28 May 2024

Free computational fragment growing with ChemoDOTS

Back in 2018 we highlighted diversity-oriented target-focused synthesis, or DOTS, a combined computational and experimental method for growing fragments. The computational piece of this has now been turned into a free web server, called ChemoDOTS, and is described in Nucleic Acids Research by Xavier Morelli, Philippe Roche, and colleagues at Aix-Marseille University.
To get started, the user draws or uploads the structure of a fragment hit they wish to expand. ChemoDOTS identifies potentially reactive functionalities, such as amine groups. For each functionality, the program also provides compatible reactions, derived from a set of 58 commonly used in industry. The user then chooses one or more reactions of interest, at which point the program generates a list of molecules that could be created by linking the fragment to various building blocks using the selected chemistries. The building blocks themselves consist of 501,542 commercially available molecules from MolPort and 988,112 molecules from Enamine having between 4 and 24 non-hydrogen atoms.
The program generates molecules quite rapidly, between 1000-1500 per second. All of these can be downloaded at this point, but ChemoDOTS also allows further processing. Histograms showing molecular weight, cLogP, total polar surface area, the number of hydrogen bond donors and acceptors, and Fsp3 for the library are displayed, and the user can adjust sliders to select molecules having, for example, cLogP between 1 and 3 and 0-2 hydrogen bond donors. Finally, ChemoDOTS generates three dimensional conformers in a ready-to-dock format for each compound.
As a retrospective example, the researchers return to the BRD4 case study we wrote about here. Starting from the amine-containing fragment and the sulfonamidation reaction, ChemoDOTS generated 5546 molecules in just 5 seconds, including all 17 of those previously identified.
This is a nice approach, and I believe the researchers are correct when they say that to the best of their knowledge “ChemoDOTS is the only freely accessible functional and maintained web server to combine the design of medchem-compatible virtual libraries with an integrated graphical postprocessing analysis.” They plan to continue improving it, for example by adding new commercial building blocks from other sources.
If I could make one suggestion, it would be to include new types of chemistries beyond the 58, which came from a paper published in 2011. In particular, C-H bond activation methodologies have made impressive strides in recent years. Adding these is all the more important given that, according to a recent analysis, about 80% of successful fragment-growing campaigns involved growth from a carbon atom. But even in its current form, ChemoDOTS looks to be a useful approach for growing focused chemical libraries around fragment hits. Let us know how it works for you!

20 May 2024

Screening MiniFrags by NMR

Small is becoming big. Five years ago we highlighted MiniFrags, consisting of just 5-7 non-hydrogen atoms; FragLites and MicroFrags soon followed. Screening these tiniest of fragments at high concentrations can thoroughly explore hot spots on a protein and identify favorable molecular interactions. But because they are so extraordinarily small, experimental methods for screening them have been mostly limited to crystallography. In a new J. Med. Chem. paper, Annagiulia Favaro and Mattia Sturlese (University of Padova) turn to the most venerable of fragment-finding methods, NMR.
The researchers started with the 81 reported MiniFrags and removed those with aqueous solubility less than 250 mM or without protons observable by NMR (such as phosphate). The remaining 69 fragments were dissolved directly in phosphate buffer, mostly at 1 M concentration, though lower solubility fragments were dissolved at 250 mM. Importantly, the pH of each sample was carefully adjusted to 7.1 to ensure that any signals correspond to MiniFrag binding and not to changes in experimental conditions.
As a test case, the researchers chose the antiapoptotic target BFL1. This protein is related to BCL2, the target of venetoclax, which was discovered using SAR by NMR. BFL1 has a hydrophobic cleft with five subpockets and has been studied by NMR. Like other BCL2 family members it is a difficult target, as we noted earlier this year.
The actual screen was done using chemical shift perturbation (CSP) detected by two-dimensional 1H-15N HMQC. Fragments were screened at 100 mM, a 5000-fold excess above the protein concentration. Hits were confirmed at 20 mM (more on that below). As with the library preparation, pH was carefully controlled.
At such high ligand concentrations, any impurities could become a problem: a 2% contaminant would be present at 2 mM. To weed these out, the researchers performed WaterLOGSY experiments. These only produce a signal at ligand to protein ratios much lower than 1000 to 1, so any hits could only come from impurities.
Even at high concentrations, CSPs caused by weak fragments are small, so the researchers developed an analysis method to identify those that shift more than at least one standard deviation from the average. CSPs can shift in any direction on a two-dimensional map, but any one protein-ligand interaction should shift signals in the same direction. Here is where the 20 mM confirmation experiment comes into play: a “cosine similarity” assesses whether two CSPs are in the same direction and thus likely to be real.
Screening BFL1 led to 53 hits, a hit rate of 78%, similar to crystallographic screens of MiniFrags against other targets. Forty percent of MiniFrags bound to multiple sites on the protein; only 11 (16%) bound to a single site. The five subpockets were each liganded by 6-17 MiniFrags. In subsequent experiments, the researchers were also able to measure binding of two different fragments to different pockets simultaneously, akin to SAR by NMR.
This is an interesting approach, but while fragments with >5 mM dissociation constants have been advanced to drugs, the utility of a 100 mM binder remains to be seen. That said, the technique could be a boon for understanding protein-ligand interactions, and I look forward to seeing it applied more broadly. In particular, screening the same set of MiniFrags on the same protein by NMR, crystallography, and computational methods could be quite informative.

