Computational prediction of cryptic pockets

Last week we highlighted how NMR-based fragment screening was able to identify cryptic pockets on a small protein. Experimental methods are (still) the gold standard, but they take considerable effort and resources. In a new open-access J. Am. Chem. Soc. paper, Neha Vithani, David LeBard, and collaborators at OpenEye, Bayer, Genentech, and Khartis provide a computational approach.

 

The researchers defined six categories of cryptic pockets: 1) side-chain motion, 2) loop motion, 3) secondary-structure motion, 4) secondary-structure change, 5) interdomain motion, and 6) pre-existing deep pockets with no access to the surface of the protein. They collected a set of 21 proteins with 26 cryptic pockets overall (some proteins contain more than one) whose apo (unbound) and holo (ligand-bound) structures were available in the Protein Data Bank. This “Encore Dataset” contains all six types of cryptic pockets, though some are represented by only two or three examples.

 

To computationally identify cryptic pockets, the researchers used weighted ensemble molecular dynamics (WEMD) simulations on the apo structures. The details are quite technical but involve focusing on “normal modes” of protein movement. To find pockets, xenon is included in the simulation; these large atoms will fill pockets that open up during the course of the simulation, so detecting bound xenon reveals pockets.

 

WEMD successfully identified 24 of the 26 cryptic pockets in the Encore Dataset. Interestingly, both failures were for the same protein, hepatitis C polymerase, one a preexisting deep pocket and the other a secondary-structure change from order to disorder. (This latter category is exemplified in one of the cryptic pockets we discussed last week, in which crystallography revealed that a helix became disordered upon ligand binding.) The researchers hypothesize that these types of order-to-disorder transitions may be particularly difficult to detect computationally because the models are designed to identify secondary structures, thus overstabilizing them.

 

So WEMD is able to correctly predict most cryptic pockets in the Encore Dataset, but does it also predict cryptic pockets that don’t exist? Paul Samulsen famously quipped that economists had “predicted nine out of the last five recessions,” which actually looks pretty good compared to WEMD, which predicted more than 30 potential cryptic pockets on the protein biotin carboxylase. For comparison, the software program fpocket, which we wrote about last year, predicts 13 for this protein.

 

To rank which pockets actually exist, the researchers turned to another computational technique called Target X, which was described in this Research Square article earlier this year. This proved reasonably successful at ranking experimentally observed pockets near the top, with the top two predicted biotin carboxylase pockets corresponding to known pockets.

 

This is an interesting paper, but I am left with more questions. First, while the Encore Dataset is nice, why didn’t the researchers (also) use CryptoSite, a previously published set of 93 proteins with cryptic sites, which we discussed in 2024? More importantly, how can drug hunters decide whether to target a specific cryptic pocket? For instance, does WEMD and Target X allow one to assess whether cryptic pockets can support high-affinity ligands? These would be interesting questions for a follow-up paper.

Fragments find cryptic sites on ARID1B

We’ve written previously about the concept of synthetic lethality, in which changes to one protein make cancer cells more dependent on another. Inhibiting this second protein allows selective targeting of cancer cells while sparing normal cells. One such opportunity is the pair of proteins ARID1A and ARID1B, which assemble into a large complex that helps remodel chromatin to regulate transcription. ARID1A is mutated in some 9% of cancers, and ARID1B can partially compensate. CRISPR knockdown studies show that these cancer cells are dependent on ARID1B. ARID1B is huge (2236 amino acids!) and predicted to be mostly disordered – not the easiest target for small molecule drugs. However, a small 109-residue AT-Rich Interactive Domain (ARID), which binds to DNA, had previously been crystallized. This domain is the focus of a new Proc. Nat. Acad. Sci. USA paper by Michael Holliday and colleagues at Relay Therapeutics.

 

Computational assessment using Schrödinger’s SiteMap did not show any ligandable pockets, but molecular dynamics revealed that the N-terminal helix might open to reveal a shallow hydrophobic groove beneath it. Thus encouraged, the researchers performed a 2D NMR (¹⁵N-SOFAST-HMQC) screen of 480 fragments and obtained two hits, both with weak but measurable affinities. Interestingly though, the NMR signals are consistent with slow kinetics, suggesting that the molecules bind to rare conformations of the protein. Chemical shift perturbation (CSP) mapping showed that the two fragments bind near one another but not exactly at the same site.

 

Crystallography revealed that compound A-1 bound to a previously cryptic tunnel of ARID that forms by the displacement of a tryptophan side chain – a motion not seen in the molecular dynamics simulation. Preliminary medicinal chemistry improved the potency to mid-micromolar affinity.

 

Compound B-1 proved recalcitrant to crystallography, but cocrystallization with the more potent compound B-2 yielded a high resolution structure. This molecule did in fact bind where the N-terminal helix normally binds, as predicted by molecular dynamics; the helix itself became disordered.

 

But wait, there’s more! To find additional chemical matter, the researchers conducted a virtual screen of 150,000 compounds against ARID. Of the several dozen molecules tested by 2D-NMR, two showed CSPs similar to those caused by compound A-1, and their binding modes were confirmed by crystallography. But one of the molecules that showed CSPs similar to those caused by compound B-1 also caused CSPs in a different region of the protein, and crystallography confirmed binding to yet a third site.

 

In order for the compounds to be selective for cancer cells they need to be selective for ARID1B over ARID1A, which is 83% identical in the ARID domain. NMR experiments revealed that the molecule that binds to the third site binds with similar affinity to ARID1A, while compound A-2 binds about 5-fold less tightly. Happily, compound B-2 showed no binding to ARID1A even at 10 mM. Thus, the researchers have found three starting points for optimization, and evidence that selectivity is possible.

 

As the researchers acknowledge, the compounds will require “substantial additional optimization,” not least for affinity. Intriguingly though, compounds A-2 and B-2 do bind sufficiently close to one another that a fragment linking or merging approach may be warranted.

