3D fragments vs the histamine H1 receptor

Last month we highlighted a paper from Iwan de Esch and colleagues at Vrije Universiteit Amsterdam about assessing molecular shapeliness. In a new open-access RSC Med. Chem. paper, de Esch and colleagues use a small, shapely library to find and develop potent antagonists of the histamine H₁ receptor (H₁R).

 

The G protein-coupled receptor H₁R is one of four histamine receptors and the target for dozens of antihistamines used for allergic and inflammatory conditions. Thus, it’s well understood and a nice model system.

 

In 2013 we discussed how the de Esch group screened a library of 1010 fragments against several targets, including H₁R. In the new paper, the researchers built a smaller library of just 80 compounds designed to be more shapely, as assessed by Fsp³, plane of best fit (PBF), and principal moment of inertia (PMI). A screen against H₁R using a radioligand displacement assay at 10 and 30 µM yielded a single hit, compound 1a.

 

Compound 1a deviates slightly from the rule of three due to some unusual elements: an azide as a synthetic handle and a butyl moiety to make the compound less explosive. Trimming these features led to compound 3a, a rule-of-three compliant fragment that – while having lower affinity – mostly maintained ligand efficiency and improved lipophilic ligand efficiency (LLE). Fragment growing led to compounds 13a and 13k, and merging with previously reported molecules led to compound VUF26691, with low nanomolar affinity and picomolar antagonistic activity in a cell assay. Throughout the process the cis isomers generally had equal or better potency than the trans isomers, an empirical observation difficult to explain by modeling.

 

This is a nice fragment-to-lead story, and the researchers note that fewer than 40 compounds were synthesized in the journey from compound 3a to VUF26691. Interestingly though, compound 1a was the only hit from the 80 compounds tested, while the hit rate from the larger, flatter library screened in 2013 was nearly three-fold higher, at 3.6%. Although it’s difficult to extrapolate from n=1, the results are consistent with our post last year that more shapely libraries will likely have lower hit rates.

 

Still, this shapely hit makes for a neat scaffold, and if nothing else perhaps it will be easier to establish intellectual property.

How best to assess molecular shapeliness?

The shape of a molecule influences its properties. While this is true on a per-compound level, things get a little more controversial when discussing molecules in general. Back in 2009 researchers argued that “three dimensional” molecules have better drug-like properties, though this assertion has been challenged, repeatedly. But how do you assess the shape of a molecule in the first place? In a recent (open-access) Drug Discov. Today paper, Iwan de Esch and collaborators at Vrije Universiteit Amsterdam compare the main metrics.

 

The researchers focus on three metrics: fraction of sp³-hybridized carbons (F_Csp³), which we wrote about here; plane of best fit (PBF), which we wrote about here; and principal moment of inertia (PMI), which we wrote about here. F_Csp³, which ranges from 0 to 1, is simple to calculate based on the chemical structure alone, while the other two metrics rely on the three-dimensional shape of the molecule, requiring calculations and indeed choices since many molecules can assume multiple conformations. PBF is measured in angstroms with a minimum of 0 Å and no maximum; a protein, for example, could easily have a PBF above 10 Å. PMI is represented by two normalized PMI ratios, and these are often added to give a number (3D Score or ΣNPR) between 1 and 2.

 

The researchers calculated F_Csp³, PBF, and ΣNPR for a set of nearly half a million commercially available fragments which we discussed here; PBF and ΣNPR were calculated based on the single lowest energy conformation for each molecule. As noted above, PBF is somewhat size-dependent. For example, adamantane and buckminsterfullerene have PBF scores of 0.79 and 1.76 Å but identical ΣNPR scores. Nonetheless, the researchers found a correlation between these two metrics, and this correlation increased when PBF was divided by the root of the molecular volume to attempt to normalize for size.