13 May 2024

Fragments in cells, writ large

Earlier this year we highlighted work in which a dozen fragments were screened against cells to look for noncovalent binders across the proteome. A new paper in Science by Georg Winter and collaborators at the Austrian Academy of Sciences, Pfizer, and several other organizations ups the game by more than an order of magnitude, and uses machine learning to make predictions about fragments’ cellular destinations and binding partners. (See also Derek Lowe’s post here.)
The researchers started with 407 diverse fully functionalized fragments (FFFs), which as we previously discussed consist of a variable fragment coupled to a photoreactive group and an alkyne moiety that can be used to pull down any bound proteins using click chemistry. These were selected from a larger set of ~6000 FFFs available from Enamine. The FFFs were incubated at 50 µM with intact HEK293T cells, followed by ultraviolet crosslinking.
Next, cells were lysed and treated with a biotin-azide probe that reacts with the alkyne on the FFFs. Covalently modified proteins were captured on streptavidin resin and proteolytically digested. Tandem mass tag (TMT) proteomics, which we wrote about here, was used to identify captured proteins. Unlike earlier methods, the researchers did not pinpoint the specific fragment binding sites on proteins.
In total the researchers found 2667 proteins bound to one or more fragments, of which ~86% had no reported ligands. Both proteins and ligands varied considerably in promiscuity: some proteins bound to more than half of the FFFs, and some fragments bound to hundreds of proteins, while others bound only a few, or none. To look for specific interactions, the researchers focused on proteins bound by fewer than 10 different ligands.
Three protein-ligand interactions were analyzed in some detail: the kinase CDK2 (and other CDK family members), the adapter protein DDB1, and the solute carrier protein SLC29A1. In each case the researchers confirmed the results from their chemoproteomic screens. Follow-up studies with related molecules led to more potent derivatives, with a CDK2 inhibitor showing low micromolar activity in a biochemical assay and an SLC29A1 inhibitor showing micromolar activity in a cell-based assay.
The researchers also found patterns in their larger data set. Armed with 47,658 protein-ligand interactions, the researchers were able to use machine learning to start to predict which molecular features were associated with binding. They ranked fragments as promiscuous or nonpromiscuous and built a promiscuity model. Molecules with higher lipophilicity and a greater fraction of aromatic carbon atoms tended to be more promiscuous, but the model could correctly categorize compounds as promiscuous even if they had lower ClogP values, or nonpromiscuous even if they had higher ClogP values.
Beyond promiscuity, the researchers used machine learning to predict other behavior, such as subcellular localization. A relatively easy case was to predict which molecules would accumulate in lysosomes; these tended to be hydrophobic basic amines. More impressively, the researchers could predict fragments likely to bind to transmembrane transporters, RNA binding proteins, and even intrinsically disordered proteins. And this is just the start: they hope one day to predict “target proteins from an input chemical structure alone.”
Perhaps most exciting, all of the data and models are available for free at Ligand Discovery. You can explore the proteins bound across all 407 fragments, input one or more proteins and find ligands, predict whether any given FFF is likely to be promiscuous or not, and even “build a machine learning model on the fly to predict potential interactions.” 
Check it out and let us know your experience.

06 May 2024

Covalent fragments vs WRN

Last week Practical Fragments highlighted a covalent clinical compound from Vividion and Roche against the oncology target WRN. Another series of inhibitors against this protein are described in a recent Cancer Discov. paper by Gabriele Picco, Mathew Garnett, and collaborators at the Wellcome Sanger Institute, GSK, IDEAYA, and several academic institutes.
As we described in more detail last week, WRN is a synthetic lethal target for microsatellite instability (MSI) cancers. In contrast to the Vividion paper, which started by screening covalent fragments against cell lysates, here the researchers incubated purified WRN protein against each member of their covalent library (at 20 µM for 24 hours at 21 ºC) and analyzed the reactions by intact protein mass spectrometry. The fragment library was based around the methyl acrylate warhead, which, as we discussed a decade ago, has a narrower range of reactivities than more common acrylamides.
GSK_WRN1 was one of the prominent hits, with 81% modification. Tryptic digestion revealed that it modified C727, the same cysteine found by the Vividion researchers. Medicinal chemistry led to GSK_WRN3, with sub-micromolar activity in MSI SW48 cells. (Unfortunately no other details on the chemistry are provided; the paper states that these will be written up separately.)
GSK_WRN3 or a closely related compound were tested in a battery of assays and found to be inactive against three other helicases, which is not surprising given that C727 is unique to WRN. Chemoproteomic studies in cells also revealed the compound to be quite selective towards WRN vs other proteins. The compounds selectively inhibited MSI cancer cell lines and patient-derived organoids while sparing microsatellite stable (MSS) cell lines and organoids. One of the compounds showed activity in a mouse xenograft model.
In a useful public service, the researchers tested two previously reported WRN inhibitors, MIRA-1 and NSC617145, in the same set of several dozen cell lines and found that they were not only ineffective, they lacked selectivity for MSI cells over MSS cells. Although Dr. Saysno might object, I nominate these molecules to be added to the “Unsuitables” bestiary at the Chemical Probes Portal.
I do wish more details about the molecules were provided, especially the kinact/Ki values. It is interesting that GSK_WRN3 bears remarkable structural similarities to VVD-109063. IDEAYA recently announced that their collaboration with GSK has resulted in a development candidate targeting WRN, and it will be fun to see the full story emerge.

29 April 2024

Covalent fragments in the clinic: VVD-133214

Back in 2016 we highlighted a paper describing chemoproteomic screening of covalent fragments. That technology formed the basis of Vividion, which was acquired by Bayer in 2021. Now, a paper just published in Nature by Matthew Patricelli, Todd Kinsella, and collaborators at Vividion, Roche, and Universitat Autònoma de Barcelona describes one of the fruits to come from this platform.
The work stems from another promising recent approach to find oncology targets, synthetic lethality: searching for proteins that are essential in certain types of cancer cells but dispensable for normal cells, which might mean reduced toxicity. WRN is a DNA helicase that can clean up secondary DNA structures caused by expanded TA-dinucleotide repeats found in cancer cells with microsatellite instability (MSI), which is caused by mutations in DNA repair genes. Previous research had shown that knocking out WRN caused double-stranded DNA breaks and cell death in MSI-high (MSI-H) cancer cells but not normal cells, which do not have so many expanded TA-dinucleotide repeats. This has set off an industry-wide search for WRN inhibitors.
The researchers screened several thousand fragment electrophiles against cell lysates and found that some, such as VVD-109063, modified C727 of WRN. Although this cysteine is located some distance from the ATP binding site, functional activity studies with the pure protein found that the molecule was an inhibitor.
Optimization of VVD-109063 and related molecules found inconsistencies between results in lysates and intact cells. Some engaged C727 better in intact cells than lysates, others worse. Differences in cell permeability were ruled out by the fact that a cysteine on an unrelated protein was liganded to a similar extent in cell lysates and intact cells. The researchers speculated that, because cell lysates are diluted, they have lower ATP concentrations, and sure enough some molecules were less active in the presence of ATP while others were more active.
The team decided to focus on the second class. Optimization ultimately resulted in the clinical candidate VVD-133214. (Unfortunately details are not given; the paper does say these will be provided elsewhere).

