker to make it more electron-withdrawing (3). I’ve included Hammett σ values in the graphic and these will give you an idea how the substituents vary in their ability to suck electron density out of the pyrrole ring (beneficial both for making the pyrrole ring more rugged and increasing the HB acidity of its NH HBD).  I see these fragments as being of about the right size to be screened crystallographically but you might want something a bit larger than methyl if you’re using another detection method.

If you’re designing (or trying to improve the coverage of) a fragment library then another selection theme that you might want to think about is fragments that can present a high ‘density’ of HBDs to a target while minimizing the number of co-occurring HBAs. One way to do this is to use the guanidine substructure although this will cause some medicinal chemists to roll their eyes (concerns about permeability) while Ro3’s adherents would be likely to denounce you for heresy (actually not such a bad thing and I think that the late, great Denis Healey might have likened this to “being savaged by a dead sheep”). Guanidine itself is extremely basic (pKa = 13.6 | ref) which means very little of the neutral form for diffusing across membranes. However, the pKa of guanidine is also extremely sensitive to substitution and a number of approved drugs incorporate this substructure. I should also point out that, even in the neutral form of guanidine, the amide-like nitrogen atoms do not function as HBAs (even though they’d be counted as such when applying Ro5).

I’ve made a small selection of substituted guanidines that I think may of interest for screening as fragments. The pKa values that I quote in this post are from an article by two former colleagues (Peter Taylor and Alan Wait who are sadly both deceased) and this is an excellent source of measured guanidine pKa values.   Two of these (4 and 5) will be predominantly protonated at neutral pH although there’ll still be a significant amount of the neutral form that you’ll need for permeability. The other two guanidines will be predominantly neutral at neutral pH although 6 is sufficiently basic to protonate in lysosomes.  As for the pyrroles, I see these as about the right size to be screened crystallographically but you might want something a bit bigger than methyl if you plan to use a different detection method.

  

Sailor Malan's guide to fragment screening library design

Today I'll take a look at a JMC Perspective on design principles for fragment libraries that is intended to provide advice for academics. When selecting compounds to be assayed the general process typically consists of two steps. First, you identify regions of chemical space that you hope will be relevant and then you sample these regions. This applies whether you're designing a fragment library, performing a virtual screen or selecting analogs of active compounds with which to develop structure-activity relationships (SAR). Design of compound libraries for fragment screening has actually been discussed extensively in the literature and the following selection of articles, some of which are devoted to the topic, may be useful: Fejzo (1999), Baurin (2004), Mercier (2005), Schuffenhauer (2005), Albert (2007) Blomberg (2009), Chen (2009), Law (2009), Lau (2011), Schulz (2011); Morley (2013). This series of blog posts ( 1 | 2 | 3 | 4) on fragment screening library design that may also be helpful.

The Perspective opens with the following quote:

"Rules are for the obedience of fools and the guidance of wise men"

Harry Day, Royal Air Force (1898-1977)

It wasn't exactly clear what the authors are getting at here since there appears to be no provision for wise women. Also it is not clear how the authors would view rules that required darker complexioned individuals to sit at the backs of buses (or that swarthy economists should not solve differential equations on planes). That said, the quote hands me a legitimate excuse to link Malan's Ten Rules for Air Fighting and I will demonstrate that the authors of this Perspective can learn much from the wise teachings of 'Sailor' Malan.

My first criticism of this Perspective is that the authors devote an inordinate amount of space to topics that are irrelevant from the viewpoint of selecting compounds for fragment screening. Whatever your views on the value of ligand efficiency metrics and thermodynamic signatures, these are things that you think about once you've got the screening results. The authors assert, "As a result, fragment hits form high-quality interactions with the target, usually a protein, despite being weak in potency" and some readers might consider the 'concept' of high-quality interactions to be pseudoscientific psychobabble on par with homeopathy, chemical-free food and the wrong type of snow. That said, discussion of some of these peripheral topics would have been more acceptable if the authors had articulated the library design problem clearly and discussed the most relevant literature early on. By straying from their stated objective, the authors have broken the second of Malan's rules ("Whilst shooting think of nothing else, brace the whole of your body: have both hands on the stick: concentrate on your ring sight").

The section on design principles for fragment libraries opens with a slightly gushing account of the Rule of 3 (Ro3). This is unfortunate because this would have been the best place for the authors to define the fragment library design problem and review the extensive literature on the subject. Ro3 was originally stated in a short communication and the analysis that forms its basis is not shared. As an aside, you need to be wary of rules like these because the cutoffs and thresholds may have been imposed arbitrarily by those analyzing the data. For example, the GSK 4/400 rule actually reflects the scheme used to categorize continuous data and it could just have easily been the GSK 3.75/412 rule if the data had been pre-processed differently. I have written a couple ( 1 | 2 ) of blog posts on Ro3 but I'll comment here so as to keep this post as self-contained as possible. In my view, Ro3 is a crude attempt to appeal to the herding instinct of drug discovery scientists by milking a sacred cow (Ro5). The uncertainties in hydrogen bond acceptor definitions and logP prediction algorithms mean that nobody knows exactly how others have applied Ro3. It also is somewhat ironic that the first article referenced by this Perspective actually states Ro3 incorrectly. If we assume that Ro5 hydrogen bond acceptor definitions are being used then Ro3 would appear to be an excellent way to ensure that potentially interesting acidic species such as tetrazoles and acylsulfonamides are excluded from fragment screening libraries. While this might not be too much of an issue if identification of adenine mimics is your principal raison d'etre, some researchers may wish to take a broader view of the scope of FBDD. It is even possible that rigid adherence to Ro3 may have led to the fragment starting points for this project being discovered in Gothenburg rather than Cambridge. Although it is difficult to make an objective assessment of the impact of Ro3 on industrial FBDD, its publication did prove to be manna from heaven for vendors of compounds who could now flog milligram quantities of samples that had previously been gathering dust in stock rooms.