 

In contrast, no correlation was found between F_Csp³ and PMI, making the former “a poor descriptor for predicting 3D molecular shape.” Is there a simple alternative? F_Csp³ only considers carbon atoms, so the researchers proposed F_HAsp³, which includes nitrogen, oxygen, and sulfur atoms. Perhaps not surprisingly, this didn’t improve the correlation.

 

Three years ago we wrote about “spacial scores,” which were developed to assess molecular complexity. The researchers calculated normalized spacial scores (nSPS) for their set of compounds, but these also showed no correlation to PMI.

 

The researchers conclude that, “once corrected for size, PBF captures three-dimensionality similarly to ΣNPR values. However, unlike a PMI analysis, it is not capable of further distinguishing between rod- and disc-shaped molecules, giving PMI a higher resolution in capturing shape diversity.” Interestingly, this is the opposite conclusion of an analysis Teddy wrote about in 2014. My take is that, if you want to assess shapeliness, steer clear of F_Csp³, but both PBF and PMI are fine.

Flatland: still a nice place to be

In 2009 we highlighted a paper reporting that approved drugs have a higher fraction of sp³-hybridized carbon atoms than discovery-phase compounds. Perhaps focusing on molecules with a high Fsp³, the ratio of sp³-hybridzed carbons to total carbons, would lead to greater success. Or not: a new analysis in Nat. Rev. Chem. by Ian Churcher (Janus Drug Discovery), Stuart Newbold, and Christopher Murray (both at Astex) finds that the relationship has not held up.

 

The 2009 “Escape from Flatland” paper was widely discussed at conferences and has been cited more than 3000 times. But according to the authors of the new study, most of these citations are from papers describing new synthetic methodologies rather than from papers discussing medicinal chemistry.

 

And not all the attention has been positive. As we noted in 2013, Pete Kenny and Carlos Montanari reanalyzed the data and found that an apparent correlation between Fsp³ and solubility disappeared when plotting all discrete data points instead of binned data.

 

More recently, we highlighted a paper that found no significant difference between the shapeliness of drugs, as assessed by their principal moment of inertia (PMI), and the shapeliness of small molecules in the ZINC database.

 

The new paper looks at Fsp³ values for drugs approved during various time periods. Among 980 drugs approved up to 2009, the average Fsp³ was 0.458. However, of the 431 drugs approved after 2009, the average Fsp³ has dropped to 0.392. The researchers speculate that this (statistically significant) decrease may be due to an increase in the number of kinase inhibitors, which are usually highly aromatic, as well as an increase in the use of metal-catalyzed cross coupling reactions.

 

In my analysis of the 2009 paper, I asked whether higher Fsp³ ratios would lead to lower hit rates, and indeed this seems to be the case, as shown in a paper we discussed in 2020. Thus, if you pursue difficult targets, you may increase your chances of finding hits by screening molecules with lower Fsp³ ratios. Also, multiple studies, including one published just last month, have found no correlation between the shapeliness of a fragment (as defined by deviation from planarity, or DFP) and the shapeliness of the resulting lead, so there appears to be no penalty to starting with a flattish fragment. 

 

The researchers conclude that their “analysis of drug development trends over the last 15 years suggests that Fsp³ may not have been a useful metric to optimize.” Importantly, the supplementary information includes a list of >1400 approved drugs and >1500 investigational drugs along with associated properties, so you can do your own analyses.

 

In the end, generalizations will only get you so far, and may even lead you astray. At least for now, there are few shortcuts in the long slog of experimental studies necessary to discover a drug.

Review of 2022 reviews

The winter solstice is behind us in the Northern Hemisphere, which means 2022 is rapidly drawing to a close. As we have done for the past decade, Practical Fragments will spend this last post of the year summarizing conferences and reviews.

 

The remarkable progress in vaccines against SARS-CoV-2 allowed the full return of in-person conferences, and it was nice to see folks at CHI’s Discovery on Target in Boston and Drug Discovery Chemistry in San Diego. Nearly twenty reviews of interest to this readership were published, and these are covered thematically.

