Design vs randomness in lead optimization across a frustrated landscape

The goal of lead optimization is to improve the properties of a molecule such that it can become a drug or a chemical probe. Early in a program, particularly one based on fragments, the focus is often potency, but eventually drug metabolism and pharmacokinetics (DMPK) parameters become important. I always enjoy reading optimization stories and seeing clever use of design principles. But how effective is the design process? Or, to put it another way, how often are improvements just luck? This is the subject of an open-access paper just published in Nature by Bryan Roth, James Fraser, Brian Shoichet, and collaborators at University of California San Franciso, University of North Carolina Chapel Hill, and several other institutes.

 

The goal was to create a “random background” against which to compare directed efforts. To do so, the researchers chose six protein targets with which they had experience: the GPCRs α₂AAR, µOR, and CB2; the transporter SERT; and the soluble enzymes AmpC and Mac1. For each protein, they then chose one to six  ligands, 18 total, with IC₅₀, K_I, or EC₅₀ values ranging from 1 nM to 43 µM. These ‘parent ligands’ were then analyzed systematically to see how they could be modified one atom at a time by adding a methyl group, a halogen, or a phenol, or replacing an aromatic ring carbon with a nitrogen. A decade ago, we wrote about the importance of synthetic tractability, and this was a key consideration here. Ultimately the researchers made 257 molecules, with each parent giving rise to between 5 and 31 analogs. These analogs were then tested in a variety of assays.

 

Breaking things is usually easier than improving them, and that turned out to be the case here: 30% of changes caused a drop in activity of at least ten-fold. Perhaps more surprisingly, 29 of the 257 analogs (11.3%) had at least ten-fold better activity than the parent molecules. Such increases were widespread: only one target (AmpC) did not see a ten-fold increase, and 10 of the 18 parent molecules had more potent analogs. In other words, the odds of improvement for any given chemical series were better than even, even without using any structural insights from the protein.

 

This latest work stands in good company. Back in 2012 we highlighted an analysis of the effects of methyl substituents across thousands of examples covering more than 100 proteins. That work came to similar conclusions, with ten-fold improvements in 8% of cases. A more recent paper looked at 633 published examples where added chlorine atoms improved activity by at least ten-fold. This latest Nature paper finds methyl substituents to be slightly more likely to give ten-fold improvements than chlorine atoms, while adding fluorine was much less likely to yield such improvements, and swapping an aromatic carbon for a nitrogen never did.

 

One might assume that gaining potency by adding a methyl group or a chlorine atom is due solely to increased lipophilicity, but this did not seem to be the case as assessed by calculating LipE/LLE values.

 

Weaker ligands were slightly more likely to be improved by making random changes, which makes sense if you consider that a more potent molecule is likely to be highly complementary to its binding site. Similarly, larger pockets were more likely to see ligand activity improve with random changes.

 

The researchers crystallographically characterized 22 analogs of varying affinities derived from three parent ligands against the protein Mac1 to look for general lessons. Although the analyses proved interesting, “many of the effects of the small perturbation analogs could be explained post hoc from their structures; however, fewer were easily anticipated.”

 

Of course, as noted above, affinity is just one component of a chemical probe, let alone a drug, so the researchers also assessed several in vitro DMPK parameters: microsome stability, plasma stability, plasma protein binding, solubility, hERG inhibition, and membrane permeability. Similar to the activity measurements, some analogs had improvements in one or more properties, while others saw declines. Unfortunately, the analogs with improved potency often had decreased values for in vitro DMPK parameters; none of the 29 analogs with ten-fold improved potency had combined improvements for stability, permeability, and plasma protein binding. As the researchers ruefully conclude, “the changes in in vitro PK were, at best, orthogonal to affinity or potency fold change, and most were, if anything, anti-correlated with it.” This “frustrated landscape” is all too familiar to medicinal chemists.

 

Making and testing all these molecules took a tremendous amount of work (the paper lists over 30 authors), and the researchers naturally wondered whether computational methods would have saved them the effort. They used free energy perturbation (specifically FEP+, from Schrödinger) to calculate affinities and three additional programs to calculate in vitro DMPK parameters. Although the affinity correlations were strong, there was still considerable variation for individual compounds: of 34 analogs predicted to increase in binding energy by more than 1 kcal/mol (about five-fold), only 14 actually did so, while 8 showed decreased activity. Meanwhile, while plasma protein binding and permeability predictions correlated with experimental values, stability and solubility “were essential uncorrelated.”