This is a good point to see what 'Sailor' Malan might have made of this article. While dropping Ro3 propaganda leaflets, you broke rule 7 (Never fly straight and level for more than 30 seconds in the combat area) and provided an easy opportunity for an opponent to validate rule 10 (Go in quickly - Punch hard - Get out). Faster than you can say "thought leader" you've been bounced by an Me 109 flying out of the sun. A short, accurate (and ligand-efficient) burst leaves you pondering the lipophilicity of the mixture of glycol and oil that now obscures your windscreen. The good news is that you have been bettered by a top ace whose h index is quite a bit higher than yours. The bad news is that your cockpit canopy is stuck. "Spring chicken to shitehawk in one easy lesson."

Of course, there's a lot more to fragment screening library design than counting hydrogen bonding groups and setting cutoffs for molecular weight and predicted logP. Molecular complexity is one of the most important considerations when selecting compounds (fragments or otherwise) and anybody even contemplating compound library design needs to understand the model introduced by Hann and colleagues. This molecular complexity model is conceptually very important but it is not really a practical tool for selecting compounds. However, there are other ways to define molecular complexity in ways that allow the general concept to be distilled into usable compound selection criteria. For example, I've used restriction of extent of substitution (as detailed in this article) to control complexity and this can be achieved using SMARTS notation to impose substructural requirements. The thinking here is actually very close to the philosophy behind 'needle screening' which was first described in 2000 by researchers at Roche although they didn't actually use the term 'molecular complexity'.


As one would expect, the purging of unwholesome compounds such as PAINS is discussed. The PAINS field suffers from ambiguity, extrapolation and convolution of fact with opinion. This series ( 1 | 2 | 3 | 4) of blog posts will give you a better idea of my concerns. I say "ambiguity" because it's really difficult to know whether the basis for labeling a compound as a PAIN (or should that be a PAINS) is experimental observation, model-based prediction or opinion. I say "extrapolation" because the original PAINS study equates PAIN with frequent-hitter behavior in a panel of six AlphaScreen assays and this is extrapolated to pan-assay (which many would take to mean different types of assays) interference. There also seems to be a tendency to extrapolate the frequent-hitter behavior in the AlphaScreen panel to reactivity with protein although I am not aware that any of the compounds identified as PAINS in the original study were shown to react with any of the proteins in the AlphaScreen panel used in that study. This is a good point to include a graphic to break the text up a bit and, given an underlying theme of this post, I'll use this picture of a diving Stuka.

One view of the fragment screening mission is that we are trying to present diverse molecular recognition elements to targets of interest. In the context of screening library design, we tend to think of molecular recognition in terms of pharmacophores, shapes and scaffolds. Although you do need to keep lipophilicity and molecular size under tight control, the case can be made for including compounds that would usually be considered to be beyond norms of molecular good taste. In a fragment screening situation I would typically want to be in a position to present molecular recognition elements like naphthalene, biphenyl, adamantane and (especially after my time at CSIRO) cubane to target proteins. Keeping an eye on both molecular complexity and aqueous solubility, I'd select compounds with a single (probably cationic) substituent and I'd not let rules get in the way of molecular recognition criteria. In some ways compound selections like those above can be seen as compliance with Rule 8 (When diving to attack always leave a proportion of your formation above to act as top guard). However, I need to say something about sampling chemical space in order to make that connection a bit clearer.

This is a good point for another graphic and it's fair to say that the Stuka and the B-52 differed somewhat in their approaches to target engagement. The B-52 below is not in the best state of repair and, given that I took the photo in Hanoi, this is perhaps not totally surprising. The key to library design is coverage and former bombardier Joseph Heller makes an insightful comment on this topic. One wonders what First Lieutenant Minderbinder would have made of the licensing deals and mergers that make the pharma/biotech industry such an exciting place to work.  

The following graphic, pulled from an old post, illustrates coverage (and diversity) from the perspective of somebody designing a screening library.  Although I've shown the compounds in a 2 dimensional space, sampling is often done using molecular similarity which we can think of inversely related to distance. A high degree of molecular similarity between two compounds indicates that their molecular structures are nearby in chemical space.  This is a distance-geometric view of chemical space in which we know the relative positions of molecular structures but not where they are.  When we describe a selection of molecular structures as diverse, we're saying that the two most similar ones are relatively distant from each other. The primary objective of screening library design is to cover relevant chemical space as effectively as possible and devil is in the details like 'relevant' and 'effectively'. The stars in the graphic below show molecular structures that have been selected to cover the chemical space shown. When representing a number of molecular structures by a single molecular structure it is important, as it is in politics, that what is representative not be too distant from what is being represented. You might ask, "how far is acceptable?" and my response would be, as it often is in Brazil, "boa pergunta". One problem is scaffolds differ in their 'contributions' to molecular similarity and activity cliffs usually provide a welcome antidote to the hubris of the library designer.         

I would argue that property distributions are more important than cutoff values for properties and it is during the sampling phase of library design that these distributions are shaped. One way of controlling distributions is to first define regions of chemical space using progressively less restrictive selection criteria and then sample these in order, starting with the most restrictively defined region. However, this is not the only way to sample and might also try to weight fragment selection using desirability functions. Obviously, I'm not going to provide a comprehensive review of chemical space sampling in a couple of paragraphs of a blog post but I hope to have shown that the sampling of chemical space is an important aspect of fragment screening library design. I also hope to have shown that failing to address the issue of sampling relevant chemical space represents a serious deficiency of the featured Perspective. 