 

So in the end, ARID1B ARID presents two cryptic sites and a third more conventional (albeit challenging) site. The presence of multiple small-molecule binding sites is consistent with this paper from 2016, but still surprising in such a small protein. As we noted then, just because a site binds fragments does not mean it will support high-affinity ligands. Will ARID1B turn out to be like KRAS, which ended up being druggable, or like IL-1β, which has resisted high-affinity small molecules? The current paper provides good starting points toward answering this question.

Vast fields of biologically active chemical space

Exactly 17 years ago, I highlighted a paper from Brian Shoichet’s lab in which he hypothesized that only tiny regions of chemical space are biologically relevant. If so, Practical Fragments quoted, “a major reason why the screening of synthetic compounds ever finds notable hits is that our libraries are biased toward the sort of molecules that proteins have evolved to recognize.” The best scientists continually question their assumptions, and this is exactly what Brian, Bryan Roth, and collaborators at University of California San Francisco and University of North Carolina at Chapel Hill have done in a new open-access J. Med. Chem. paper.

 

The idea that molecules more closely resembling metabolites and natural products would be more likely to be biologically active is reasonable given the promiscuity of many naturally occurring molecules. As the researchers point out, dopamine signals through 5 receptors, while serotonin binds to 14. Nature is the ultimate recycler, constantly reusing chemical motifs.

 

But since 2009 there has been a massive growth of make-on-demand libraries, and these have increasingly diverged from known molecules. Brian has been among the most prolific explorers of this new chemical space, and as he noted in his keynote at DDC 2026, these gargantuan libraries are yielding more hits against more targets.

 

Are these hits more selective? In other words, are molecules that are less “bio-like” likely to bind to fewer targets? This is the question the new paper addresses, focusing on the 5-HT_2A serotonin receptor (5-HT_2AR), the target for a variety of drugs including psychedelics. The researchers have extensively studied this GPCR; we wrote about a successful computational screen here. In the new paper, the researchers compared hits from a previous “make-on-demand” (MoD) library screen of 1.6 billion molecules with a new screen of 3.5 million “in-stock” molecules. As predicted, the in-stock compounds were much more bio-like than the MoD compounds as assessed by several metrics (Tanimoto similarity, Avalon fingerprints, and RDK7 fingerprints, for the cheminformatics aficionados among you).

 

Both libraries were computationally screened using DOCK3.8 against 5-HT_2AR. Of the top scoring hits, 85 molecules from the in-stock library and 384 molecules from the MoD library were tested in a radio-ligand displacement assay. Hit rates were nearly identical at around 24% each. Functional assays revealed the hits to be active, with some acting as agonists while others were antagonists.

 

When tested against related GPCRs, specifically 5-HT_2BR and 5-HT_2CR, the in-stock molecules were more promiscuous than the MoD molecules. But when tested against a panel of 318 GPCRs there were no differences: the in-stock molecules bound on average 1.7% of the receptors while the MoD molecules bound on average 2.1%. The similar promiscuity persisted even when focusing on the 55 aminergic GPCRs (5-HT_2AR is an aminergic GPCR). In fact, the most promiscuous compound of all came from the MoD set and is distinctly not bio-like.

 

Of course, this is just one study, but I suspect it will generalize. In 2009 I questioned whether biologically relevant chemical space is really so sparse. As I wrote then, “consider a vast field of some crop that can only be harvested by night. There are lights scattered haphazardly throughout the field. One might expect that the crops immediately under the lamp posts would be harvested more intensively than crops in darker parts of the field, even if other areas are equally productive. In this scenario, the lamp posts reveal natural products and similar molecules, but much – or even most – of (unlit) chemical space may also be biologically active, it just hasn’t been sampled yet.”

 

There is grandeur in this view of chemical space, which is supported by the new paper. Imagine yourself in the midst of the field on a moonless night. Turn on your headlamp and step forward in any direction. Drug leads glimmer as far as your light can reach.

Twenty-First Annual Fragment-Based Drug Discovery Meeting

Last week some 875 people attended the CHI Drug Discovery Chemistry (DDC) meeting in San Diego. I can’t do justice to the 40 or so presentations I attended over four days but can highlight some of the main themes.

 

Reversible fragments

Membrane targets such as G protein-coupled receptors (GPCRs) pose a challenge for biophysical methods, but three talks presented progress. Matthew Eddy (University of Florida Gainesville) described high-resolution magic angle spinning (HRMAS) NMR, which entails spinning isolated cellular membranes containing GPCRs at high speed (4 kHz!), which miraculously yields sharp NMR signals for bound ligands. Matthew demonstrated applications with the human adenosine A_2A receptor and weak (mM) ligands. He noted that the technique can work with native, poorly expressed proteins, though data collection times can be upwards of 30 minutes.

 

Kris Borzilleri described using ¹⁹F NMR to find ligands against an orphan GPCR at Pfizer. The 2287 fragments screened yielded 87 hits, of which 38 confirmed by SPR. SAR studies eventually yielded low micromolar ligands, but these were difficult to advance in the absence of structure (see here for a more successful example from Merck).

 

Vanessa Porkolab (Eurofins Cerep) described using the Nanotemper Spectral Shift technology to screen 826 fragments against the adenosine A_2A receptor at 300 µM, with a 9.2% hit rate. Many of these ligands stabilized the GPCR in a thermal shift assay and seven were even active (as antagonists) in a cellular assay.

 

Turning to soluble proteins, Paola Di Lello presented a case study from Genentech and Vernalis applying ligand-observed NMR to the protein phosphatase PTPN22. Subsequent protein-observed NMR revealed that most of the 16 validated hits bound to two pockets some distance from the active site. The fragments were optimized to mid-micromolar affinity but showed no functional activity.