A crystal structure of VVD-133214 confirmed covalent binding to C727, with the molecule in a hydrophobic pocket in a flexible “hinge region” of the protein. This causes a conformational rearrangement into a “closed” form, which presumably affects the catalytic activity of the helicase. Surprisingly, there are no hydrogen bonds between WRN and VVD-133214. This is highly unusual: a paper we discussed in 2021 found >90% of fragment-derived leads had at least one polar contact.
The kinact/Ki value is reported as being 4848 M-1s-1, which is on the low side for clinical-stage irreversible inhibitors. Like sotorasib, its potency seems driven by kinact, with the Ki being greater than > 15 µM. Consistent with this low inherent affinity, the molecule was inactive against the C727A mutant enzyme.
Much of the paper focuses on the biology, which is interesting but beyond the scope of this post. Suffice it to say that VVD-133214 is cytotoxic in MSI-H cells, where it causes G2 arrest and DNA damage, but inactive in microsatellite stable (MSS) cells. Oral dosing led to tumor regression in several MSI-H mouse models, including patient-derived xenografts.
This is a nice paper, though I look forward to a full account of the medicinal chemistry. In particular, vinyl sulfones are generally considered quite reactive, and I know of only one other clinical-stage molecule with this warhead. Presumably the cyclopropyl substituent was added at least in part to sterically block access to the electrophile.
Also, while the paper refers to VVD-133214 as “clinical-stage,” it appears neither on clinicaltrials.gov nor on Vividion’s website. The Roche website lists RG6457 as a phase 1 WRN covalent inhibitor partnered with Vividion, so perhaps this is the same molecule.
The paper ends by mentioning another clinical-stage WRN inhibitor from a different company, this one noncovalent. It notes that “this presents a rare opportunity to compare two small molecule oncology drugs targeting the same protein by different mechanisms,” and that using both could be useful in combating resistance. Practical Fragments wishes luck to these – and other drugs targeting WRN – helping patients quickly.

22 April 2024

The limits of published data

Machine learning (or, for investors, artificial intelligence) has received plenty of attention. To be successful you need lots of data. If you’re trying to, say, train a large language model to write limericks you’ve got oodles of text to draw from. But to train a model that can predict binding affinities you need lots of measurements, and they must be accurate. Among public repositories, ChEMBL is one of the most prominent due to its size (>2.4 million compounds) and quality, achieved through manual curation. But even here you need to be cautious, as illustrated in a recent open-access J. Chem. Inf. Model. paper by Gregory Landrum and Sereina Riniker (both at ETH Zurich).
The researchers were interested in the consistency of IC50 or Ki values for the same compound against the same target. They downloaded data for >50,000 compounds run at least twice against the same target. Activity data were compared either directly or after “maximal curation,” which entailed removing duplicate measurements from the same paper, removing data against mutant proteins, separating binding vs functional data, and several other quality checks. They used a variety of statistical tests (R2, Kendall τ, Cohen’s κ, Matthew’s correlation coefficient) to evaluate how well the data agreed, the simplest being the fraction of pairs where the difference was more than 0.3 or 1 log units, roughly two-fold or ten-fold differences.
The results were not encouraging. Looking at IC50 values, 64% of pairs differed by >0.3 log units, and 27% differed by more than 1 log unit. In other words, for more than a quarter of measurements a molecule might test as 100 nM in one assay and >1 µM in another.
Of course, as the researchers note, “it is generally not scientifically valid to combine values from different IC50 assays without knowledge of the assay conditions.” For example, the concentration of ATP in a kinase assay can have dramatic effects on the IC50 values for an inhibitor. Surely Ki values should be more comparable. But no, 67% of pairs differed by >0.3 log units and 30% differed by >1!
The situation improved for IC50 values using maximal curation, with the fraction of pairs differing by >0.3 and >1 log units dropping to 48% and 13%. However, this came at the expense of throwing away 99% of the data.
Surprisingly, using maximal curation data for Ki data actually made the situation worse. Digging into the data, the researchers found that 32 assays reporting Ki values for human carbonic anhydrase I, all from the same corresponding author, include “a significant number of overlapping compounds, with results that are sometimes inconsistent.” Scrubbing these improved the situation, but 38% of pairs still differed by >0.3 log units, and 21% differed by >1 log unit.
This is all rather sobering, and suggests there are limits to the quality of available data. As we noted in January there are all kinds of reasons assays can differ even within the same lab. Add in subtle variations in protein activity or buffer conditions and perhaps we should not be too surprised at log-order differences in experimental measurements. And this assumes everyone is trying to do good science: I’m sure sloppy and fraudulent data only make the situation worse. No matter how well we build our computational tools, noisy data will ensure they often differ from reality, whatever that may be.

15 April 2024

Detailing hot spots with atomic consensus sites

Practical Fragments has written frequently about hot spots, regions on proteins that are predisposed to bind ligands such as drugs. Determining whether a protein has a hot spot can help prioritize a target for screening, and one of the more established computational approaches to do so is FTMap, which we wrote about most recently just a couple months ago.
While FTMap can tell you whether a protein has one or more hot spots, it provides few further details, such as which regions might prefer a hydrogen bond donor or acceptor. This has now been addressed in a new J. Chem. Inf. Mod. paper by Sandor Vajda and collaborators at Boston University, Stony Brook University, and Acpharis. (Diane Joseph-McCarthy presented some of this work at the CHI DDC conference earlier this month.)
The original version of FTMap started with a collection of 16 very small molecule probes: these were docked all over a protein, with hot spots being identified as consensus sites where many probes bound. To get more information about each hot spot, the researchers have extended the method – now called E-FTMap – by increasing the number of probes to 119 covering key functional groups. For example, whereas FTMap included dimethyl ether as a probe, E-FTMap also includes 2-methoxypropane, 2-methoxy-2-methylpropane, and tetrahydropyran. If all these probes bind with the oxygen in the same part of the hot spot, this suggests a predilection for a hydrogen bond acceptor, and also provides information about nearby hydrophobic contacts.
By using a sufficiently diverse group of virtual probes, E-FTMap is able to more finely detail hot spots, tallying the “atomic consensus sites” within them. This is reminiscent of an approach we wrote about several years ago, though that method used just three different probes.
To benchmark E-FTMap, the researchers took 109 fragment-to-lead pairs with published crystallographic information and assessed whether the program could identify interactions that had been experimentally observed. The results were encouraging and far superior to the original version of FTMap. The highest ranked atomic consensus sites generally overlapped with appropriate atoms in fragments and leads. Interestingly, the results for fragments were better than those for leads, and the researchers suggest this is because the fragment “core is responsible for the bulk of the binding free energy in a ligand and that larger ligands bind by forming additional interactions at weaker hot spots that surround the fragment binding site.”
Next, E-FTMap was tested against five proteins for which between 31 and 353 fragment-bound crystal structures were available. Here too the program was broadly successful, though some fragments bound regions of the protein that E-FTMap overlooked, particularly in cases where there were conformational changes. This is not surprising given that the program assumes the protein remains rigid. (Other computational approaches such as SWISH, which we wrote about here, are starting to account for protein flexibility.)
E-FTMap looks qualitatively at specific atomic interactions, and one question I had was how well the atomic consensus sites matched up with binding affinities of known fragments; perhaps some crystallographically identified fragments bind so weakly one would not expect to find them computationally, as we discussed here and here. This hypothesis might be tested by focusing on comparisons with experimentally characterized fragments with the highest ligand efficiencies.
Also, I was struck by the fact that the virtual probes in E-FTMap are roughly the size of MiniFrags or MicroFrags, and I couldn’t help but wonder how well the atomic consensus sites from the virtual screens would correlate with the binding modes of these tiniest of fragments.
One nice feature of E-FTMap is that it can be accessed through a simple web server, so if you’re interested in these and other questions you can test it for yourself. If you do, please share your experiences.