 

Targets

Several reviews cover the use of FBLD to target antiviral and antibacterial targets. Sangeeta Tiwari and colleagues at University of Texas El Paso cover both in an open access Pharmaceuticals review, focusing on tuberculosis and HIV, which often afflict the same individuals, leading to worse outcomes. The paper includes several tables with chemical structures, though the fragment origins of some molecules are not apparent.

 

Tuberculosis is caused by Mycobacterium tuberculosis, but there are more than 170 known members of the Mycobacteriaceae family. In an open access Int J. Mol. Sci. paper, the Tiwari group describes fragment-based approaches against these bugs. In addition to multiple examples, the review provides summaries of fragment finding methods and some of the challenges the field faces.

 

Another organism, Pseudomonas aeruginosa, infects the lungs of people with cystic fibrosis. In an open access Front. Mol. Biosci. paper, Tom Blundell and collaborators at University of Cambridge summarize fragment-based campaigns against this organism and its enzymes. The authors focus on structure-guided methods and note that the work is “at an early stage” but encouraging.

 

Switching to mammalian targets, Katrin Rittinger and colleagues at The Francis Crick Institute review (open access) applications of FBLD for targeting the ubiquitin system in Front. Mol. Biosci. The paper includes a nice table summarizing 15 examples that includes target, enzyme class, fragment binding mode, detection methods, and chemical structures of the fragment hit and optimized compound where applicable. Many of these are covalent modifiers; more on that topic below.

 

Finally, Tarun Jha, Shovanlal Gayen, and collaborators at Jadavpur University discuss “recent trends in fragment-based anticancer drug design strategies” in Biochem. Pharm. In addition to case studies (with chemical structures) of FBLD approaches against 18 oncology targets, the review covers fragment libraries, screening methods, optimization, and challenges.

 

Methods

Many of the targets above are challenging, and it’s always nice to be able to assess how challenging a project might be at the outset. In Curr. Opin. Struct. Biol., Sandor Vajda and collaborators at Boston University and Stony Brook University discuss (open access) “mapping the binding sites of challenging drug targets.” This is a brief, readable account of computational methods to identify hot spots, including allosteric ones. The authors examine the various small-molecule binding sites on KRAS and conclude that, due to “limited druggability,” the “other G12 oncogenic mutants will be very challenging.” Perhaps, but not impossible, as researchers at Mirati demonstrated earlier this year with the (open access) publication of a low (or sub) nanomolar KRAS^G12D inhibitor.

 

Among experimental methods used in FBDD, NMR is a mainstay, as demonstrated by Luca Mureddu and Geerten Vuister (University of Leicester) in Front. Mol. Biosci. (open access). The paper covers methods, successes, and challenges, focusing on three compounds that reached the clinic: AZD3839, venetoclax, and S64315.

 

In contrast to NMR, dynamic combinatorial chemistry (DCC) and DNA-encoded libraries (DEL) are used less frequently in FBLD. In RSC Chem. Biol., Xiaoyu Li and collaborators at University of Hong Kong and Jining Medical University discuss “recent advances in DNA-encoded dynamic libraries.” This concise paper covers lots of ground and does not understate the challenges.

 

Libraries

“The importance of high-quality molecule libraries” is emphasized by Justin Bower and colleagues at the Beatson Institute in Mol. Oncol. This highly readable and wide-ranging open access review covers all aspects of library design and use and includes comparisons of some of the major commercial vendors. An important point is that the “hit rate does not define the success of a library as it is more important to identify ligand-efficient and chemically tractable start points.”

 

Thus, even though shapely fragments may have lower hit rates than more planar aromatic fragments, they may still be worth including – if you can make them. In Drug Discov. Today (open access), Peter O’Brien and collaborators at University of York and Vrije Universiteit Amsterdam review synthetic strategies behind 25 “3D” fragment libraries. The tabular summary showing all the scaffolds emphasizes that most of these libraries are modest in size, with the largest being 102 members. Chemists will particularly enjoy the multiple synthetic schemes. The authors note the importance of “fragment sociability” to facilitate SAR and elaboration.