 

As of now, computers are still no substitute for the good old-fashioned design, make, test cycle. This paper suggests that, in addition to careful design, it may also be worth introducing some randomness into your analogs, particularly when they are easy to make. Sound advice indeed.

Covalent allosteric inhibitors of NLRP3

NLRP3 has received considerable attention as an inflammatory disease target. The protein normally exists in an auto-inhibited conformation, but in response to various stimuli it changes shape to help form a multimeric complex called the inflammasome. This in turn activates various inflammatory pathways, including cell death. Several drugs targeting NLRP3 have gone into the clinic, but none have been approved. In a new open-access Br. J. Pharmacol. paper, Brian Cook, Gabriel Simon, and colleagues at Vividion describe a covalent inhibitor that binds to a previously unknown site.

 

As we wrote nearly a decade ago, Vividion pioneered the use of chemoproteomics to identify covalent binders in cells and cell lysates. While exploring the mechanism of the previously reported NLRP3 inhibitor MCC950, they found that cysteine 280 became resistant to modification by electrophiles in the presence of the ligand. MCC950 does not contain an electrophile, arguing against direct interaction with Cys280 and suggesting instead that binding causes a conformational change to the protein which makes Cys280 less accessible. In other words, protein inhibition correlated with Cys280 being less exposed.

 

A screen of Vividion’s library yielded no hits against Cys280 but more than 300 against cysteine 463. Interestingly, modification of C463 also caused a decrease in reactivity of Cys280, suggesting a conformational change similar to the one caused by MCC950. Thus, the researchers pursued Cys463 binders.

 

Two parameters were used to guide compound optimization, target engagement and promiscuity. The first number, TE₅₀, measures the extent of covalent engagement of a given cysteine after 1 hour in cell lysates. The second, P₅₀₀, measures the percentage of all measured cysteines in the sample labeled by at least 40% after incubation with 500 µM compound for 1 hour. One of the initial hits, VVD-124476, gave a low micromolar value for TE₅₀ but labeled some 20% of the measured proteome. Indeed, it was previously reported as a covalent ligand for a completely different protein. To be useful as a NLRP3 inhibitor, the researchers needed to reduce promiscuity, and the most straightforward approach is to reduce general reactivity.

 

To decrease reactivity, the researchers swapped the acrylamide warhead with a butynamide (VVD-142397). Butynamides are reported to be less reactive than acrylamides, a result corroborated at Vividion with chemoproteomic data on 400 matched molecular pairs. The acrylamide to butynamide swap improved selectivity some 20-fold, though it also decreased potency against NLRP3. Increasing the core ring size regained activity, and rigidifying the molecule led to further improvements. Further optimization ultimately led to VVD-338213 and several related molecules.

 

Although the researchers do not report k_inact/K_I values, a back of the envelope calculation puts it around 1200 M^-1s^-1 for VVD-338213, a potency where cell activity might be expected. Happily this turned out to be the case: the molecule blocked IL-1β secretion from human cells at lower concentrations than MCC950. Importantly, in cells where Cys463 had been mutated to alanine, MCC950 was still active while the covalent molecules were inactive.

 

In addition to promising cell activity, many of the covalent molecules had reasonable stability in whole blood, good permeability, and low efflux. (I do wish the ligand efficiency reactivities of the molecules were reported; after all Vividion, introduced this metric.) Some of the molecules also showed good brain penetration. In vivo pharmacodynamic studies are complicated by the fact that Cys463 is not conserved in mice, but fortunately the researchers could run studies on transgenic mice in which human NLRP3 had been introduced. In these animals, IL-1β was decreased by both VVD-338213 and by MCC950.

 

The researchers were also able to obtain a cryo-EM structure of one of their molecules bound to NLRP3, which confirmed covalent binding to Cys463. More importantly, it revealed that the binding site is in a cryptic pocket some 20 Å from where MCC950 binds. (One wonders if the computational technique we discussed last week would have been able to predict this pocket.) Similarly to the WRN inhibitor Vividion has taken into the clinic, the molecule appears to make only hydrophobic contacts with the protein, with no polar interactions. Mechanistically, the “Cys463 ligands act as molecular doorstops, stabilizing the inactive conformation and preventing the structural rearrangements necessary for transition to the active inflammasome disc.”