The Perspective concludes with a number of recommendations and I'll conclude the post with comments on some of these. I wouldn't have too much of a problem with the proposed 9 - 16 heavy atom range as a guideline although I would consider a requirement that predicted octanol/water logP be in the range 0.0 - 2.0 to be overly restrictive. It would have been useful for the authors to say how they arrived at these figures and I invite all of them to think very carefully about exactly what they mean by "cLogP" and "freely rotatable bonds" so we don't have a repeat of the Ro3 farce. There are many devils in the details of the statement:"avoid compounds/functional groups known to be associated with high reactivity, aggregation in solution, or false positives". My response to "known" is that it is not always easy to distinguish knowledge from opinion and "associated" (like correlated) is not a simple yes/no thing. It is not cleat how "synthetically accessible vectors for fragment growth" should be defined since there is also a conformational stability issue if bonds to hydrogen are regarded as growth vectors.   

This is a good point at which to wrap things up and I'd like to share some more of Sailor Malan's wisdom before I go. The first rule (Wait until you see the whites of his eyes. Fire short bursts of 1 to 2 seconds and only when your sights are definitely 'ON') is my personal favorite and it provides excellent, practical advice for anybody reviewing the scientific literature. I'll leave you with a short video in which a pre-Jackal Edward Fox displays marksmanship and escaping skills that would have served him well in the later movie. At the start of the video, the chemists and biologists have been bickering (of course, this never really happens in real life) and the VP for biophysics is trying to get them to toe the line. Then one of the biologists asks the VP for biophysics if they can do some phenotypic screening and you'll need to watch the video to see what happens next...

Unknown knowns and binding kinetics

I hope that you all enjoyed the April Fool post but now it’s time to get back to business.  Before returning to Thermodynamics, I’d like to point you towards a couple of articles that you might find to be of interest.  In the first of these, Ben Davis and Practical Fragments’ Dan Erlanson take a look at some of what can go wrong when you screen fragments and it is noteworthy that they actively sought material from the fragment community before writing the review.  If you’re thinking about running a fragment screen, you do need to read this article which has already been featured in other blog posts ( 1 | 2 | 3).    

The article by Göran Dahl and Tomas Åkerud focuses on binding kinetics, specifically in the context of pharmacokinetics. In multistep processes, timescale determines relevance.  My take is that slow binding is equivalent to slow distribution and, at some point when the topic is raised, I will usually get round to asking whether slow distribution is something that you would want to design into a drug. The authors conclude that ‘the drug-target residence time appears to be limited in scope for prolonging duration of effect’ and the article provides a timely reminder that drugs act within a pharmacokinetic framework.

Literature cited

Davis & Erlanson (2013) Learning from our mistakes: The ‘unknown knowns’ in fragment screening. Bioorg Med Chem Lett In press DOI

Dahl & Åkerud (2013) Pharmacokinetics and the drug-target residence time concept. Drug Discov Today In press DOI

Screening library size

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This post got prompted by a survey of screening library size by Teddy at Practical Fragments. I commented briefly there but it soon became clear that it was going to take a really long comment to say everthing. While at AstraZeneca, I was involved in putting together a 20k screening library for fragment-based work and I thought that some people might be interested in reading about some of the experiences. The project started at the beginning of 2005 although the library (GFSL2005) was not assembled until the folllowing year. Some aspects of the library design were also described in our contribution to the JCAMD special issue on FBDD. The NMR screening libraries with which I've been involved have been a lot smaller (~2k) than this so why would one want to assemble a fragment screening library of this size? One reason was diversity of fragment-based activities, which included a high concentration component when running HTS In screening, you soon learn the importance of logistics and, in particular, compound managment. If you're going to assemble a screening library that can be easily used, this usually means getting the compounds into dimethyl sulfoxide (DMSO) so that samples can be dispensed automatically. Fragments will usually be screened at high concentration which means that stock solutions also need to be more concentrated (we used 100µM) because most assays don't particularly like DMSO. Another reason for maintaining stock solutions is that there are minimum volume limits for dissolving solids in DMSO so starting from solids each time you run a screen is wasteful of material as well as time. The essence of the GFSL2005 project was ensuring that there were enough high concentration stock solutions in the liquid store to support diverse fragment-based activities on different Discovery sites. While the entire library might be screened as a component of HTS, individual groups could screen subsets of GFSL05 that were significantly smaller (at least when I left AZ in 2009). It's important to rememember that while GFSL05 was a generic library we were also trying to anticipate the needs of people who might be doing directed fragment screening. The compounds were selected using Core and Layer (which I've discussed before). This approach is not specific to fragment screening libraries and I've used it to put together a compound library for phenotypic screening. The in house software, some of which had been developed in response to emergence of HTS, was actually in place by the beginning of 1996. Here (from one of my presentations) is a slide that illustrates the approach.
 

One of the comments on the Practical Fragments post was about molecular diversity and the anonymous commentator asked: "Are some companies more keen because of their bigger libraries or just lazy and adding more and more compounds without looking at diversity?" This is a good challenge and I'll try to give you an idea of what were trying to do. Core and Layer is all about diversity in that the approach aims to drive compound selection away (in terms of molecular similarity) from what has already been selected. However, when designing the library we did try to ensure that as many compounds as possible had a near neighbour (within the library). Here's a slide showing (in pie chart format) the fractions of the library corresponding to each number of neighbours. There are actually three pie charts because counting neighbours depends on the similarity threshold that you use and I've done the analysis for three different thresholds.
 