 

Covalent fragments

Special libraries are required for covalent fragment-based drug discovery, the most notable feature being the “warhead” that reacts with the protein target. These are the focus of a chapter in Adv. Chem. Prot. by Péter Ábrányi-Balogh and György Keserű of the Hungarian Research Centre for Natural Sciences. The review includes a table containing more than 100 warheads with associated mechanisms and amino acid selectivity.

 

The “reactivity of covalent fragments and their role in fragment-based drug design” is the focus in an (open access) Pharmaceuticals review by Kirsten McAulay and colleagues at the Beatson Institute. This is a nice overview of the field and contains several case studies. The authors conclude that “striking a balance between reactivity, potency and selectivity is key to identifying potential candidates.”

 

“Advances in covalent drug discovery” are reviewed (open access) by Dan Nomura and colleagues at University of California Berkeley in Nat. Rev. Drug Disc. This is a highly readable and comprehensive overview of the field. The authors differentiate between “ligand-first” approaches, in which a covalent warhead is appended to a known binder (such as here) and “electrophile-first,” in which “the initial discovery process is rooted in finding a covalent ligand from the outset,” such as for KRAS^G12C inhibitors.

 

Another broad overview of covalent inhibitors is provided by Juswinder Singh (Ankaa Therapeutics) in J. Med. Chem. Jus is a pioneer in the field, having published the first targeted covalent inhibitor in 1997. Of 1673 small molecules approved as drugs by the US FDA, only about 7% are covalent, and it wasn’t until recently that these have been intensively pursued. Part of the reluctance has been concerns over toxicity, but the paper suggests that – at least among kinase inhibitors – covalent drugs may actually be safer, perhaps due to conjugation of glutathione to the warhead and rapid clearance rather than formation of reactive metabolites.

 

Other

Whether covalent or not, thermodynamics plays a fundamental role in protein-ligand interactions, and this is the topic of an (open access) review in Life by Conceição Minetti and David Remeta of the State University of New Jersey. The paper covers a lot of ground, including drug discovery approaches, metrics (such as LE, LLE, etc.), isothermal titration calorimetry, case studies, and more. Importantly, the authors acknowledge the many challenges of applying thermodynamics to drug discovery, some of which we highlighted here.

 

Thermodynamics explains the potency increases longed for when doing fragment-linking, the subject of two reviews. In Chem. Biol. Drug Des. Anthony Coyne and colleagues at University of Cambridge provide a broad overview, starting with the historical theoretical background and newer developments. The bulk of the paper surveys published examples of fragment linking, with structure-based methods (whether X-ray, NMR, or computational) separated from target-guided methods such as DCC.

 

The second review, published in Bioorg. Chem. by Junmei Peng and colleagues at University of South China, is broader in scope, encompassing not just FBLD but also linkers used in PROTACs and even antibody-drug conjugates. The paper is organized by chemical structure of the linker.

 

Finally, in J. Med. Chem., Peter Dragovich, Wolfgang Happ, and colleagues at Genentech and Roche examine “small-molecule lead-finding trends” at their organizations between 2009 and 2020. (Although Genentech is fully owned by Roche, its research organization operates independently.) Fragment-based approaches led to only a small fraction of chemical series at Genentech and none at Roche. The authors note that leads derived from public sources such as patent applications were often found and pursued earlier, and that “purposeful dedication” of resources to fragment approaches may be necessary. Another major source of leads at Genentech is in-licensing, and some of these are fragment-derived.

 

And that’s it for 2022, year three of COVID-19. Thanks for reading and special thanks for commenting. May the coming year bring health, peace, and significant scientific progress.