 

This is a lovely paper and a case study in transforming a highly reactive molecule into a selective molecule with in vivo activity. It is interesting that the starting molecule, with 28 non-hydrogen atoms, flagrantly violates the rule of three. Last year we asked whether covalent fragments need to be larger than their non-covalent counterparts, and this paper provides another example where the answer is yes. Whether or not this chemical series ultimately advances, it is nice to have another chemical probe for this target.

From noncovalent fragment to (non)covalent leads against PLPro

The most successful drug against COVID-19, nirmatrelvir, targets the main protease of SARS-CoV-2. As we discussed just last year, this protein has received considerable attention. But the genome for SARS-CoV-2 also encodes a second cysteine protease, papain-like protease, or PLPro. Despite this enzyme being essential for viral replication, the only previously disclosed chemical series targeting it dates from 2008 efforts against the original SARS. Two new papers in J. Med. Chem. from Stephen Fesik and colleagues at Vanderbilt University introduce new molecules, covalent and non-covalent.

 

One reason progress has been slow against PLPro is that it is inherently challenging. It recognizes the sequence LXGG, where X = Arg, Lys, or Asn. The two glycine residues thread a narrow channel to access the catalytic cysteine, while leucine and the “X” residue bind in solvent-exposed subsites. In 2024 the Fesik group published an open-access paper in ACS Med. Chem. Lett. describing the results of a protein-observed NMR fragment screen. Out of 13,824 fragments screened in pools of 12 at 0.8 mM each, 77 confirmed when tested individually, and 22 had affinities better than 1 mM.

 

An attractive feature of protein-observed NMR is that it tells you where the fragments bind, and in this case there were two main binding sites. One set of fragments bound in the S3 and S4 pockets, where the leucine and lysine residues of the substrate would normally fit, while another group of fragments bound some distance from the active site. Representatives from both groups were tested for inhibition of enzymatic activity. Only those in the first group were active, so these were prioritized.

 

In the first open-access J. Med. Chem. paper this year, the researchers started with compound 11, which fulfills rule-of-three fragment criteria, binds in the S4 pocket, and inhibits the enzyme with high-micromolar activity. Adding a couple methyl groups (compound 15) improved the potency by roughly 10-fold, and building towards the S3 pocket yielded compound 37, with low micromolar activity. Addition of a basic nitrogen, as in compound 46, improved the potency to submicromolar activity, and crystallography revealed that the basic nitrogen interacts with a glutamic acid side chain. This molecule was active in a cellular assay at submicromolar concentration.

 

An increasingly popular strategy for addressing difficult targets is through covalent inhibitors, and this is the subject of the second open-access J. Med. Chem. paper this year. The researchers synthesized and tested 25 analogs based on molecules such as compound 37 in which various warheads were appended by flexible linkers. Although some of these were active at low micromolar concentrations, only a few showed time-dependent activity as would be expected for an irreversible covalent inhibitor.

 

These few were optimized with the aid of molecular dynamics and structure-based design to molecules such as compound 45. Interestingly, despite being nearly 10-fold more potent than the best non-covalent molecule, it was less active in cells; the researchers attribute lower-than-hoped activity to the two hydrogen-bond donors in the diacetylhydrzaine linker. Unfortunately, these turned out to be essential for covalent binding; crystallography revealed that the compound forms four hydrogen bonds in the glycine channel. Indeed, this particular linker-warhead combination had been previously reported, and the inability to improve on it emphasizes the restrictive requirements for this particular protein.

 

This is a nice series of papers that shows how a single fragment can lead to multiple leads. The last paper is also a useful reminder that adding a warhead to a high-affinity binder is not always easy, nor does it necessarily lead to superior molecules. Indeed, neither the covalent nor the noncovalent leads have any reported in vitro ADME or pharmacokinetic data. It would be fun to screen PLPro against a library of covalent fragments to look for even more starting points.

xSAR: Crystallographic SAR from crude reactions

Last year we highlighted an example of crystallographic screening of crude reaction mixtures to find inhibitors against the oncology target PHIP(2). Of 957 molecules tested, 22 showed crystallographic binding in two different orientations: 19 in a “lateral” pose and 3 in a “diving” pose. In a new open-access Chem. Sci. article, Philip Biggin and collaborators at Diamond Light Source and University of Oxford try to extract information from both the binders and the non-binders using crystallographic structure-activity relationships, or xSAR.