08 April 2024

Nineteenth Annual Fragment-Based Drug Discovery Meeting

Last week the CHI Drug Discovery Chemistry (DDC) meeting was held in San Diego. This was the largest ever, with more than 900 participants, 95% of whom attended in person, up from 87% last year. I won’t attempt to cover all fourteen tracks but will just touch on some of the main themes.
Computational approaches
All four days of the conference featured dedicated sessions on machine learning and artificial intelligence, but since I was in other sessions I don’t know how relevant they were to FBLD. If you attended an interesting talk please let me know so I can watch it on-demand.
Among computational talks I did see, Antonina Nazarova (University of Southern California) provided an update on V-SYNTHES, which we first wrote about here. This synthon-based screening approach now covers 36 billion molecules and has been tested against eight different proteins, four of which yielded nanomolar hits when tested experimentally.
Computational methods have historically treated proteins as rigid, though many targets are anything but. Diane Joseph-McCarthy (Boston University) described an improvement to the pocket finding approach FTMap, called FTMove, to incorporate molecular dynamics by starting with an ensemble of different crystal structures. A further advance is E-FTMap, which expands the number of virtual probes from 16 to 119 to more finely assess ligandable sites.
Benjamin Walters (Genentech) described using protein dynamics to find cryptic pockets using ESP, or Experimental Structure Prediction. In this approach, experimental data from hydrogen-deuterium exchange (HDX) or chemical shift perturbations (CSPs) are used to constrain multiple parallel computational simulations, leading to better models than flexible docking, even for weak fragments.
Experimental approaches
Protein-detected NMR was the first practical fragment-finding method, and Steve Fesik (Vanderbilt) described using SAR by NMR to find fragments binding to the papain-like protease of SARS-CoV-2. These have been advanced to molecules with nanomolar affinity and activity in cell-based assays.
Andreas Lingel described the new fluorine-containing fragment library built at Novartis and how 19F NMR was used to generate inhibitors of IL-1β. We wrote about that success last year, noting that the initial fragment hit was “super-sized,” and Andreas confirmed that for trifluoromethyl-containing fragments the upper molecular weight limit was relaxed to 350 Da.
Sriram Tyagarajan (Merck) presented a crystallographic screen against the neurodegeneration target TTBK1 which yielded hits at 15 sites. Several potential allosteric sites were identified, but fragment growing and linking were not successful, leading them to a quick (3 month) no-go decision on the protein.
Virgil Woods (City University of New York) described using crystallographic screening to find hits against the challenging phosphatase PTP1B both under conventional cryogenic temperatures as well as at room temperature. As we noted about related work, there was a surprisingly poor overlap between the two sets of hits, and some fragments bound in a different manner at different temperatures.
Integrating FBDD and DNA-encoded libraries (DEL) for lead generation was the topic of Chaohong Sun’s talk. She noted that of some two dozen targets at AbbVie screened by both methods, 60% found hits from both, 10% found only fragment hits, and 5% found only DEL hits, with a quarter of the targets producing no hits. Hits from both approaches can be combined, as we noted here. Chaohong also noted that for both FBDD and DEL, high quality protein is essential for successful screens.
Covalent approaches
Covalent approaches to drug discovery are becoming ever more acceptable as more covalent drugs are approved. Understanding these in depth was the focus of Micah Niphakis (Lundbeck), who characterized 22 approved drugs containing 18 different warheads. The stability in buffer, liver microsomes, and hepatocytes varied dramatically, though more recently approved drugs tended to be more stable. Chemoproteomic studies revealed many off-targets in cells; for example, all the kinase inhibitors tested hit BTK to some extent even when this was not the primary target. The fact that the drugs are (mostly) safe and well-tolerated is a useful reminder that just because we can detect something doesn’t mean it is relevant.
Henry Blackwell described building a 12,000-member covalent fragment library at AstraZeneca. Due to the presence of a warhead, they relaxed rule of three parameters, with MW ranging from 250-400 Da and ClogP from 0-4. Henry also discussed the successful use of this library to identify covalent hits against the anticancer target BFL1 that were optimized to kinact/KI ~ 7000 M-1s-1. This accomplishment is all the more impressive given that screens using ASMS, DSF, 19F NMR, and SPR had all failed to yield validated hits.
We recently wrote about electrophilic MiniFrags, and György Keserű (Research Center for Natural Sciences, Hungary) described screening these against HDAC8 and the main protease from SARS-CoV-2. He also mentioned that the set is available for purchase from Enamine, so you can try it yourself against your favorite target.
As covalent modifiers become more common we will see new metrics for characterizing them, as illustrated by Benjamin Horning’s (Vividion) presentation, “Ligand Efficiency Metrics in Covalent Drug Discovery.” He described Ligand Reactivity Efficiency (LRE), defined as pTE50(target, 1 hr) – pTE50(glutathione, 1 hr), where TE is the target (or glutathione) engagement. LRE is analogous to LLE but focused on reactivity rather than lipophilicity. Despite my post last week, the metric could be useful, and I look forward to seeing what Dr. Saysno and friends will make of it.
Most covalent modifiers bind to a target and remain intact, but Nir London (Weizmann Institute) has developed Covalent Ligand Directed Release (CoLDR), in which a portion of the small molecule leaves; applications include release of fluorescent or chemiluminescent probes. Useable warheads include α-substituted methacrylamides and sulfamate acetamides.
Although more recent covalent drugs have targeted cysteine residues, there is growing interest in other amino acid side chains. Nir mentioned that thio-methacrylate esters can react with lysine residues, thought the kinetics are slow. And Carlo Ballatore (University of California San Diego) described hydroxy-naphthaldehyde fragments that bound reversibly to a lysine on the vascular target KRIT1.
Both plenary keynote speakers focused heavily on covalent chemistry. Dan Nomura (UC Berkeley) described using chemoproteomics approaches to find covalent molecules that could inhibit, degrade, or change the cellular localization of myriad proteins.
Finally, K. Barry Sharpless (Scripps), one of only five people to have been awarded the Nobel Prize twice, gave a rich description of sulfur (VI) fluoride exchange chemistry (SuFEx), which included drawing chemical structures on a flip chart. He presented the discovery of a fluorosulfate that is bactericidal against multiple resistant forms of Mycobacterium tuberculosis. Interestingly, the molecule works by modifying a catalytic serine residue which then cyclizes to form a β-lactam. His passion for chemistry is obvious, but he also has personal reasons for pursuing the second most deadly infectious disease: his brother died of tuberculosis before effective drugs were developed. And with the rise of extensively drug resistant TB, we’ll need new ones.
I’ll end on that note, but please leave comments. And mark your calendar for April 14-17 next year, when DDC returns to San Diego.