 

And Charlotte Hodson presented the eIF4E story from Astex. As we discussed last year, this yielded a low nanomolar ligand that did not have the desired cellular effects. Charlotte noted that subsequent genetic experiments were consistent with the limited efficacy. Still, the target was sufficiently interesting that a chemical probe would have been pursued even knowing it would be high-risk.

 

Covalent ligands

Covalent approaches made appearances throughout the conference. Keriann Backus (UCLA) described chemoproteomic approaches to find cysteine-targeting ligands; she noted that gain of cysteine residues (such as G12C in KRAS) are the most common missense variants in cancer. Keriann also warned how covalent compounds can cause potentially misleading effects in cells, as she described in Nat. Chem. Biol. last year.

 

In 2021 we wrote about the SpotXplorer fragment library from György Keserű (Hungarian Research Centre for Natural Sciences). György has now prepared a PhotoXplorer library, which uses diazirine tags for photochemical screening, which we described here. The new library has produced high hit rates across a variety of targets. György also described a new sulfozone-based photoprobe that is easier to prepare than diazirines.

 

Kelly Craft recounted a DNA-encoded library (DEL) screen at AbbVie against the target BCL2A1, also known as BFL1. This produced an aldehyde-containing low micromolar binder that formed an imine with buried lysine 102. Uncomfortable progressing an aldehyde, the researchers sought to covalently engage cysteine 55, the same cysteine targeted by AstraZeneca, as we wrote about here. The progression included at least one dual-warhead molecule which was crystallographically confirmed to bind both the lysine and cysteine. The effort ultimately yielded cysteine-selective leads.

 

Earlier this year I described the dDRTC method we developed at Frontier Medicines for determining k_inact/K_I, and Svetlana Kholodar presented a nice overview of its scope and utility. My colleague Johannes Hermann spoke in more detail about our covalent technologies, particularly those using AI.

 

Chemical space and the exploration thereof

Brian Shoichet (UCSF) gave an entertaining and wide-ranging account of “directed and random walks in chemical space.” Brian has consistently been on the bleeding edge of high-throughput in silico screens, from 67,000 compounds in 2009 to 138 million molecules in 2019 to 4 billion molecules today. When docking artifacts are avoided (as we discussed here), bigger libraries consistently produce more potent hits for more targets – an observation strikingly consistent with Alex Shaginian’s in 2023 as HitGen expanded their DEL libraries from billions to more than a trillion molecules. Brian is developing methods to computationally screen the >4 trillion make-on-demand molecules now available from companies such as Enamine.

 

Direct-to-biology (DTB) approaches, which rely on microscale chemical reactions screened without purification, have become increasingly popular methods for exploring chemical space. Jack Sadowsky correctly stated that Carmot was the first company formed around this approach; we previously wrote about the role Chemotype Evolution played in the discovery of sotorasib. Jack described how Kimia, which spun out of Carmot, has continued to advance the technology, applying it to find inhibitors selective for single members of closely related kinase families.

 

Allan Jordan described how Sygnature Discovery is applying DTB in a variety of assays including microsome stability and crystallography. (We wrote about crude reaction screening by crystallography earlier this year.) Expanding beyond DTB, Allan called their platform direct-to-discovery, and discussed how it led to a preclinical candidate with STORM Therapeutics in just 18 months.

 

WuXi Apptec is also using DTB. Peichuan Zhang described starting with ligands derived from fragment and DEL screens against the E3 ligase GID4 to make PROTACs to degrade BRD4; DTB was used to explore a wide range of different linkers. And Daniel Blair (St. Jude) described using DTB and affinity selection-mass spectrometry (AS-MS) to find new molecular glues for the oncology target LCK.

 

Computers, DEL, and DTB are not the only way to explore chemical space. Last year we covered Tom Kodadek’s bead-based screening approach at University of Florida Scripps, and Tom presented two talks on the topic, one using macrocycles to find binders to difficult targets such as PTP1B and one using small molecules to find molecular glues.

 

Speaking of PROTACs and glues, plenary keynote speaker Alessio Ciulli (University of Dundee) discussed the “evolution and future of targeted protein degradation.” Alessio noted that there are >25 PROTAC degraders and >10 glues in the clinic, though these collectively target only a small number of E3 ligands, so there is plenty of opportunity for the area to expand.

 

For many of us in industry, drugs represent the most privileged points in chemical space, and these often look quite different than we assume, as Dean Brown (Jnana) noted in his recent analysis of 104 oral small molecule drugs approved by the FDA from 2020 to 2024 (which we mentioned here). Some drugs contain eye-raising moieties such as acetylenes, styrenes, N-O bonds, and nitro groups. Indeed, it is worth remembering that venetoclax, arguably the most successful fragment-derived drug, sports a nitro group.

 

But before getting too complacent, Jonathan Baell (Manas) warned about frequent hitters in libraries of FDA-approved drugs. He notes in Eur. J. Med. Chem. earlier this year that many commercial libraries are actually enriched for molecules that cause spurious biological activity. Jonathan calls on library vendors to remove particularly egregious compounds, though I’d settle for world peace.

 

I’ll close on that pleasant thought, but please feel free to comment. I hope to see you in San Diego next year April 19-22 for the twenty-second iteration of DDC.

Review of 2025 reviews

Turning and turning in the widening gyre

The falcon cannot hear the falconer…

 

In our small, annual counterweight to Yeats’ “mere anarchy," Practical Fragments looks back on 2025.

 

This year marked the thousandth post on Practical Fragments, a milestone neither Teddy nor I imagined when the blog launched back in 2008. In terms of conferences, I wrote about CHI’s Drug Discovery Chemistry in San Diego here and Discovery on Target in Boston here.