 

Chemists often think about SAR in qualitative terms: a methyl group here improves affinity, a chlorine atom there reduces it. In xSAR, the researchers sought to take a more quantitative approach. They converted each molecule into “Morgan fingerprints,” a set of more than 2048 binary bits describing structural features such as atom type, hybridization, and connectivity to other atoms within a certain distance. Some bits were found in all binding compounds, and these were referred to as conserved binding bits (CBB), while conserved nonbinding bits (CNB) were found only in non-binding compounds. These bits were then used to calculate Positive and Negative Binding Scores (PBS and NBS); a compound with a PBS of 1 contains all the CBB. Since there were two separate binding modes, the researchers calculated PBS and NBS values for both lateral and diving poses individually as well as for all binders.

 

As the researchers note, false negatives are a likely issue in crude reaction screening for a variety of reasons. To hunt for these, the PBS and NBS values were calculated for all 957 molecules previoulsy tested. A set of 97 pure compounds having mostly high scores were acquired and tested crystallographically, yielding an additional 23 lateral binders and 3 diving binders, more than doubling the initial yield. PBS was particularly informative in this retrospective exercise to recover false negatives, outperforming both NBS as well as other methods such as Tanimoto similarity scores.

 

The researchers also used PBS and NBS scores to search prospectively for new binders in a virtual set of more than 1.7 billion compounds in the Enamine REAL database. After filtering for high PBS/NBS scoring compounds followed by docking, 93 compounds were acquired and tested crystallographically. Interestingly, this yielded a relatively low hit rate of 9 binders, 6 in the lateral pose and three in somewhat different poses. None of the new compounds bound in the diving pose, which the researchers suggest may be due to the small sample size used to calculate PBS and NBS for this binding mode.

 

The 93 new compounds were also tested for binding using grated-coupled interferometry (GCI), and 13 showed measurable affinity, with most better than 50 µM. Two even showed single-digit micromolar affinity, more than an order of magnitude better than the best compound from the screen we discussed last year, and with better ligand efficiencies too. Surprisingly, these two compounds were not hits in the crystallographic screen.

 

This is an interesting paper with a couple important lessons. First, despite the fact that affinity was not used in calculating PBS and NBS, these metrics were nonetheless useful for identifying molecules with better affinity than those in the original training set, arguing for their utility. But perhaps just as importantly, the molecules with the best affinity were missed by crystallographic screening. If anything, this observation only strengthens my conclusion last year that while “there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.”

GAS41 revisited: a chemical probe

The YEATS domain of the protein GAS41 is an epigenetic reader that modulates gene expression by binding to acetylated lysine residues in chromatin. Multiple lines of evidence suggest it could be a useful target for various cancers, in particular non-small cell lung cancer (NSCLC). Four years ago Practical Fragments highlighted a paper from Jolanta Grembecka, Tomasz Cierpicki, and colleagues at the University of Michigan describing a fragment screen and subsequent optimization of a hit to molecules with some cellular activity. In a new J. Med. Chem. paper, the same team now describes molecules with better cell potency, as well as a negative control.

 

Compound 1, the initial fragment hit, had weak affinity for GAS41, but replacing the t-butyl group with a proline led to compound 7, with low micromolar activity in a fluorescence polarization assay. On the other side of the molecule, modification and growth of the amide moiety led to compound 16, also with low micromolar activity. In the 2021 paper, molecules related to compound 16 were dimerized to bind to two YEATS domains in close proximity in the GAS41 dimer. This yielded mid-nanomolar inhibitors, but the molecules were also large, with limited cell permeability. In the new paper, the researchers instead combined medicinal chemistry learnings and used structure-based design to generate monomeric molecules, culminating in DLG-41.

 

The affinity of DLG-41 for GAS41 was measured as 1 µM using isothermal titration calorimetry (ITC). In accordance with best practices for chemical probes, the researchers also developed a negative control by replacing the thiophene moiety with a thiazole; this compound, DLG-41nc, shows negligible activity in two different biochemical assays.

 

DLG-41 showed high nanomolar activity in a NanoBRET assay, demonstrating that the molecule is both permeable and binds to the GAS41 protein in cells. Importantly, the IC₅₀ for the negative control DLG-41nc was > 25 µM in this assay. DLG-41 blocked proliferation in a panel of NSCLC cell lines, though DLG-41nc also showed some activity, albeit at higher concentrations. Gene expression studies in one cell line showed that DLG-41 caused changes in hundreds of genes, while DLG-41nc was inactive.