01 April 2024

Personality tests for molecules

Long-time readers of Practical Fragments will be familiar with various metrics for measuring molecules, such as LE, LLE, and WTF. But these are all hard-edged, numerical constructs. Some folks argue that we should take a softer, more nuanced approach. This call has been heeded by Katharine Bigg and Isabel Myerrors in the form of a “Myerrors-Bigg” Type Indicator, or MBTI.
The MBTI consists of a series of questions which rank a molecule into four dimensions: Extroversion/Introversion, Sociable/Nonsociable, Flat/Three-dimensional, and Pretty/Janky. Defining molecules as extroverts may sound strange, but it really just comes down to a question of molecular recognition: we’ve noted that 4-bromopyrazole seems to bind to just about every protein and is thus an Extrovert while other compounds, being Introverts, fall into the category of “dark chemical matter” and never come up in screens.
As for the other dimensions, Practical Fragments has written previously about (Non)Sociable fragments as well as Flat fragments. This leads to the last dimension. Claims that beauty is in the eye of the beholder are undermined by the rigorous process of the MBTI, which places molecules such as curcumin squarely in the Janky category while approved drugs are self-evidently Pretty. Thus, toxoflavin is an ESFJ, while sotorasib is an INTP.
The utility of the MBTI remains to be established, but this has not stopped companies everywhere from applying it in their acquisition and evaluation processes. And other tests, such as the Decagram of Personality and the Big Six Personality Traits, are also becoming popular. Which do you prefer?

25 March 2024

Fragments vs DHODH

Rapidly proliferating cancer cells require a steady supply of nucleic acids, and cutting that off is a potential therapy. The enzyme dihydroorotate dehydrogenase (DHODH), which is important for pyrimidine synthesis, is thus an interesting drug target. In a recent ACS Med. Chem. Lett. paper, Lindsey DeRatt, Scott Kuduk, and colleagues at Janssen describe their approach.
The researchers had previously used virtual screening and structure-based drug design to develop compound 1, which is potent in both biochemical and cell-based assays. However, the molecule is highly effluxed by P-glycoprotein, which can limit both oral bioavailability and brain penetration. Thus, they turned to fragments.
An SPR screen (about which sadly no details are provided) yielded compound 2, and crystallography revealed that the amide carbonyl makes a similar contact to tyrosine 356 (Y356) as does the carbonyl in the triazolone moiety of compound 1. Merging these led to compound 4, which was considerably more potent than compound 2 but much less so than compound 1. However, further optimization led eventually to compound 25. Although less potent in an enzymatic assay than compound 1, compound 25 is equally effective in cells. It also has excellent pharmacokinetics in mice and – importantly – a considerably lower efflux ratio.

Interestingly, when the researchers solved the crystal structure of a related molecule bound to DHODH, they found that the carbonyl no longer interacts with Y356 but is instead flipped 180º and interacts with a different residue. The researchers conclude by stating that they are designing new molecules to reengage Y356, which could further improve potency.
Several lessons emerge from this brief paper. First, the flipped urea moiety is another reminder that fragments do not always maintain their orientations, as also seen here, here, and here. Second, information from the fragment was used not to improve potency but rather to address other aspects of an existing lead series, as seen here and here. And finally, one could argue that the only critical feature of the fragment remaining in the final molecule is the NH of the urea. But the fragment did cause the researchers to examine their molecules from a different perspective, resulting in a better series. Perhaps you could call this an example of fragment-assisted drug discovery. As is so often the case, fragments can inspire new ideas that may otherwise be overlooked.

18 March 2024

Fragments vs SHP2

One of the success stories we highlighted in last week’s summary of Fragments 2024 was the discovery of a potent inhibitor of SH2 domain-containing protein tyrosine phosphatase 2 (SHP2). James Day and colleagues at Astex and Taiho have just published the full account in J. Med. Chem.
Previous studies had shown that blocking SHP2 might be effective in certain cancers, particularly those dependent on mutant KRAS. As its name suggests, however, SHP2 is a phosphatase. This class of enzymes has highly charged active sites, which makes drug discovery notoriously difficult (see here for example). Indeed, a crystallographic fragment screen of the isolated phosphatase domain produced just one hit.
Simultaneously, the researchers performed NMR and crystallographic screens of the full-length protein, which contains two SH2 domains. This campaign was much more successful, with 88 crystallographically validated fragment hits. (Interestingly, a thermal shift assay of the same construct came up empty.) As Astex has previously reported, secondary binding sites on proteins are common, and SHP2 is no exception, with fragments binding to five sites. However, the vast majority – 83 of 88 – bound to what is called the tunnel region between the phosphatase domain and one of the SH2 domains.
The researchers note that “following completion of our Pyramid fragment screen, Novartis independently reported several SHP2 inhibitors” binding to the same site, which must have been both validating and irritating. Indeed, the Astex researchers did work on fragments binding to other sites, advancing one to a low micromolar inhibitor. But it’s hard to ignore a hot spot with dozens of bound fragments, and the tunnel region became their primary focus. One fragment was optimized to a low micromolar inhibitor. Another, fragment 3, had measurable affinity by ITC and respectable ligand-efficiency, and this was taken the furthest.