 You can do a similar analysis to ask about neighbours of library compounds that are available. This is of interest because you'll generally want to be able to follow up hits with analogues in a process that some call 'SAR-by-Catalogue' although I regard the term as silly and refuse to use it. Availability is not constant and the analysis shown in the following slide was a snapshot generated for a 2008. You'll notice that there is an extra row of pie charts since one can define availability more (> 20mg) or less (>10 mg) conservatively. If you require plenty of sample for availability and require a neighbours to be very similar then you'll have less neighbours.
 

 There was also some commentary on solubility and ideally this should be measured in assay bufffer for library compounds. We used of our in house high throughput solubility assay when putting GFSL2005 together. This solubility assay had original been designed for assessing hits from conventional HTS and had a limited dynamic range (1-100 µM) which was not ideal for fragments. The assay used standard (i.e. 10mM) stock solutions which meant that we could only do measurements for samples that were in this format (library compounds acquired from external sources were only made up as 100mM stocks). Nevertheless, we used the assay since we believed that the information would still be valuable. The graphic below illustrates the relatonship between solubility and lipophilicity (ClogP) for compounds that are neutral under assay conditions. We used ClogP to bin the data which allowed us to make use of out of range data by plotting percentiles. This allowed us to assess the risk of poor solubily as a function of ClogP. It's worth pointing out that the binning was done so as to be able to include in range and out of range data in a single analysis. We just showed the lowest percentiles because we were only interested in the least soluble compounds. Even so, you should be able to get an idea of the variation in solubility for each bin.
 

Staying with the solubility theme I'll finish off taking a look at with a look at the aromatic rings and carbon saturation as determinants of aqueous solubility. The two articles at which I'll be looking were both featured in a post at Practical Fragments and, given that the analyses in the two articles were not exactly rose-scented, the title of the post struck me as somewhat ironic. I'll start with Escape from Flatland which introduces carbon bond saturation, defined by fraction sp³ (Fsp³) as a molecular descriptor. Figure 5 is the one most relevant to the discussion and this is captioned, "Fsp³ as a function of logS..." although the caption is not totally accurate. LogS is binned and it is actually average Fsp³ that is plotted and not Fsp³ itself. I'm guessing that the average is the mean (rather than the median) Fsp³ for each logS bin. If you look at the plot there appears to be a good correlation between the mid-point of each logS bin and average Fsp³ although it would have been helpful if they'd presented a correlation coefficient and shown a standard deviation for each bin. In fact, it would have been helpful if they'd shown some standard deviations for the other figures as well but that is peripheral to this discussion. The problem with Figure 5 is that the intra-bin variation in Fsp³ is hidden (i.e. no error bars) and without being able to see this variation it is very difficult to know how strongly Fsp³ (as opposed to its average value) is correlated with logS. Those readers who were awake (it was immediately after lunch) at my RACI talk last December will know what I'm talking about but hopefully some of the rest of you will at least be wondering why the authors didn't simply fit logS to Fsp³. Anyone care to speculate as to what the correlation coefficient between logS and Fsp³ might be?

Impact of aromatic ring count presents a box plot of solubility as a function of number of aromatic rings. At least the variation in solubility is shown for each value of aromatic ring count is shown (even though I'd have preferred to see log of solubility plotted). The authors also looked at the correlation between number of aromatic rings cLogP (which I prefer to call ClogP) and judged it to be excellent (although it is not clear whether they bothered to calculate a correlation coefficient to support their assertion of excellence). Correlations between descriptors are important because the effect of one can be largely due to the extent to which is correlated with another. Although there are ways that you can model the dependence of a quantity on two descriptors that are correlated with each other, the authors chose to do this graphically using the pie chart array in Figure 6. If you look at the pie chart array, you can sort of convince yourself that aromatic ring count has an effect on solubility that is not just due to its effect on ClogP. However, there is established statistical methodology for dealing with this sort or problem. I couldn't help wondering why the authors didn't use this to analyse their data. What puzzled me even more was that they didn't seem to have considered the possiblity that the number of aromatic rings might be correlated with molecular weight (Dan also picked up on this in his post) since I'd guess that this correlation might be even stronger than the one with ClogP. I do believe that a strong case can be made for looking beyond aromatic rings when putting screening libraries and I'll point you to a recent intiative that may be of interest. However, this case is based on considerations of molecular recognition and molecular diversity. I don't believe that either of these studies (which both imply that substituting cyclohexadiene for benzene would be a good thing) strengthens that case. Possibly if the analysis had been done differently I might have arrived at a different conclusion but a lack of error bars and a failure to acccount for the effect of molecular weight leave me with too many doubts.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD 2009, 23 513-525 DOI

Colclough et al, High throughput solubility determination with application to selection of compounds for fragment screening. Bioorg. Med. Chem. 2008, 16, 6611-6616 DOI

Lovering, Bikker & Humblet, Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success. J. Med. Chem. 2009, 52, 6752-6756 DOI

Ritchie & MacDonald, The impact of aromatic ring count on compound developability: Are too many aromatic rings a liability in drug design? Drug Discov. Today 2009, 14, 1011-1020 DOI

Rule of 3 takes some flak

You might remember my rantlet about the Rule of Three (Ro3) at the beginning of the year. Well it seems that others consider Ro3 to be over-restrictive and this JMC article will be as welcome in some parts of Cambridge as a staffel of Ju87 Stukas would have been back in 1940.