A framework for evaluating commercial fragment libraries

The easiest way to build a fragment library is to purchase one. Quite a few vendors sell fragments, and as our poll from a few years ago demonstrated, most buyers are quite happy with them. But what exactly do they offer? This is the subject of a new paper by Gilles Marcou, Esther Kellenberger, and colleagues at CNRS Université de Strasbourg in RSC Med. Chem.

 

The researchers analyzed 86 different libraries from 14 vendors that were available in February of 2021. These were classified into ten categories, such as “general,” “3D-shaped,” “metal chelating,” “diverse,” “covalent,” etc. Individual library sizes ranged considerably: 41 had ≤ 2000 compounds, 31 had 2000-10,000, and 14 libraries had > 10,000 molecules. The total number of fragments came to 754,646, of which 512,284 were unique, indicating some redundancy between libraries. Laudably, the structures and several analyses are all provided as downloadable files here.

 

Most of the fragments are 200-300 Da, with only 13% less than 200 Da. This skew towards larger molecules is common but may not be desirable, as researchers at Astex demonstrated back in 2014. On the other hand, people do seem to be paying attention to lipophilicity: nearly half the fragments have AlogP < 1. Interestingly, less than a quarter of fragments strictly fulfill the rule of three, though the majority of violations are for more than 3 hydrogen bond acceptors, which is probably not as important as the other criteria, according to analyses of approved drugs.

 

Two different methods were used to assess diversity. These were applied to 433,433 compounds from fifty libraries; specialized libraries such as fluorine-rich and covalent libraries were excluded. The first analysis deconstructed fragments into 59,270 component scaffolds. Not surprisingly, benzene was the most common, present in nearly 5% of all fragments. Quinoline, indole, pyridine, and benzimidazole all were present in at least 1% of compounds. At the other end of the spectrum, 36,555 scaffolds occurred only once. Not surprisingly, these tended to be more complex.

 

In addition to assessing scaffolds, the researchers developed a “Generative Topographical Map (GTM) model to represent the chemical space in a landscape.” The resulting figures do indeed look like topographical maps, with darker regions corresponding to more populated areas. For example, since substituted benzimidazoles are common and similar to one another, they form a dark cluster. Not unexpectedly, the landscape for the set of 433,433 compounds is heterogenous, with denser regions separated by sparsely-populated regions.

 

A nice feature of the GTM model is that it allows easy, intuitive comparisons. For example, some of the “diverse” libraries are more diverse than others, or emphasize different regions of chemical space, and potential customers may want to take these into account.

 

Fragment shapeliness was assessed using plane of best fit (PBF), where lower values correspond to “flatter” molecules, such as benzene, with PBF = 0. The libraries varied considerably in their average PBF, though reassuringly the “3D-shaped” libraries did have higher values. Interestingly, GTM models showed both flat (PBF < 0.1) and non-planar (PBF ≥ 0.1) fragments had similar distributions across fragment space.

 

Overall this is a valuable snapshot of the current state of commercial libraries, and makes a useful complement to the ongoing analysis Chris Swain does at Cambridge MedChem Consulting. Of course, the devil is in the details; PAINS still sometimes show up in commercial libraries, and quality control can vary. In the end you’ll want to do your own vetting, but this is a good place to start.

New fragments suggested by machine learning

Machine learning has become a hot new thang in drug discovery, attracting massive attention and investment. While easy to parody, artificial intelligence techniques are becoming increasingly powerful. A new paper in J. Chem Inf. Mod. by Angelo Pugliese and colleagues at the Beatson Institute applies the methodology to generate a new fragment library.

 

Machine learning entails collecting large amounts of data, passing that through various neural networks, and obtaining recommendations. In this case, the researchers wanted to generate “privileged fragments” that would hit in multiple assays. (Of course, the idea would be to make genuinely privileged fragments, such as 4-azaindole, rather than PAINS.) The researchers used a training set of 66 fragments that hit in at least three of 25 screens done at the Beatson, for which the average hit rate was 2.18%.