We’ve written previously about the importance of water in molecular interactions, and here the researchers performed solvent mapping molecular dynamics to identify water molecules that could be advantageously engaged. Scaffold hopping led to compound 15, and crystallography confirmed that the pyridine nitrogen forms a hydrogen bond to a water molecule. Increasing the lipophilicity around the phenyl ring and adding a basic amine to engage an electronegative region of the protein led to compound 18, with nanomolar biochemical activity and low micromolar activity in cells. Further structure-based design ultimately led to compound 28, with sub-micromolar cell activity. This compound has low efflux, low clearance and excellent oral bioavailability. When dosed orally in mouse xenograft models the molecule significantly inhibited tumor growth.
The exo-diastereomer of compound 28, in which the primary amine is facing down instead of up, shows interesting differences. It has a similar pKa as well as similar biochemical and cell-based activity but is plagued by high efflux and poor oral bioavailability. The researchers suggest that “steric shielding of the tropane bridge or pharmacophoric differences in efflux transporter recognition” may be responsible. There was considerable discussion at Fragments 2024 as to the precise source of the differences, but whatever the cause, this pair serves as a useful reminder that pharmacokinetics may vary dramatically even between nearly identical molecules.
Clinical development of SHP2 inhibitors has slowed due to a variety of reasons, including apparent on-target toxicity, but this is still a nice fragment-to-lead success story. Perhaps, as with capivasertib, it will just take time to find the right clinical strategy and patients who can benefit from these molecules.

11 March 2024

Fragments 2024

Last week saw the first of four dedicated fragment meetings this year: Fragments 2024, the 9th RSC-BMCS Fragment-based Drug Discovery Meeting, was held in historic Hinxton Hall, Cambridge, UK. I won’t attempt to cover the 17 talks, 40+ posters, and 20 exhibitors in detail but just try to hit on some broad themes.
One highlight was a talk by Chris Swain, whose Cambridge MedChem Consulting has come up several times at Practical Fragments. Chris has been systematically cataloging fragment hits reported in the literature, and his database now includes >2500 fragments from >300 papers that hit 265 targets. This has not been easy: as we’ve noted in our annual F2L reviews, papers don’t always mention fragments in the title or abstract; sometimes you need to dig deep into the experimental methods to find out the origin of the initial hits, and even then there are questions of interpretation. Chris noted that the the drug aprepitat originated from a fragment-like pharamacophore extracted from a more complex literature compound. That story was published in 1998, predating the term “fragment-based drug discovery,” but perhaps it would be considered FBDD today.
The fragments themselves are a diverse bunch, with an average Tanimoto similarity of just 0.09, but there are small clusters. Looking at them in more detail, the ten most common scaffolds are aromatic (benzene, indole), which is a departure from approved drugs. There is also a significant fraction of charged molecules, including 298 acids and 348 basic groups. About 10% of the fragments hit more than one target, exactly what you would expect from the theory of molecular complexity.
Chris’s talk was followed by a wide-ranging panel discussion that expanded on some of these themes. Solubility was recognized as important, though with different techniques being more persnickety: Justin Dietrich (AbbVie) noted that pre-screening is critical for SPR, but for protein-detected NMR the protein is present at high enough concentrations to act as a “phase transfer reagent.”
The topic of thermodynamics also came up, with Chris Murray noting that Astex collects lots of ITC data but uses it for assessing free energy (ΔG) values rather than enthalpic energy (ΔH) values. Helena Danielson (Uppsala University) noted that the early correlation between compound quality and enthalpy found with HIV protease inhibitors did not seem to apply to other targets despite significant investment in collecting data at multiple companies, as also noted by Chris Smith (Mirati) and Mike Hann (GSK). Rod Hubbard (Vernalis) puckishly suggested that the study of ΔH had produced “more heat than light.”
The topic of MiniFrags also came up during the panel discussion. Chris Murray noted that they had been tried on quite a few targets but, as Rod Hubbard confirmed, were more helpful in identifying binding sites than providing starting points. But Chris Smith pronounced himself a “complete convert” after a MiniFrag identified an induced pocket on a previously intractable target where fragments (and other techniques) had failed.
Covalent fragments also made several appearances, with Jonathan Pettinger describing a phenotypic screen at GSK looking for compounds that block the pro-inflammatory M1 polarization of macrophages. After screening some 2000 covalent fragments they used chemoproteomics to determine that one of the best compounds acted by modifying cysteine 817 of the kinase JAK1. Interestingly, this is the same cysteine identified independently by researchers from Vividion, which could speak to the centrality of this target, the reactivity of this particular cysteine, or both.
Pursuing residues other than cysteine is seen as difficult, with Mike Hann noting in the panel discussion that these may require more extensive non-covalent interactions and Chris Murray noting that the warheads themselves were less attractive. But these challenges have not dissuaded Peter Cossar (Eindoven University of Technology), who has introduced cysteine-reactive disulfide and lysine-reactive aldehyde moieties into the same fragment to crosslink a 14-3-3 protein to substrate ERRγ.
Another theme was screening crude reaction mixtures in a “direct to biology” approach. Vernalis was an early adopter with their off-rate screening, and a talk by Lucie Guetzoyan confirmed that they are continuing to invest here not just with SPR but also with affinity-selection mass spectrometry and X-ray crystallography. Lucie also described using flow chemistry to enable sensitive organometallic chemistries such as Grignard and Negishi couplings. John Spencer (University of Sussex) is also using crude reaction screening by crystallography and thought the approach can compress ten years worth of work into a few months.
As with most conferences these days there were plenty of success stories. Martina Schaefer (Nuvisan) described the discovery of the Bayer SOS1 inhibitor BAY-293, which we wrote about here. Anna Vulpetti (Novartis) described the discovery of IL-1β inhibitors, which we wrote about here. Nicola Wilsher (Astex) described the discovery of potent SHP2 inhibitors; I’ll write more about these later. And Matthew Calabrese described the discovery of allosteric activators selective for the γ3 subunit of AMPK, which could avoid the cardiotoxicity seen with less selective molecules. Three HTS screens had failed but fragments ultimately led to a potent tool molecule. Interestingly, some of the HTS compounds were later found to be hits but had been overlooked because they were so weak that they did not rise above the noise of the assay.
Finally, Justin Dietrich described several success stories, including against TNFα (which we wrote about here) as well as CD40 ligand. Justin noted that FBLD is used alongside HTS and DEL at AbbVie, and that the techniques can be complementary – a theme noted by several others.
Despite being so intimately integrated with other discovery approaches, FBLD continues to innovate and evolve and remain sufficiently quirky that stand-alone meetings are still valuable and rewarding. I’m looking forward to seeing what the next several meetings reveal.