The authors of this work describe screening of a library of 364 fragments against the aspartyl protease endothiapepsin and crystal structures of 11 hits bound to the target protein. The library was designed “without strictly applying the rule of 3” and, as it turns out, “only 4 of the 11 fragments are consistent with the rule of 3”. Not exactly a ringing endorsement for Ro3 or a compelling incentive to buy a Ro3-compliant fragment library.

Hopefully one point that you’ll have taken away from my earlier post is that those who gave us Ro3 don’t say a whole lot about how they define hydrogen bond donors and acceptors so it can be difficult to say whether some fragments are Ro3-complaint or not. I’m guessing that Ro3’s proposers may not be be using the same definitions that are used to apply the Rule of 5 (Ro5). However, I really don’t know and don’t really care.

Now you can see the problem. Do any of the 7 fragment hits (I refuse to call them frits since that term is more usually associated with ceramics than Drug Discovery and an association with ceramics is something that you’ll want to avoid for fragments) from the endothiapepsin screen that are reported to be inconsistent with Ro3 actually fail to comply with the rule? Let’s take a look at the structures of two fragments for which binding to target was observed crystallographically. You’ll notice that I’ve retained the structure numbering from the article.

There are two nitrogens and three oxygens in 041 so with Ro5 hydrogen bonding definitions this fragment would not be Ro3-compliant. Personally, I wouldn’t count the amide nitrogen as an acceptor but that’s my choice. The cyclic ether oxygens in 041 will be very weak hydrogen bond acceptors and even three or four of these together will pack less of a punch than the typical amide carbonyl oxygen. One can make similar observations about 291 and I’d actually be more worried about the reactivity of the aniline portion of this molecule although this is not the place for that discussion. Fragment 255 has 3 nitrogens and one oxygen which translates into 4 hydrogen bond acceptors if you use Ro5 definitions. However, I would not count either the amide nitrogen or the bridging nitrogen of the fused heteroaromatic ring as acceptors so with my definitions the fragment would be Ro3-compliant.

The assay and crystallisation were carried out at a pH of 4.6. This means that the heteroaromatic nitrogens of 255 and 291 are likely to be protonated to a significant extent under experimental conditions. It’s interesting that the solubility measurements were run at a pH of 7.4 because basic fragments such as 041, 255 and 291 should be even more soluble in assay and crystallisation buffers. It would be a different story for acids but I didn’t see any of those so I guess no harm done.

It’s good to see the output of a fragment screen being published in this manner and the crystal structures for a number of fragments bound to this target represent a welcome addition to the protein knowledge base. Given that I’ve never been a fan of Ro3, I do like to see others questioning the rule although reading this paper gives the impression that Ro3 has never before been questioned. I also believe that they could have addressed the issue of hydrogen bonding definitions rather than simply jumping to the conclusion that Ro5 definitions (all nitrogens and oxygens are acceptors) were being used by those who gave us Ro3.

On a final note, you might wonder why I keep banging on about Ro3 when it’s something that I’ve never used to select fragments. It’s a good question and this is a good place to answer it. My own view is that the way many researchers have blindly adopted the rule is merely a symptom of a much bigger malaise in Drug Discovery research. Pharma appears to be dans la merde but the response of its leaders is typically to increase the frequency with which the Titanic’s complement of deck chairs is shuffled. Is this really a good time for those who represent the best chance of a future for the industry to be switching off their critical thinking skills?

Literature cited

Köster et al, A Small Nonrule of 3 Compatible Fragment Library Provides High Hit Rate of Endothiapepsin Crystal Structures with Various Fragment Chemotypes. J. Med. Chem. 2011, in press. DOI

FBLD versus DOS

The relative merits of Fragment Based Ligand Design (FBLD) and Diversity-Oriented Synthesis (DOS) were recently debated in a Nature Forum. This debate has already been reviewed both in Practical Fragments and In The Pipeline.

I believe by setting up the debate like this, the editorial staff of the journal show a poor understanding of Lead Discovery (LD). In essence a comparison is being made between apples and oranges. FBLD (also known as FBLG with the LG for lead generation) is an integrated LD framework and a comparison with conventional high throughput screening (HTS) and associated Hit-to-Lead (H2L) chemistry would have made more sense. DOS is essentially an approach to extending the chemical space covered by screening collections and filling ‘holes’ in the existing chemical space. A DOS approach could be easily used to enhance existing fragment libraries (especially if using molecular shape to quantify similarity) while the output of a fragment screen could be used as input to design of DOS libraries. The ‘Core and Layer’ approach that I’ve used in design of generic fragment libraries (and even one library for cell screening) can accurately be described as diversity-oriented.

The case for FBLD is made by Philip Hajduk who makes the important points that a relatively small number of fragments can be used to cover a relatively large chemical space and that synthetic resource is always directed towards the target of interest. I like to say that leads from FBLD are assembled from proven molecular recognition elements and would add that fragments allow you to search chemical space with a better-controlled resolution than do more elaborated molecules. I don’t happen to agree with his assertion that “there is ample evidence that larger molecules are more likely than smaller ones to succeed as drugs in clinical trials” but this does not weaken the first two points that he makes. It's worth remembering that you usually need protein crystal structures in FBLD both for the target (at the outset of screening) and for complexes with weakly-bound fragments. If you don't obtain these quickly you're going to be working on Project Passchendaele.