 

This is a nice example of fragment optimization in academia. With both biochemical and cell-based potency around one micromolar, DLG-41 is hovering on the edge of the 2015 suggestions for a chemical probe. But used alongside the negative control, the compound should be useful for further exploring the biology of GAS41.

Crystallography first in fragment optimization: Binding-Site Purification of Actives (B-SPA)

At FBLD 2024, Frank von Delft (Diamond Light Source) announced the ambitious goal of taking a 100 µM binder to a 10 nM lead in less than a week for less than £1000. Fragment to lead optimization usually takes longer, as dozens or even hundreds of compounds need to be synthesized and tested. One way to speed things up is through “crude reaction screening,” otherwise known as “direct to biology,” in which unpurified reaction mixtures are tested directly. In a new (open-access) Angew. Chem. Int. Ed. paper, Frank, John Spencer, and collaborators at University of Oxford, University of Sussex, and Creoptix apply this approach to crystallographic screening.

 

The researchers were interested in the second bromodomain of Pleckstrin Homology Domain-Interacting Protein, or PHIP(2), an oncology target. As we discussed in 2016, they had previously run a crystallographic screen and identified multiple hits, including F709, which, despite having no measurable affinity, had good electron density and multiple vectors for optimization. Six separate libraries based on this fragment were constructed, with between 58 and 1024 targeted small-molecule products per library and up to four steps done without purification.

 

One challenge for crude reaction screening is assessing whether or not a reaction has actually generated product. Typically this is done by analytical liquid chromatography mass spectrometry (LCMS), but analyzing results manually is tedious. Fortunately academics have graduate students and postdocs, and it was presumably these intrepid souls who spent 17 days analyzing the 1876 small-molecule products attempted.

 

I can say from personal experience that spending hours perusing LCMS chromatograms is not enjoyable, so the researchers built an automated tool called MSCheck, which appears to be freely available here. This showed 83% agreement with the manually curated data, and even identified additional true positives that had been missed. All together 1077 of the reaction mixtures had the desired product, with success rates for the various libraries ranging from 39% to 97%.

 

The successful reactions were soaked into crystals and screened, and nearly 90% of these generated usable data. A total of 29 crystals had interpretable density in the ligand binding site: 7 were starting materials and 22 were desired products. Of the products, 19 bound with the piperazine core in a similar position as the initial fragment, while three bound in an alternate manner.

 

Of course, the whole point of this exercise is to find improved binders, so the researchers tested pure versions of each of the 22 crystallographic hits in two different assays. Only compound PHIP-Am1-20 had measurable affinity, with modest ligand efficiency.

This is not the first example of crude reaction screening by crystallography; we wrote about REFiL_x and a related technique in 2020. In one of those papers, the crude reaction mixtures were assessed by SPR as well as crystallography, which revealed that the crystallographic screen missed some binders, and there is no reason to think the same did not happen here. Indeed, molecules that bind tightly in a different conformation may be more likely to shatter the crystal lattice and thus go undetected.

 

The researchers state that for non-crystallographic crude reaction screening “only strong assay readouts are informative.” But is this bug, or a feature? A 2019 publication that used crude reaction screening to identify KRAS ligands (which I wrote about here) used an assay cascade to quickly select the most potent hits. Even the fastest crystallographic screens can’t compete with plate-based assays in terms of speed.

 

Perhaps PHIP(2) is a particularly challenging test case. As we discussed in 2022, multiple computational screens performed poorly in predicting crystallographic binding modes of ligands for this protein. But as I wrote at the time, it may be that many crystallographic ligands are just too weak to be useful.

 

Although there is a strong case for using crystallography first for finding fragments, I am not yet convinced the same applies for optimizing fragments.

Crude success against the SARS-CoV-2 main protease: From covalent fragment to noncovalent lead

With increased throughput and reliability of biophysical and other methods, finding fragments against most targets is now fast and easy. Advancing these fragments to leads, not so much. In a new open-access Angew. Chem. Int. Ed. paper, Jacob Bush and collaborators at GSK, University of Strathclyde, and the Francis Crick Institute provide a case study for how to accelerate the process.

 

Almost exactly five years ago we highlighted early efforts against the main protease (M^pro) from SARS-CoV-2. This target turned out to be a good choice, as demonstrated by the rapid discovery and approval of the drug nirmatrelvir. M^pro is a cysteine protease and thus ideally suited for covalent fragment screening.