 

For the past decade I’ve participated in annual J. Med. Chem. perspectives covering fragment-to-lead success stories, two of which published this year. The first, spearhead by Rhian Holvey at Astex, covers the year 2023, while Astex’s David Twigg took the lead on covering the year 2024. In addition to the tabular summaries for which these reviews are best known, both also include tables of “near misses,” none of which made the main tables because the starting points were (sometimes just slightly) too large. Four out of six of these heavyweights are covalent fragments, suggesting that the rule of three may need to be relaxed for these. The most recent paper also includes a table showcasing the eight approved FBLD-derived drugs.

 

Two more general publications are also of interest. In a brief (open-access) editorial in J. Med. Chem. Weijun Xu and Congbao Kang at A*STAR summarize fragment-finding methods and approved drugs and discuss future applications of FBLD in PROTACs and targeting RNA. And in Curr. Res. Pharm. Drug Discov., Geoffrey Wells, Exequiel Porta, and colleagues at University College London present a “graphical review” which covers library design, screening strategies, hit validation, fragment optimization, and a few case studies of approved drugs, along with current challenges.

 

In Drug Des. Devel. Ther., Bangjiang Fang and colleagues at Shanghai University of Traditional Chinese Medicine present an open-access bibliometrics analysis of 1301 fragment-based drug design papers published between 2015 and the end of 2024, which includes top ten lists of institutions, authors, and papers as well as keyword trends and analyses. Annual growth has averaged 1.4%, and the field is both global and collaborative, with 35% of publications involving more than one country.

 

Targets

Two reviews focus on oncology. The first, in Bioorg. Chem. by Milind Sindkhedkar and collaborators at Manipal College of Pharmaceutical Sciences and Lupin Ltd., briefly covers the history and practice of FBDD before providing short summaries of seven of the eight approved drugs to come from it. The second, published open-access in Chem. Rev. by Vanderbilt’s Steve Fesik, is a concise and highly readable introduction and account of the author’s groundbreaking work on BCL-2 family proteins, KRAS, and WDR5.

 

A much longer open-access review in Chem. Rev. by Paramjit Arora and colleagues at New York University covers protein-protein interactions (PPIs). Much of the focus is on larger molecules such as cyclic peptides, peptide mimetics, and other macrocycles, but there are summaries of fragment-based approaches against KRAS and 14-3-3 proteins.

 

The topic of 14-3-3 proteins is treated more fully in an open-access Acc. Chem. Res. paper by Michelle Arkin and colleagues at UCSF. While the focus of most efforts against PPIs is to find inhibitors, for 14-3-3 the goal is to find stabilizers, or molecular glues. The Arkin lab and others have been succeeding using various approaches, particularly disulfide tethering. We wrote about these efforts most recently in 2023, and the new review provides a nice update.

 

Fragment finding methods and libraries

Sahra St. John-Campbell and Gurdip Bhalay, both at The Institute of Cancer Research, published a massive open-access perspective on “target engagement assays in early drug discovery” in J. Med. Chem., covering a host of biochemical, biophysical, and cell-based assays. A table lists more than 50 different techniques, almost half of which are applicable to FBLD. Each row shows what characteristic(s) are measured as well as critical requirements for  protein, sample, and equipment. The paper is also beautifully illustrated with dozens of figures: one shows which techniques are most useful for different types of targets, and each method gets its own diagram.

 

A more focused open-access review is provided by Stefanie Freitag-Pohl and colleagues at Durham University in Biophys. Rev. After surveying various biophysical techniques, the researchers focus on spectral shift analysis, and in particular the Dianthus instrument from NanoTemper Instruments. This plate-based, high-throughput microfluidics-free instrument can detect changes in fluorescence caused by environment or temperature. Examples demonstrate affinity measurements across several orders of magnitude, up to double-digit millimolar, and a nice scheme shows use of the Dianthus in a fragment-screening workflow.

 

Moving to specific techniques, Jia Gao, Ke Ruan, and colleagues at University of Science and Technology of China Hefei provide an open-access survey of “the rise of NMR-integrated fragment-based drug discovery in China” in Mag. Res. Lett. After a brief overview of NMR approaches, they cover case studies from China, most of which are focused on fragment screening rather than optimization.

 

A less common biophysical method is native mass spectrometry (nMS), the subject of an open-access opinion in RSC Med. Chem. by Louise Sternicki and Sally-Ann Poulsen at Griffith University. This is a good survey of the approach; we highlighted a more fragment-focused review by the same authors last year.

 

The most common fragment-finding approach, X-ray crystallography, is covered in two open-access reviews. The first, in Acta Cryst. F by Sarah Bowman and collaborators at University of Buffalo and Brookhaven National Laboratory, focuses on critical early stages, from protein characterization to sample preparation and various crystallization approaches. The second, in Curr. Opin. Strut. Biol. by Martin Noble and colleagues at Newcastle University, starts by briefly reviewing crystallographic fragment screening before turning to fragment libraries. The paper includes a nice table summarizing publicly available libraries at major synchrotrons, with the text describing these in more detail.

 

The provider of one of these libraries, EU-OPENSCREEN, is the subject of an open-access review in SLAS Discov. by Robert Harmel and collaborators at EU-OPENSCREEN ERIC and Fraunhover ITMP. As we wrote last year, EU-OPENSCREEN is a broad consortium whose mission is to advance early drug discovery by providing access to technology and expertise. The new paper summarizes the four compound collections, including the European Fragment Screening Library (EFSL), and surveys progress to date. It also lays out ambitious plans, including expanding to >30 sites in nine countries.

 

Computational approaches and cryptic sites

Despite the hype about artificial intelligence in the broader world, AI in fragment-based drug discovery has been less common. In Curr. Opin. Struct. Biol., Woong-Hee Shin and colleagues at Korea University College of Medicine summarize applications to fragment growing, merging, and linking. The open-access paper includes a handy table of 13 programs, and includes GitHub links where available.