03 March 2024

The EU-OPENSCREEN fragment library

A well-curated fragment library is usually the starting point for fragment-based lead discovery, and not an insignificant investment. If you are just starting out you may want to use an existing library. One such option is described in an (open-access) paper in RSC Med. Chem. by Jordi Mestres and collaborators at IMIM Hospital del Mar Medical Research Institute and across Europe.
The EU-OPENSCREEN European Research Infrastructure Consortium (ERIC) allows researchers to access early lead discovery and chemistry resources. Among other components, it includes a set of more than 96,000 compounds for high-throughput screening, the European Chemical Biology Library, or ECBL. To complement this, the researchers have developed what they call the European Fragment Screening Library, or EFSL.
Recognizing that rapid follow-up is a critical next step in fragment-based lead discovery, the researchers designed EFSL based on ECBL. They did this by choosing fragments commercially available from Enamine that were sub-structures of ECBL members. Fragments were chosen to represent as much of the ECBL as possible, as well as for rule-of-three compliance. Fragments with multiple vectors for growing were also prioritized, similar to the “sociable fragments” concept we wrote about here. Finally, a set of 88 very small “minifrags” were also included.
Fragments were dissolved in deuterated DMSO at 100 mM (or 1000 mM for minifrags). Solubility and integrity were assessed at 1 mM (or 10 mM for minifrags) in PBS using 1H-NMR using an internal standard; those with solubility < 0.2 mM were rejected, as were those with missing or extra peaks in the NMR spectra. Of 1056 compounds tested, 913 passed these QC criteria.
The EFSL is available for screening (via grant applications), and the paper summarizes the results of eight screens performed over two years using a range of detection technologies including crystallography, ligand-detected NMR, small-angle X-ray scattering, thermal shift, and BLI. Hit rates ranged form just 0.1% to 31.3%, though in the last case only a small subset of the library was tested.
After fragment screening and confirmation, four of the projects tested larger compounds from the ECBL in follow-up studies, and two were able to identify hits. One project targeting a bacterial beta-ketoacyl-ACP synthase 2 (FabF) used BLI to identify a fragment with a dissociation constant of 35 µM. Of the 147 compounds related compounds from the ECBL, two had slightly higher affinity, albeit at the expense of lower ligand efficiency. Perhaps exploring Enamine REAL Space as in this example would be more effective at finding significantly more potent molecules.
In summary, the EFSL seems to be a useful resource, particularly for academic labs. If you’ve got a target and no internal fragment-screening capabilities, it might be worth putting in an application. 

26 February 2024

Fragments in the clinic: 2024 edition

It has been more than a year since our last list of fragment-derived clinical compounds. Since then capivasertib has been approved, bringing the number of marketed drugs to seven. There have also been a few other changes.
As always, this table includes compounds whether or not they are still in development (indeed, some of the companies no longer even exist). Because of this, the Phase 1 section contains a higher proportion of compounds that are no longer progressing. The full list contains 59 molecules, up slightly from 2022, with just under 40% approved or in active trials.
Drugs reported as still active in clinicaltrials.gov, company websites, or other sources are in bold, and the 37 that have been discussed on Practical Fragments are hyperlinked to the most relevant post. The list is almost certainly incomplete, particularly for Phase 1 compounds. If you know of others please leave a comment.


PexidartinibPlexxikonCSF1R, KIT
Amgen KRASG12C
VenetoclaxAbbVie/GenentechSelective BCL-2
Phase 3

Navitoclax (ABT-263)AbbottBCL-2/BCLxL
Pelabresib (CP-0610)
Phase 2

AT9283 AstexAurora, JAK2
IndeglitazarPlexxikonpan-PPAR agonist
MAK683NovartisPRC2 EED
Cullinan Oncology / Wistar
Phase 1

ABBV-744AbbottBD2-selective BET
ABT-518AbbottMMP-2 & 9
AT13148AstexAKT, p70S6K, ROCK
AZD5099AstraZenecaBacterial topoisomerase II
BI 1823911Boehringer IngelheimKRASG12C
BI 691751Boehringer IngelheimLTA4H
CFTX-1554Confo TherapeuticsAT2 receptor
HTL0014242Sosei HeptaresmGlu5 NAM
HTL0018318Sosei HeptaresM1-receptor partial agonist
HTL9936Sosei HeptaresM1-receptor partial agonist
NavoximodNew Link/GenentechIDO1

19 February 2024

Hot spots real and imagined

Practical Fragments has written several times about “hot spots”: regions on proteins where small molecules and fragments readily bind. Knowing whether your target protein has a hot spot can help you decide whether to pursue the target in the first place. A variety of computational approaches have been developed for finding hot spots, most of which start with a crystallographically determined structure. In a new J. Chem. Inf. Mod. paper, Sandor Vajda and collaborators at Boston University and Stony Brook University ask whether computational models of proteins can also be used for one of the more popular methods, FTMap.
The researchers started with a set of 62 proteins, each of which had a published crystal structure bound to a fragment (MW < 200 Da) as well as to a larger molecule. The predicted structures of these proteins were then downloaded from the AlphaFold2 (AF2) site, and these models were truncated to correspond to the residues seen in the crystal structures to facilitate comparisons. The computational models were quite similar to the experimental models, particularly when comparing the positions of the peptide backbone atoms which define the overall shape of the proteins.
Next, the researchers applied the program FTMap, which computationally explores the surface of proteins using a set of 16 very small probes such as ethanol. Hot spots are regions where lots of probes bind, and the “hotness” of these spots correlates with the number of bound probes. FTMap assessed hotness on the AF2 structures and the crystallographicaly determined structures. (Before running FTMap, the bound ligands in the crystal structures were computationally removed.) Additionally, the researchers ran FTMap on unliganded crystal structures for the 47 proteins where these had been reported.
FTMap was broadly successful at finding the hotspots defined by bound fragments, succeeding 77% of the time starting with either the fragment-bound or unliganded structures and 71% starting with the AF2 models. Implementing stricter criteria (demanding the experimental fragment binding site be the top hot spot, for example) reduced the success to 56% for the crystallographic starting points and 47% for the AF2 models.
The paper discusses several examples in detail, in particular the two where the AF2 models were most different from the experimental models. Both of these were large, multidomain proteins. When AF2 models of just the ligand-binding domains were used, the models were significantly improved. This seems to be a generally useful hack: generating truncated AF2 models for other proteins also improved the performance of FTMap.
The utility of AF2 models for docking has been the subject of some debate, with some arguing that even though the overall protein folds may be accurate, local side chain conformations may be wrong, and a single side chain rotation may make the difference between ligand binding or not. This paper suggests that hot spots are not too sensitive to these subtleties, and that AF2 models can be used for finding hot spots.