 

First though, the researchers needed to teach their model how to generate chemically valid fragments in the first place (for example, fewer than 5 bonds to carbon). To do this they used both SMILES (simplified molecular-input line-entry system) and chemical fingerprints from a set of 486,565 commercially available fragments. They then combined this model with the privileged fragments. Extensive details are provided; as they go well beyond my expertise I won’t even attempt to summarize them. (For example, “the classifier for the smi2smi model comprised sequential 64-unit and 32-unit dense ReLU layers followed by a single sigmoid output neuron.”) At the end of the exercise, and after triaging by medicinal chemists, the researchers came up with a set of 741 fragments.

 

What are their overall properties? For one thing, generated fragments tend to be more planar (as assessed by PBF) and have lower Fsp³ values than the nearly half-million fragments used for training. The researchers acknowledge that this could reflect the historical composition of the Beatson fragment library, although as we argued here it could also be true that flatter fragments just give higher hit rates.

 

Molecular complexity is a fundamental but poorly defined aspect of fragment-based lead discovery, and the researchers have come up with their own metric, called feature complexity (FeCo), which incorporates information on rotatable bonds, numbers of halogens, hydrogen bond donors and acceptors, charged groups, aromatic rings, and hydrophobic elements, all normalized by the number of heavy atoms. Hopefully this will be explored more fully in a dedicated publication.

 

What do the individual fragments actually look like? Five examples are shown in the paper, and nearly 200 more are provided in the supporting information. Below are seven chosen arbitrarily from that list (sampling every 30 structures).

 

Of course, the question remains as to whether these fragments will truly turn out to be privileged. As might be expected given the vastness of chemical space, only 78 of the 741 are commercially available. The researchers note that they are acquiring some of these, and it will be interesting to see how they perform in the screens to come.

Fragment mixtures vs protein mixtures

In FBLD – as in most areas of research – speed and efficiency are prized. The faster you can find quality fragments, the faster you can advance them. NMR-based screening remains one of the most popular fragment-finding methods, and in a recent Molecules paper William Pomerantz and collaborators at the University of Minnesota and Gustavus Adolphus College provide an accelerated workflow.

 

The Pomerantz lab is well known for protein-observed ¹⁹F (PrOF) NMR, in which fluorine-labeled residues are incorporated into proteins. This is easily accomplished by supplementing the media with fluorine-containing amino acids during protein expression. To date more than 15 fluorinated amino acids have been tested in more than 70 proteins, ranging from 7 to 180 kDa in size. Because the chemical shift of fluorine is so sensitive to its environment, a fragment binding nearby can be readily detected by PrOF NMR.

 

When a single type of amino acid is fluorinated, the resulting protein spectrum is considerably simpler than in traditional protein-observed NMR methods. Taking advantage of this, the researchers mixed two different bromodomain proteins: the human oncology target BPTF and PfGCN5 from the malarial parasite Plasmodium falciparum. Both of these bromodomains contain a tryptophan in their N-acetyl-lysine binding sites, so each protein was labeled with 5-flurotryptophan. The proteins were then screened (at 50 µM each) against 467 fragments from Life Chemicals in pools of 4-5 (at 400 µM each). Chemical shift perturbations of the binding-site tryptophan were seen for half of the 98 pools. To determine which fragments were responsible for these shifts, the researchers tested their fragment mixtures against the relevant proteins using (ligand-detected) CPMG NMR. Since they had previously determined the ¹H NMR spectra of all their fragments, it was easy to pick out the binders.

 

Hit rates were similar for both BPTF (9.8%) and PfGCN5 (9.2%), and 4.1% of fragments hit both bromodomains. The researchers had previously screened this library, which is enriched for shapely fragments, against the bromodomain BRD4 D1 (see here) and obtained a similar hit rate. Statistical analyses revealed that the 3D-character for PfGCN5 hits is similar to the library as a whole, as had also been seen for BRD4 D1, while the BPTF hits tended to be flatter.