 

Cryptic binding sites, defined by Ehmke Pohl and collaborators at Durham University and Cambridge Crystallographic Data Centre “as binding pockets that exist in the ligand-bound state of a protein but not in its apo form,” are the focus of an open-access review in Bioinform. Adv. The researchers cover earlier computational approaches for finding these, especially molecular dynamics (MD) and machine learning (ML). They note that a key challenge for ML is the limited quantity and quality of experimental data: undiscovered cryptic sites would be misclassified as non-binding sites.

 

Yowen Dong, Ge-Fei Hao, and colleagues at Guizhou University review “computational methods for identifying cryptic pockets” in Drug Discov. Today. As with the previous review, these are divided between molecular dynamics and AI-based techniques, which are discussed individually and then compared. The researchers apply six approaches to the model bacterial protein TEM-1 β-lactamase and find that, for this highly studied single protein, the AI-based methods are much faster (seconds instead of days) and just as accurate, though MD-based methods provide more insight into formation mechanisms of cryptic pockets.

 

Covalent ligands

Allosteric sites are an important sub-class of cryptic pockets, and in J. Med. Chem. Jianing Li and colleagues at Purdue University discuss covalent allosteric inhibitors. After briefly discussing advantages of covalent molecules, they review examples targeting protein phosphatases, kinases, and GTPases, such as KRAS.

 

Of course, covalent molecules are not limited to allosteric sites. An open-access review in Bioorg. Med. Chem. Lett. by Walaa Bedewy, John Mulawka, and Marc Adler at Toronto Metropolitan University summarizes published covalent protein ligands, grouping them by target site:  active sites, residues adjacent to an active site, protein-protein interfaces, cofactor binding sites, and allosteric sites.

 

Chem. Rev. published two massive reviews on covalent ligands, each with more than 300 references. The first, by Tomonori Tamura, Masaharu Kawano, and Itaru Hamachi at Kyoto University, covers a wide range of topics, from covalent drugs, to peptide- and protein-based covalent inhibitors, to chemical biology labeling and target engagement strategies, to covalent bifunctional molecules such as PROTACs and radionucleotide-based molecules, and even covalent modification of DNA and RNA. The paper includes 68 figures, many reproduced from the original publications.

 

The second (open-access) Chem. Rev. paper, by Ku-Lung Hsu and colleagues at University of Texas at Austin, focuses on covalent ligands targeting protein residues other than cysteine, particularly lysine and tyrosine; we highlighted some of Hsu’s work recently. The paper also discusses naturally occurring molecules that bind to lysine, such as pyridoxal phosphate and aldose sugars.

 

Methods for finding covalent ligands are the focus on an open-access review in JACS Au by Mengke You, Hong Liu, and Chunpu Li at Shanghai Institute of Materia Medica. Specifically, they review disulfide tethering, activity-based protein profiling (ABPP), covalent DEL, phage and mRNA display, and sulfur(IV) fluoride exchange (SuFEx), with examples for each.

 

The last paper on this topic, in J. Med. Chem., offers a brief but important overview of all covalent FDA-approved small molecule drugs through 2023. Samuel Dalton and collaborators at Isomorphic Laboratories and Merck counted 128 covalent drugs, about 7% of all small molecule drugs. More than half are antibiotics, and more than 85% target serine or cysteine. Only 10% are reversible, but this number is rapidly increasing, with 11 of the 13 reversible covalent drugs approved since 2010. Importantly, the names, chemical structures, indication, target and target residue, warhead, and key references for all the drugs are provided in the supporting information.

 

Miscellaneous

Deconstruction of ligands to smaller fragments that are then “reconstructed” into new leads is a venerable approach in FBLD and the subject of an open-access perspective in J. Med. Chem. by J. Henry Blackwell, Iacovos Michaelidies, and Floriane Gibault at AstraZeneca. Multiple examples dating as far back as the late 1990s are provided, along with appropriate caveats about potential changes in fragment binding modes and protein conformations.

 

Finally, an open-access perspective in J. Med. Chem. by Dean Brown (Jnana Therapeutics) examines the 104 oral drugs approved from 2020 through 2024, including structures, dosing, pharmacokinetics, and safety. Roughly a third of these drugs are dosed more than once per day, and almost a quarter have a black box warning, while 42% have at least one contraindication. Dean warns that “overly prescriptive [development candidate] criteria may inadvertently stifle the development of innovative drugs,” and that it is difficult but important “to be the champion for a compound that others perceive as ‘un-drug like.’” The growing success of covalent drugs illustrates that some organizations are taking this to heart.

 

And that’s it for 2025. Thanks for reading and special thanks for commenting. And in 2026, may the best of us be filled with passionate intensity.

Searching monstrously large chemical space with FrankenROCS

Back in 2023 we highlighted a computational fragment linking/merging approach which was used to find high nanomolar inhibitors of the SARS-CoV-2 macrodomain (Mac1), a COVID-19 target. However, those molecules contained carboxylic acids, often associated with poor cell permeability. In a new open-access Sci. Adv. paper, James Fraser and collaborators at UCSF, Relay Therapeutics, Enamine, and Chemspace describe a related approach to find new, non-charged inhibitors.

 

The new approach, called FrankenROCS, “takes pairs of fragments as input to query a database using the rapid overlay of chemical structure (ROCS) method of comparing 3D shape and pharmacophore distribution;” the goal is to find larger molecules that most closely resemble the initial fragment pairs. As with the previous publication, the team started with more than 200 crystallographic fragment hits published in 2021. A set of 7,181 pairs of adjacently-bound fragments were searched against 2.1 million compounds commercially available from Enamine. The top 1000 were inspected, and 39 were purchased and soaked into crystals of Mac1. This led to 10 successful structures, of which AVI-313 did not contain a carboxylic acid. This molecule had weak but measurable activity in an HTRF competition assay.