 

In the new paper, the researchers screened a library of 219 chloroacetamide-containing fragments (each at 5 µM) individually against 0.5 µM protein for 16 hours at 4 ºC and then analyzed them by intact protein mass spectrometry. Six of these gave at least 75% modification, and further characterization found that the most potent, compound 2, had a k_inact/K_I = 170 M^-1s^-1. This (and the other hits) also inhibited the protein in an enzymatic assay, and additional chemoproteomic experiments revealed that compound 2 could bind to the active site cysteine of M^pro in living cells with surprising selectivity; just 11 targets were more strongly engaged than M^pro.

 

To optimize compound 2, the researchers turned to crude reaction screening, also known as direct-to-biology or D2B. As we described here and here, this entails running reactions at small scale and testing them directly, without purification. To validate the approach, the researchers synthesized a subset of the original 219 chloroacetamides in 384-well plates. HPLC studies confirmed the desired product as the major component for 43 of the 69 attempted syntheses; only four failed. Importantly, there was a good correlation in activity between the crude reaction mixtures and the pure molecules.

 

Next, the researchers synthesized a new D2B library of 193 molecules related to compound 2. HPLC analysis of the crude products showed a 77% success rate, with just nine outright failures. The library was screened against M^pro for 1 hour (as opposed to 16 hours in the first screen), resulting in 14 hits. The best of these, compound 7a, was such a rapid modifier that the a k_inact/K_I could not be easily calculated, but it showed nanomolar activity in the enzymatic assay. It was also more selective than compound 2 in cell-based experiments.

 

Chloroacetamides are not considered advanceable as drugs, so the researchers sought to remove the warhead, initially by replacing it with the simple acetamide in compound 12. Although this molecule showed almost no activity in the enzymatic assay, the researchers coupled a diverse set of 146 carboxylic acids to the amine building block and screened the crude reaction mixtures in a functional assay at 50 µM to identify seven molecules that gave nearly complete inhibition, with compound 13 being the most potent. A second D2B library of analogs around compound 13 was screened at 1 µM, leading to the mid-nanomolar compound 14.

 

This is a nice illustration of the power of crude reaction screening to rapidly identify new chemical matter. It is true that M^pro is quite ligandable; we wrote about other non-covalent fragment success stories here and here. However, as we discussed here, D2B can be applied to more challenging targets. The supporting information in the new paper should be particularly valuable for those hoping to try the approach themselves.

 

At FBLD 2024 Frank von Delft set a goal of taking a “100 µM binder to a 10 nM lead in less than a week for less than £1000.” We’re not there yet, but developments in D2B are moving us forward.

Poll results: fragment finding methods and structural information needed for fragment-to-lead efforts

Our most recent poll asked about fragment finding methods. The poll ran from September 21 through November 8 and received 135 responses from 20 countries. Two thirds of these were from the US, about 12% were from the UK, 4% from Germany, 3% from the Netherlands, and 2% from Australia.

 

The first question asked how much structural information you need to begin optimizing a fragment. In contrast to 2017, when we first asked this question, crystallography has significantly increased at the expense of the other choices. 

 

 

I confess to being surprised, as I expected that by now people would be more comfortable beginning optimization in the absence of structural information, an approach that has been quite successful as discussed in a 2019 open-access Cell Chemical Biology review by Ben Davis, Wolfgang Jahnke, and me. Perhaps the increasing speed and accessibility of new methods has so lowered the bar to getting crystal structures that people have the luxury of waiting. Of course, with an online poll there is always the risk that many respondents from the same organization may skew the results.

 

The second question asked which methods you use to find and validate fragments. This is the fifth time we’ve run this poll, starting in 2011. As with our first question, X-ray crystallography came out on top, with nearly 80% of respondents choosing it. This was followed by SPR, at 67%, and thermal shift and ligand-detected NMR, each around 55%. 

 

 

Functional screening was used by nearly half of respondents, with computational methods, protein-detected NMR, and literature starting points used by around a third. Mass spectrometry and ITC were each used by slightly more than a quarter of respondents.

 

For the first time we asked about cryo-EM, and nearly 20% of respondents reported using this technique.

 

MST and affinity-based methods each came in at 13%, with just 4% of respondents using BLI, and 5 individual respondents using other methods. I’d be curious to know what these are.

 

The average respondent reported using just over 5 different techniques, which is down slightly from 6 in 2019 but up from 4 in 2016. Using multiple orthogonal methods is clearly well established as best practice, even if the precise number varies.

 

How do these results compare with your own practices?