DOS is championed by Warren Galloway and David Spring. They note that there are situations in which FBLD is not currently applicable, for example in phenotypic screens (see Derek Lowe's comments In The Pipeline) or for probing certain protein-protein interactions. I agree with this point and believe that we’ll always need a variety of assays for successful LD, especially as Drug Discovery is expected to get even more challenging in the future. If you’re trying to enhance the ability of screening libraries to hit targets then it makes sense to use molecular diversity criteria to extend coverage in a more systematic manner. I don’t see why the term DOS should only apply when molecular size exceeds an arbitrary cut off and believe the real issue is more about how than whether DOS should be used to enhance screening libraries.

The advocates of DOS need to take a close look at how they define diversity. If the conserved core of a DOS library cannot be accommodated in a binding site then, barring nuclear fusion, none of the compounds in the library will fit either. From the point of view of this target the library has no diversity regardless of the number of compounds in it.

I was disappointed that molecular complexity (check this link for an alternative view) was not raised by either party in this debate since it’s a unifying concept that brings together different strategies for compound library design. Very complex molecules leave the H2L chemists with little or no room to manoeuvre. This is less of a problem if the screening hit nails the target with nanomolar potency and has jaw-dropping bioavailability. However, reality is more likely to be micromolar with one or more ADMET issues needing to be addressed. Advocates of DOS really do need to start thinking a bit more about molecular complexity in the context of screening compounds for biological activity. I always encourage folk designing a DOS library to make a relatively large sample of the library prototype so that it can be included in the fragment screening collection.

So what’s the verdict? I believe that FBLG is here to stay although it is not yet clear how widely applicable the approach is. I also believe that some form of DOS can be used to enhance any screening collection provided that:

(1) Diversity is seen in the context of the existing collection
(2) The importance of hit exploitability is recognised

I’d be interested to hear what other people think about this topic so feel free to comment. I’ll also set this up as a discussion for the FBDD LinkedIn group since commenting there is a bit easier. Also don’t forget that the journal allows you to comment on the article directly.


Literature cited

Hajduk, Galloway & Spring, A question of library design (Forum Drug Discovery). Nature 2011, 470, 42-43 | DOI

Nicholls et al, Molecular Shape and Medicinal Chemistry: A Perspective. J. Med Chem. 2010, 53, 3862-3886 | DOI

Hann, Leach & Harper, Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. J. Chem. Inf. Comput. Sci., 2001, 41, 856–864 | DOI

Rule of Three considered harmful?

I should start this post by saying that I’ve never actually used the Rule of Three for fragment selection. Part of the reason for this is simply a matter of timing since I’d been designing fragment libraries before the Rule of Three came along. However, I believe that there are reasons that you need to take a very close look at the Rule of Three if you’re planning to build a fragment library strategy around it. The rule was introduced in late 2003:

“We carried out an analysis of a diverse set of fragment hits that were identified against a range of targets. The study indicated that such hits seem to obey, on average, a ‘Rule of Three’, in which molecular weight is < 300, the number of hydrogen bond donors is ≤3, the number of hydrogen bond acceptors is ≤3 and ClogP is ≤3. In addition, the results suggested NROT (≤3) and PSA (≤60) might also be useful criteria for fragment selection. These data imply that a ‘Rule of Three’ could be useful when constructing fragment libraries for efficient lead discovery.”

My first criticism of the Rule of Three is that the authors do not say how they define hydrogen bond acceptors. I’ll illustrate this point with reference to the phenylhydantoin below which along with the accompanying properties was retrieved from eMolecules. As far as I’m concerned, this compound would have been perfectly acceptable for inclusion in a fragment library before the Rule of Three was published and the publication of the rule would not make change my mind. If, however, you asked me whether the compound complied with the Rule of Three, I’d have to admit that I simply don’t know. The number of hydrogen bond donors is not an issue because there is only one of these in the molecule. The number of acceptors is more problematic. I would only count the oxygen atoms in this molecule as acceptors and, since there are two of these, the molecule would be compliant with the Rule of Three. However the well-known Rule of Five treats all nitrogen and oxygen atoms as acceptors so if you use those criteria you’ll count a total of four acceptors and conclude that the compound is not compliant with the Rule of Three. This is not a problem for me because I don't use the Rule of Three but spare a thought for the person assembling a commercial fragment library.

My second criticism of the Rule of Three concerns how it was actually derived. The authors describe performing “an analysis of a diverse set of fragment hits” without actually saying anything about what this analysis entailed. If they were analysing hits from their own fragment screens then the characteristics of the hits will reflect the criteria by which compounds were selected for fragment screening. If they were sampling from a more extensive database of screening hits, I’d still want to know how the fragment hits were distinguished from the other hits.

My third criticism is as much about how cut offs get used as it is of the Rule of Three. There’s a diagram of a funnel that you often see in virtual screening reviews. We also use funnels (or filters as we prefer to call them) in screening library design and in fact this activity is not a whole lot different from working up a virtual screen. Typically we apply filters and sample (e.g. using molecular diversity criteria) from what makes it through. Note that I say ‘filters’ rather than ‘a filter’. The Core and Layer (CaL) approach to library design has been described both in this blog and in a journal article. In CaL the filters used prioritise compounds get less restrictive as more compounds are added to the library. The reason for doing this is that it gives better control of chemical space coverage since it forces the selection of the smallest and least complex molecules first. A molecular diversity maximiser such as BigPicker, will tend to pick larger, more complex molecules because these tend to be more dissimilar to each other.

I am also prepared to accept compounds that have measured/calculated logP values in excess of 3 provided that the appropriate precautions (select ionisable compounds and/or use measured solubility values) have been taken to minimise the risk of poor solubility. You don’t want a whole library of compounds with logP values in excess of 4 but having some will increase the range of targets that you can nail. I am more concerned about the distribution of logP and molecular size in a library than I am with their maximum values and believe using multiple cut offs allows better control of these distributions.