 

Two million compounds is a lot but pales in comparison to Enamine’s “make-on-demand” REAL space, which at the time this research was done consisted of more than 22 billion molecules. The REAL space molecules are constructed from 960,398 building blocks that can be combined using 143 reactions. We previously described an approach called V-SYNTHES to screen Enamine’s REAL space. FrankenROCS takes a different active-learning approach called Thompson Sampling, which dates back nearly a century.

 

Imagine two sets of 1000 building blocks, R1 and R2, which could be coupled to generate 1,000,000 molecules. Rather than searching all possibilities, each R1 building block is linked to three random R2 building blocks, and each R2 building block is linked to three random R1 building blocks. These are virtually screened, and the R1 or R2 building blocks from those with the highest scoring compounds are used for further iterations. In theory, after tens of thousands of iterations, the best compounds will have been identified.

 

The researchers fed 97 fragment pairs from the 2021 paper into Thompson Sampling FrankenROCS to find molecules that would best overlay with the fragment pairs.  Ultimately 32 compounds were purchased, six of which were successfully crystallized with Mac1. Unfortunately, the most potent was a weaker inhibitor than AVI-313 and contained a carboxylic acid. The researchers speculate that the inability to find better molecules in larger chemical space may have stemmed from limitations of the scoring function, a problem we’ve previously discussed.

 

The researchers returned to focus on AVI 313, making substitutions at multiple positions, ultimately synthesizing 148 compounds, 121 of which could be characterized crystallographically. Importantly, several compounds had low micromolar activity, even without a carboxylic acid. The crystal structures show the binding site to be somewhat flexible, as evidenced by side chain and main chain movements to accommodate some of the binders.

This is a nice, thorough investigation, and the 137 protein-compound crystal structures deposited into the protein data bank provide useful training data for next-generation computational approaches. Moreover, the fact that immeasurably weak fragments can be advanced to low micromolar, ligand-efficient hits is yet another reason for the research community to figure out how to make crystallographic fragment screening data more widely available, as we exhorted here.

Keeping molecular dynamics cool for fragments

Accurately and reliably predicting fragment binding modes would be preferrable to doing messy, expensive, and sometimes tedious experimental work, but we’re not there yet. One of the biggest problems is that, because fragments usually bind weakly to proteins, it is hard to tell which of several possible binding modes is most favorable. In an open-access J. Chem. Inf. Model. paper published earlier this year, Stefano Moro and colleagues at University of Padova report progress.

 

Their approach, called Thermal Titration Molecular Dynamics (TTMD), analyzes short molecular dynamics simulations across increasing temperatures; if the ligand remains bound to the protein, this indicates a more stable binding mode. (It seems a bit like the dynamic undocking we wrote about here.) The researchers had previously reported good results for larger, drug-sized molecules, but not for four fragment-protein complexes.

 

Recognizing the low affinities of fragments, the researchers decided to lower the (virtual) temperatures. Rather than heating from 300 to 450 K, they heated from 73 to 233 K; ie, from just below the boiling point of liquid nitrogen to a moderately cold winter’s day in Minnesota. They first docked fragments using PLANTS-ChemPLP, which is free for academics, and chose the five best-scoring poses for evaluation.

 

Next, the researchers performed TTMD. There are several different ways to assess how well the ligand remains bound to the protein over the course of a molecular dynamics simulation, and four different scoring methods were chosen. When TTMD was tested on the four fragment-protein complexes that had previously failed, at least two of the scoring methods correctly identified the crystallographic binding mode for three of the fragments.

 

Thus encouraged, the researchers tested ten more compounds bound to six new proteins. The results were quite encouraging, with up to 86% of crystallographic binding modes being correctly identified by at least one of the scoring functions in TTMD vs 50% for docking alone. Impressively, two of the examples were MiniFrag-sized, with just 6 or 7 non-hydrogen atoms, yet the crystallographic pose was identified as the lowest energy in all four TTMD scores.

 

This is nice work, but the question arises how these specific ligands and proteins were chosen. Several years ago we highlighted a curated set of 93 protein-ligand structures that were used to benchmark other virtual approaches, and it would be nice to see how TTMD performs on these. Still, TTMD’s performance on its chosen examples is encouraging, and laudably the researchers have made their code freely available. If you try it out, please let us know how it works in your hands.

Small and simple, but novel and potent

Back in 2012 we wrote about GDB-17, a database of possible small molecules having up to 17 carbon, oxygen, nitrogen, sulfur, and halogen atoms, most of which have never been synthesized. Although novelty isn’t strictly necessary for fragments, as evidenced by the fact that 7-azaindole has given rise to three approved drugs, it’s certainly nice to have. In a new (open-access) J. Med. Chem. paper, Jürg Gertsch, Jean-Louis Reymond, and colleagues at the University of Bern synthesize fragments that had not been previously made and show that they are biologically active.

 

When you start drawing all possible small molecules you get lots of weird stuff, including an explosion of compounds containing multiple three- and four-membered rings, which may be difficult to make. The researchers wisely focused on “mono- and bicyclic ring systems containing only five-, six-, or seven-membered rings.” They further limited their search to molecules containing just carbon and one or two nitrogen atoms (as well as hydrogen, of course). Systematic enumeration led to 1139 scaffolds, ignoring stereochemistry, of which 680 had not been previously reported in PubChem. Out of these, three related scaffolds were chosen for investigation.

 

Computational retrosynthesis was used to devise routes to the three bicyclic scaffolds, and these were successfully synthesized, along with mono-benzylated versions, for a total of 14 molecules (including stereoisomers), all rule-of-three compliant. The online Polypharmacology Browser 2 (PPB2) was used to predict targets, and several monoamine transporters came up as potential hits. The molecules were tested against norepinephrine transporter (NET), dopamine transporter (DAT), serotonin transporter (SERT), and the σ-R1 receptor in radioligand displacement assays. None of the free diamines were active, but several of the benzylated compounds were, in particular compound 1a.