12 February 2024

Fragment screening across the proteome, noncovalently

Last week we discussed methodological improvements to industrialize covalent fragment screening across the proteome. While I’m a huge fan of covalent binders, their noncovalent counterparts are the vanilla ice cream of FBLD: also tasty and much more common. Back in 2017 we described how “fully functionalized fragments,” or FFFs, could be used to screen noncovalent fragments in cells. A new paper in Nat. Chem. Biol. by Christopher Parker and collaborators at Scripps and BMS further optimizes the approach.
FFFs contain, in addition to the variable fragment, a photoreactive group (often a diazirine) and an alkyne tag. When exposed to light the photoreactive group can react with nearby proteins and the alkyne tag can be used to fish out the proteins. In the new paper the researchers started with a dozen FFFs.
One challenge, which we discussed in 2021, is that the FFFs may react with many sites on a given protein. During analysis, a protein is typically digested into peptides for mass spectrometry. If a FFF reacts at several sites on a peptide the resulting spectra will be “chimeric” and more difficult to characterize.
The researchers developed methods to take these chimeric spectra into account when searching for sites of modification. The approach, called Dizco (for diazirine probe-labeled peptide discoverer) can identify three times as many peptides as standard approaches, as well as more detailed information on sites of modifications. 
Two pairs of FFF probes consisted of enantiomers, and these showed differential labeling across the proteome, consistent with specific molecular recognition. The researchers also confirmed binding of a few FFF probes to several proteins using a cellular thermal shift assay (CETSA).
In all, the probes modified 3603 peptides on 1669 proteins. The sites of modification were then mapped onto predicted or modeled three dimensional structures of the proteins. Importantly, and consistent with the 2017 work, most of the labeled sites were near predicted pockets. The researchers confirmed this for four proteins by showing that FFF probe binding could be competed by adding ligands known to bind to the pockets.
Next, the researchers docked (using AutoDock) their FFF probes onto 175 proteins (108 from structures in the Protein Data Bank and 67 from AlphaFold structures). They found that the docking experiments recapitulated the experimental data, and in fact often placed the diazirine tag near the protein residues found to react. Strikingly, and in another step forward for in silico approaches, docking against structures from AlphaFold was nearly as effective as those from the protein data bank.
As the researchers conclude, “we identified many binding pockets that have no reported ligands… these probes may serve as leads for further optimization.” It will be fun to see how far they go.

05 February 2024

Fragment screening across the proteome, industrialized

Last week we discussed covalent fragment screens against isolated enzymes, which can be very effective. But screening in cells or cell lysates preserves proteins in a more physiological environment and allows many proteins across the proteome to be screened simultaneously. In 2016 we wrote about covalent screens in human cell lysates which identified fragment hits for 758 cysteine residues in 637 proteins. Mass spectrometry techniques have improved since then in terms of both speed and sensitivity, as illustrated in a new Cell Chem. Biol. paper from Steve Gygi, Qing Yu, and collaborators at Harvard Medical School and Biogen. (Disclosure: Steve Gygi is on the Scientific Advisory Board of my current company, Frontier Medicines.)
The approach is called TMT-ABPP, or tandem mass tag activity-based protein profiling, and it involves multiple improvements to previous methods, some of which Steve discussed at the Discovery on Target meeting last year. Covalent fragments are added separately to cell lysate aliquots, after which a desthiobiotin iodacetamide (DBIA) probe is introduced. If a given site on a protein has reacted with a fragment, it will not be available to react with the DBIA probe.
Next, proteins are digested to peptides and labeled with TMT (tandem mass tag) reagents, which allow multiple samples (18 in this case, either individual fragments or DMSO-only controls) to be combined for simultaneous analysis. Peptides functionalized with the DBIA probe are captured on streptavidin resin while those that had previously reacted with a covalent fragment will not stick to the resin and be lost. Peptides eluted from the resin are then analyzed by mass spectrometry. The “competition ratio” between treated and untreated lysate gives a measure of how strongly a given site on a given protein is labeled by a fragment.
Multiple other tweaks, such as capturing proteins using magnetic beads and using a special type of mass-spectrometry (high-field asymmetric waveform ion mobility spectrometry, or FAIMS), further streamline the process to a 96-well plate format, with each well containing a mere 10-20 µg of cell lysate, as much as 100-fold less than earlier approaches.
The researchers benchmarked TMT-ABPP using three reactive “scout fragments,” including compound 1 from last week’s post. Collectively they identified 6813 cysteine residues hit by one or more of the scouts.
To demonstrate throughput, the researchers next screened 192 fragments, a third of which were acrylamides while the rest were chloroacetamides. Even with two controls for every 16 samples, this only required 12 injections on a mass spectrometer and resulted in hits against 38,450 cysteines, about 50-fold more than the 2016 paper. Proteins that were more highly expressed were better represented, as were proteins with known reactive cysteine residues, such as thioredoxins. Surprisingly though, surface-exposed cysteine residues were only slightly enriched over more buried cysteines.
The researchers also applied TMT-ABPP to five well-characterized covalent molecules, including the mutant KRASG12C inhibitor ARS-1620, which we wrote about here. In addition to the G12C site of KRAS, several other proteins were also liganded, including adenosine kinase (ADK). The researchers confirmed that ARS-1620 inhibited ADK in an enzymatic assay.
As the researchers note, “proteome-wide profiling of thousands of compounds remains a formidable challenge, both technically and financially.” This paper reveals how to significantly reduce the costs. By using such approaches, it is possible to build a catalog of fragment ligands for thousands of proteins. Doing so with a well-curated library could enable rapid fragment-to-lead campaigns.