You'll find plenty of material on the internet that deals with the Rule of Three although inconsistencies can be observed. It is not clear whether or not the Rule of Three includes the restrictions on NROT and PSA. As I read it in the original article, I don't think it does but I'm not sure and think it could have been made clearer. This webpage (accessed 11-Jan-2011) appears to suggest that Maybridge FBDD team think that the NROT and PSA criteria are included in the Rule of Three. However, another webpage (accessed 11-Jan-2011) seems to suggest that the FBDD team at Chembridge think otherwise. Cambridge Medchem Consulting (accessed 11-Jan-2011; I expect that this page will get updated once the error is discovered) appear to share the Chembridge view that the NROT and PSA criteria are not included in the Rule of Three although they use < instead of ≤ when stating the Rule which makes a big difference when the number in question is 3. Yet another variation on the Rule of Three can be found in the BioScreening.net glossary (accessed 12-Jan-2011) in which the hydrogen bond criteria are stated as "number of H-bond donors and acceptors less than, or equal to 3", which could be taken to imply that the sum of donors and acceptors cannot exceed 3.

I should of course let you know where the title of this post comes from since I borrowed most of it from a computer science paper that is over forty years old. I can’t even claim originality for adapting the title of the earlier paper because my friends at OpenEye have beaten me to that as well.

I hope that this post will at least make people ask a few questions when presented with rules like these in the future. I'll also set up a discussion in the LinkedIn Medicinal Chemistry group which will facilitate posting of comments.

Literature cited

Congreve, Carr, Murray & Jhoti, A ‘Rule of Three’ for fragment-based lead discovery? Drug Discov. Today 2003, 8, 876-877 | DOI

Lipinski, Lombardo, Dominy &Feeney, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997, 23, 3-25 | DOI

Blomberg, Cosgrove, Kenny & Kolmodin, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 | DOI

Dijkstra, go to statement considered harmful. Communications of the ACM, 1968, 11, 147-148 | DOI

Screening libraries: Sampling Chemical Space

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I am currently in Rapa Nui (aka Easter Island) and it seemed fitting to continue the series on compound library design from here since the first two posts have been from less commonly visited places like Asuncion and Tierra del Fuego. In the previous post I discussed 2D molecular similarity and showed how this can be used to define diversity and coverage, two important compound library characteristics. In general, compounds in a library need to be mutually diverse in order to provide good coverage although high diversity does not guarantee optimal coverage.

In this post, I’ll take you through an approach to library design called ‘Core and Layer’ (CaL). Although we used this to select compounds for generic fragment libraries and more specialised NMR screening libraries, the method is quite general and I have used it to design a compound library for black box cell screening and to select compounds to complement high throughput screens. The software tools (Flush and BigPicker) used to apply CaL were created at Zeneca by Dave Cosgrove and are described in our article in some detail. Although you might think that the tools were developed in order to apply the CaL method, things actually happened the other way round and it was the availability of the software that led to CaL being adopted as an approach to library design.

Figure 1 shows a schematic view of CaL. The core consists of the compounds currently in the library at any point of the design process and a layer is a set of compounds that have been selected to be diverse with respect to the core. Once a layer has been selected, it is added to the core and the combined set of compounds becomes the new core. The process of selecting layers goes on until you’re either happy with the library or you run out of patience.

You’re probably thinking that this is a very tedious and time-consuming way to build a compound library and might ask whether it would be better to select a maximally diverse set of compounds in a single step. However, there are advantages in building up a library in this manner. In library design, all compounds are not equal and CaL allows you to bias compound selection in a highly-controlled manner. I’ll discuss fragment selection criteria in some detail in future posts in this series so please just assume for now that there are some fragments that you would prefer to have in your library than others. The initial core consists of a sampling of your favourite fragments and as you add layers the compounds in them become progressively less attractive. Another feature of CaL is that it provides a solution to the problem of selected compounds proving to be unavailable as can be the case when trying to source relatively large samples from commercial suppliers.

I think this is a good place to stop as it’s dinner time in Rapa Nui. CaL is an approach to biased sampling of chemical space but it doesn’t tell us about which regions of chemical space should be sampled preferentially. In the next posts of this series I’ll take a look at what makes one fragment better than other. On the travel front, I fly into Auckand in a couple of week’s time for a month and a half in New Zealand and expect to be around Melbourne for the first four months of the New Year. Feel free to get in touch if you’ve got fragment stuff that you’d like to discuss.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 DOI

Screening Libraries: Diversity & Coverage

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I’m guessing that this may be the first blog post on screening library design to be written in Tierra del Fuego. The weather is currently rather unpleasant although less so than an hour ago when the snow was horizontal.

I introduced screening library design in the previous post with a generalised view of the work flow for fragment based lead generation. When selecting compounds for screening it can be helpful to think in terms of a chemical space in which all possible compounds (real or virtual) can be found. Now you’ve just got to sample the regions of chemical space that you like and you’ve got your library.

Life of course is not so easy. The main problem is that, despite the occasional claim to the contrary, nobody has found a convincing set of coordinates with which to describe chemical space usefully. You can sort of describe organic molecules by size and polarity without having worry about minor irritations like conformational flexibility, ionisation and tautomers. However, molecular recognition also depends on the shapes of molecules which, even for rigid species, are not so easy to turn into coordinates. Especially when you want these coordinates to be predictive of biological activity.