 

Compound 1a was initially made as a racemic mixture, and when the two enantiomers were resolved (R,R)-1a was found to be a mid-nanomolar inhibitor of NET while (S,S)-1a was 26-fold weaker. Compound (R,R)-1a was also a mid- to high nanomolar inhibitor of σ-R1, DAT, and SERT. Pharmacokinetic experiments in mice revealed that the molecule had poor oral bioavailability but remarkably high brain penetration and caused sedation. The researchers conducted additional mechanistic studies beyond the scope of this blog post and conclude that (R,R)-1a could be a lead for “neuropsychiatric disorders associated with monoamine dysregulation.”

 

There are several nice lessons in this paper. First, as we noted more than a decade ago, there is plenty of novelty at the bottom of chemical space. Moreover, and in contrast to our post last week, even small fragments can have high affinities. But novelty comes at a cost: synthesis of compound 2a required eight steps from an inexpensive starting material with an overall yield of just 9%, though this could certainly be optimized. Nonetheless, particularly for CNS-targeting drugs which usually need to be small in order to cross the blood brain barrier, the price might be worth paying.

 

Of course, even within this paper there are hundreds more scaffolds to look at than the three tested, and perhaps the researchers were lucky that their choices were biologically active. As computational methods continue to advance, it will be worthwhile turning them loose on GDB-17.

Fragments beat lead-like compounds in a screen against OGG1

The twin rise of make-on-demand libraries and speedy in silico docking has supercharged fragment screening and optimization: we’ve written previously about V-SYNTHES, Crystal Structure First and a related method. Another advance is described by Jens Carlsson (Uppsala University) and a large group of multinational collaborators in an (open access) Nat. Commun. paper.

 

The researchers were interested in 8-oxoguanine DNA glycosylase (OGG1), a DNA-repair enzyme and potential anti-inflammatory and anticancer target. They started with a crystal structure into which they docked 14 million fragments (MW < 250 Da) or 235 million lead-like molecules (250-350 Da) from ZINC15. Multiple conformations and thousands of orientations were sampled for each molecule. In all, 13 trillion fragment complexes and 149 trillion lead-like complexes were evaluated using DOCK3.7, a process that took just 2 hours and 11 hours on a 3500 core cluster.

 

After removing PAINS and molecules similar to previously reported OGG1 inhibitors, the top-scoring 0.05-0.07% molecules from each screen were clustered and, after manual evaluation, 29 fragments and 36 lead-like compounds were purchased from make-on-demand catalogs. These were tested at 495 µM (for fragments) or 99 µM (for larger molecules) in a DSF screen. None of the lead-like compounds significantly stabilized the protein, while several fragments did. Four of the fragments were successfully crystallized with OGG1, and in all cases the key interactions predicted in the computational screens were confirmed in the actual crystal structures.

 

Compound 1 showed the greatest stabilization of OGG1 (2.8 ºC) and some inhibition in an enzymatic assay, but not enough to calculate an IC₅₀. Searching for analogs that contained compound 1 as a substructure in the Enamine REAL database of 11 billion compounds produced few hits, but, as before, thinking in fragments proved fruitful. Searching for molecules containing just the core heterocycle and amide (colored blue below) yielded nearly 43,000 possibilities. Docking these and making and testing a few dozen led to compound 5, with mid-micromolar inhibition. Further iterations led to low micromolar compound 7.

At this point the researchers turned from make-on-demand libraries to synthetically accessible virtual libraries to fine-tune the molecule. After docking 6720 virtual molecules, they synthesized and tested 16, of which 12 were more potent than compound 7, with five of them being submicromolar. Compound 23 showed low micromolar activity in two different cell assays and was selective against four other DNA repair enzymes.

 

The same high-throughput docking approach was applied to three other protein targets: SMYD3, NUDT5, and PHIP. In each case crystal structures of bound fragments were available to use as starting points. Multiple compounds with improved docking scores compared to the initial fragments were identified, though no compounds were actually synthesized and tested.

 

The success in finding compound 1 demonstrates experimentally the advantage fragments have in efficiently searching chemical space. The researchers note that 97% of the >30 billion currently available make-on-demand compounds have molecular weights >350 Da, while only 50 million are < 250 Da. Screening all of these fragments in silico is possible; screening everything, less so. Although the fragment hits for OGG1 were weak, this isn’t always the case, as noted here. The fact that fragment 1 could be advanced to a sub-micromolar inhibitor after synthesizing just a few dozen molecules also testifies to the efficiency of in silico approaches.

 

The paper contains lots of useful details and suggestions for streamlining the process and is well worth perusing if you are trying to find hits against a structurally-enabled protein.

Stitching together fragments with Fragmenstein

As we noted just last week, crystallography has unleashed a torrent of protein-ligand complexes, especially fragments. Historically a single structure might be used for fragment growing, but so many structures present an embarrassment of riches, with sometimes dozens of fragments that bind in the same region. Merging or linking these fragments can be done manually, as seen here and here, but how to do so when the binding modes are partially overlapping is not always intuitive. In a new open-access J. Cheminform. paper, Matteo Ferla and colleagues at University of Oxford and elsewhere describe an open-source solution called Fragmenstein.

 

We briefly described Fragmenstein in 2023, where it was used to combine pairs of low-affinity fragments bound to the Nsp3 macrodomain of SARS-CoV-2 to generate sub-micromolar inhibitors. The current paper describes the platform in detail.

 

Fragmenstein starts by taking two (or more) structures of fragments bound to a protein and virtually combining them. This is done by collapsing rings to their individual centroids, stitching these together along with their substituents, and then re-expanding the ring(s) so the substituents will be close to where they were in the initial fragments. This process produces a surprising array of molecules beyond the obvious. For example, if one fragment contains a phenyl ring and the other fragment contains a furan ring, the stitched molecule might just contain the phenyl (if the two rings bind in nearly the same position), a benzofuran (merging the rings), a phenyl ring linked to a furan by one or more atoms, or even a spiro compound if the rings are perpendicular to one another.