 

The researchers also followed up on several  fragments individually. One in particular had low micromolar affinity for PfGCN5 as assessed with both PrOF NMR and ¹H-¹⁵N HSQC NMR titrations. Interestingly, this fragment also caused a chemical shift in a different 5-fluorotryptophan residue some 22 Å away from the canonical binding site. Binding at this site could not be competed by a known high-affinity ligand, and a computational screen using FTMap suggested that this does appear to be a secondary binding site.

 

Overall this approach appears to be an appealing workflow as judged by comparing required time, protein, and ligand amounts to other NMR-based screening cascades. As the researchers note, it is advantageous to assess both protein and ligand behavior, as done here. Have you tried using PrOF, and if so how has it performed for you?

Flatland: a nice place to be

The ideal shape of compounds used for biological screens is a subject of vigorous debate, with some arguing that shapely molecules may be superior in various ways to the “flatter” aromatic compounds that tend to dominate libraries. This view was expressed more than a decade ago in the paper, “Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success.” However, those conclusions have been challenged. Since many of us are trying to discover drugs, it is worth asking what actual drugs look like. This is the subject of a new ACS Med. Chem. Lett. paper by Seth Cohen and colleagues at University of California, San Diego.

Assessing shapeliness is itself contentious. Here the researchers chose the intuitive metric, principal moment of inertia (PMI), which uses a simple triangle plot to assess whether a molecule is more rod-like, disk-like, or sphere-like. The degree of shapeliness (3D Score) can be calculated by summing the x- and y-coordinates to give values between 1 (rod- or disk-like) and 2 (sphere-like).

The researchers first extracted more than 8500 drugs and nutraceuticals from DrugBank, all of which had associated three-dimensional structures and MW >100. PMI calculations revealed that nearly 80% were linear or planar, with 3D Scores < 1.2. Another 17.5% had 3D Scores up to 1.4, while only 0.5% were greater than 1.6. Interestingly, this distribution is similar to that of the ZINC database of small molecules. You might expect a correlation between size and shapeliness, with larger molecules being more three-dimensional, but this was not the case. Perhaps related, a separate analysis found no correlation between shapeliness of fragments and resulting leads.

The 3D structures of compounds in DrugBank are calculated for energy-minimized conformations, which are not necessarily the biologically relevant conformations. So the researchers next went to the protein data bank (PDB) and its crystal structures of 502 unique DrugBank molecules bound to various proteins. Some molecules were represented multiple times (1036 structures of sapropterin!), and for these the PMIs were averaged. The results of this analysis were similar, with 83.5% of molecules having a 3D Score < 1.2 and just three molecules with a 3D Score > 1.6. As with the DrugBank data, there was no correlation between 3D Score and molecular weight.

Further analyses of compounds with multiple crystallographic structures was interesting. For diclofenac, with 51 PDB entries, 3D Scores ranged from 1.03 to 1.52, with the minimized score being 1.22. However, some of these structures are likely low affinity with questionable biological relevance. In contrast, for five approved HIV drugs, the PMIs remained very similar for molecules bound in the active sites.

Getting out of flatland is surprisingly difficult: the researchers examined the PMIs for several fragments from libraries designed to have shapely members and found that none had 3D Scores > 1.4. They suggest clever ways of increasing three dimensionality, such as building organometallic molecules. While this is likely to increase novelty and patentability, it also introduces unknown biological risks. One analysis that would be interesting is whether natural-product-derived drugs are significantly shapelier than their purely synthetic counterparts.

The researchers conclude:

The true need for topological diversity in feedstocks and final drug molecules remains unclear given the overwhelming number of linear and planar drugs. The question remains as to whether more 3D compounds represent attractive and untapped therapeutic space, or if more linear/planar molecules are indeed the best topologies for bioactive molecules.

This is indeed an interesting question, and I hope that chemists – particularly those in academia – continue to make and test ever more exotic molecules. But since the first word of this blog is “Practical,” I would not discount the more planar molecules that make up most of our pharmacopoeia.