All is not lost since structurally similar molecules often have similar biological properties. One way that the similarity of a pair of compounds can be quantified is by comparing their molecular connection tables (the structures that you would write down on a piece of paper) and for this reason we sometimes talk about 2D similarity. There is no need for 3D molecular structures when you calculate molecular similarity in this way which means that there is no need to deal with conformations. Molecular fingerprints are used frequently to calculate similarity and the idea behind this is that the fingerprints encode the presence or absence of structural features in molecules. Many shared features suggest that two molecules are likely to be very similar. I’ll not go into the details of fingerprints in this post although you’ll be able to find some detailed discussion in our screening library design article.

The downside of 2D molecular similarity measures is that they are unlikely to reveal any but the most trivial shape match or pharmacophoric (e.g. oxadiazole replaces ester) similarity between molecules. This is not too much of a problem in library design because you’ll often to want select both molecules if they are based on different scaffolds, even if they can both orient their hydrogen bonding groups in a similar way. Once you’ve found some active compounds it becomes a very different game because now you’ll be looking for less obvious similarities between these actives, either to extract structure activity relationships or to define search queries.

So even though we don’t have a set of coordinates that defines chemical space in a way that is predictive of biological properties of molecules, we can still use molecular similarity to sample from a collection of compounds. Figure 1 illustrates how this sampling works and will give you an idea of what we mean by the terms diversity and coverage. The key thing to remember when looking at Figure 1 is that similar compounds are close to each other so there is an inverse relationship between distance and similarity. The stars are selected to cover the chemical space occupied by all the molecules and a star can’t cover its neighbourhood effectively the compounds in it are too far away

Although I needed put the molecules in particular positions (i.e. give them coordinates) to generate the graphic, you only need the distances between molecules to select representative subsets. In our paper we described in house software which can be used to do this and the two programs (Flush and BigPicker) are actually quite complementary to each other. Left to its own devices, BigPicker tends to select compounds with no near neighbours and we typically use Flush to ensure that the compounds that BigPicker is selecting from all have sufficient number of neighbours.

This is probably a good point at which to leave things. In the next post, I’ll describe the Core and Layer approach to selecting compounds for screening. This method is not specific to fragment libraries and in fact I’ve used it in work up of high throughput screening output and selection of compounds for cell-based assays.

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD, 2009, 23, 513-525 DOI

Grant & Pickup, A Gaussian Description of Molecular Shape. J. Phys. Chem. 1995, 99, 3503–3510 DOI

Screening Libraries: Introduction

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Tonight seemed like a good time to start the series of posts on screening library design. For those of you who don’t know, I’m taking a year out to travel and am currently in Asuncion, Paraguay and it is currently raining heavily with excellent electrical activity. It’s not all holiday and I’ll be dropping in on friends in Brasil and Australia to see if I can make myself useful in their research groups. It’s also a good time to send best wishes to my friends who are currently attending the Gordon Research Conference on Computer Aided Drug Design.

The special issue of the Journal of Computer-Aided Molecular Design devoted to FBDD has just come out and this includes an article on screening library design that three of my friends and I put together. I’ll certainly be drawing on the article in the series of posts on screening library design but these should complement the article rather than reproduce it verbatim.

A good place to start is a graphic of the generalised work-flow for fragment based lead generation which illustrates the process from the perspective of the library designer. Both protein structures and known ligands can be used to select compounds for screening but selections can also be made generically. Note the colour-coding of the arrows which illustrate flows of compounds (red) and information (blue). You keep cycling around until you find something cool or your management runs out of patience.

The graphic represents a generalised work-flow and in some cases selections based on target and/or known ligands may not be made. There are a number of reasons why this may be the case. For example, there may not be any known potent ligands for the target. Also specialised screening technologies may require specialised library formatting which favours the use of generic screening libraries. Even with protein structures available, there is still a role for generic libraries because the current state of the art for fast prediction of binding affinity still falls short of what is required for selection of compounds for screening against an arbitrary target. That is not to say that docking, scoring and affinity prediction methods are completely useless but just that at this stage they represent a basket into which you would not want to put all your eggs. Deciding how many eggs to put in the ‘generic’ and ‘targeted’ baskets depends on how much you know (or think you know) about your target. My own view is that we’re still a long way off being able to usefully predict affinity and much of this is due to the difficulties in modelling the displacement of water molecules from contact with protein molecular surfaces. Time will tell and I’ll be delighted if somebody proves me wrong!

Literature cited

Blomberg et al, Design of compound libraries for fragment screening. JCAMD 2009, 23, 513-525 DOI

Fragment Library Design

The first law of computing (garbage in, garbage out) applies equally well to screening fragments. Selection of compounds for fragment screening is a theme that I will explore in some depth in future posts. For now, I'll just let folk know that we have already compiled some literature on the topic and that our article on this topic is now available 'online first' at the Journal of Computer Aided Molecular Design. Three of my friends and I put this together as a contribution for the special issue of this journal on FBDD so you can expect to see more articles on the subject appearing soon.

There's going to be some fragment action at the ACS. Mel will be there so look out for her.

Literature cited
Blomberg et al, Design of compound libraries for fragment screening, J. Comput.-Aid. Mol. Des., in press (online first) DOI

Screening Library Literature

The first law of computation (garbage in, garbage out) applies equally well to FBDD. These articles discuss the selection of compounds for fragment screening.

Blomberg et al, Design of compound libraries for fragment screening, J. Comput.-Aid. Mol. Des., 2009, 23, 513-525 DOI

Schuffenhauer et al, Library Design for Fragment Screening. Curr. Top. Med. Chem. 2005, 5, 751-762 link

Baurin et al, Design and Characterization of Libraries for Use in NMR Screening against Protein Targets. J. Chem Inf. Comp. Sci. 2004, 44, 2157-2166 DOI