 

In silico approaches sometimes suggest molecules that are synthetically challenging to make, but Fragmenstein can also be used to find purchasable analogs.

 

Next, the new molecules are energetically minimized, first by themselves and then while docked into the protein. In contrast to other docking programs, which might allow molecules to sample thousands of different conformations and sites in a protein, Fragmenstein maintains the new molecule in a similar position and orientation to the initial fragments, with the assumption that these have already identified energetically favorable interactions.

 

The researchers successfully applied Fragmenstein retrospectively to several targets. The COVID Moonshot (which we discussed here) crowd-sourced molecule ideas for the SARS-CoV-2 main protease based on structures of bound fragments. Of 87 ligands that had been crystallographically characterized and were designed based on two fragments, Fragmenstein successfully (RMSD < 2 Å) predicted the binding mode for 69%.

 

Fragmenstein can even be used for covalent ligands, as shown for the target NUDT7, which we wrote about here. Merging two fragments led to compound NUDT7-COV-1, and the RMSD between the Fragmenstein model and the crystal structure was an impressive 0.28 Å.

 

Of course, as the researchers acknowledge, the number of possible analogs might be daunting, and deciding which to make or buy is not necessarily straightforward. Also, Fragmenstein assumes that the fragments themselves are making productive interactions with the protein, which may not be the case, as we suggested here. Still, the tool is open-source and worth trying, especially if you are swimming in crystal structures.

Review of 2024 reviews

Long winter is here in the global north, with its dim days and gaping nights. As is tradition, Practical Fragments looks back on the year almost done. 2024 was the best year for conferences since the arrival of COVID. I wrote about CHI’s Drug Discovery Chemistry in San Diego, FBDD-DU in Brisbane, FBLD 2024, and CHI’s Discovery on Target, both in Boston. 

Another tradition is the annual J. Med. Chem. fragment-to-lead success story review; the latest covers the year 2022 and was written by Andrew Woodhead (Astex) and collaborators, including yours truly.

 

For a timely and accessible overview of “how to find a fragment,” look no further than a review of that title in ChemMedChem by Marcio Vinicius Bertacine Dias and collaborators at University of São Paulo and University of Warwick. This covers crystallography, cryo-EM, NMR, SPR, thermal shift, virtual screening, functional screening, ITC, mass spectrometry (including HDX-MS), MST, and BLI, and concludes with a nice comparison table. Some twenty other reviews were also published throughout the year, and these are discussed thematically.

 

Structure-based methods

Three reviews cover NMR. In J. Med. Chem., Janet Caceres-Cortes and colleagues at Bristol-Myers Squibb provide “perspectives on nuclear magnetic resonance spectroscopy in drug discovery.” Ligand- and protein-detected screening are covered thoroughly, with examples such as the discoveries of BI-2852 and venetoclax. Applications beyond hit finding are also discussed, such as the characterization of atropisomers in sotorasib, the identification and characterization of impurities and metabolites, in-cell NMR, and much more.

 

“Perspectives on applications of ¹⁹F-NMR in fragment-based drug discovery” is the title of an open-access review in Molecules by Qingxin Li and CongBao Kang at Guangdong Academy of Sciences and A*STAR, respectively. As we discussed in 2020, fluorine NMR is becoming increasingly common in FBLD, and this paper covers the various methods, including using ¹⁹F-NMR to measure ligand affinity. The authors also include a table summarizing 17 fragment screens that used fluorine NMR.

 

The rise of powerful permanent magnets has enabled low-maintenance benchtop NMR instruments that can be yours for as little as $50,000, compared to upwards of $1 million for a 600 MHz superconducting machine. Although sensitivity is at least 150-fold lower, hyperpolarization techniques such as photo-CIDNP, which we wrote about here, can close the gap. The latest developments are described (open access) in Chemistry–Methods by Felix Torres and collaborators at NexMR and the ETHZ.

 

X-ray crystallography has retained the top position among fragment-finding methods according to our most recent poll. In an open-access Applied Research paper, Daren Fearon, Frank von Delft, and collaborators describe high-throughput crystallographic fragment screening at the Diamond Light Source. As of August 2024 they have collected more than 240,000 academic data sets on hundreds of targets, and the paper distills some of the key lessons, some of which were applied to the COVID Moonshot, which we last wrote about here. The paper also describes future developments and needs, such as how and where to house such massive quantities of data.

 

Another center for high-throughput crystallographic screening is the Helmhotz-Zentrum Berlin (HZB) F2X-Facility at the BESSY II synchrotron, and in an open-access Applied Research paper Manfred Weiss and collaborators provide an overview of workflows and capabilities. One unique offering is the F2X-GO kit, in which F2X fragment libraries (which we wrote about here) as well other supplies are shipped to users to do soaking experiments in their own laboratories prior to shipment to the synchrotron.

 

“Structure-based virtual screening of vast chemical space” is the topic of an open-access review in Curr. Opin. Struct. Biol. by Jens Carlsson (Uppsala University) and Andreas Luttens (MIT). The Enamine REAL collection currently contains 40 billion compounds, and the researchers predict that “make-on-demand chemical libraries will likely reach more than one trillion compounds in the next few years,” which presents both opportunities and challenges, particularly given the existence of the “virtual cheaters” we recently discussed. Machine learning and fragment-based methods such as V-SYNTHES could help.

 

Continuing the virtual theme, Li Wang and collaborators at Nantong University discuss “molecular fragmentation as a crucial step in the AI-based drug development pathway” in an open-access Commun. Chem. paper. This summarizes 15 different computational methods for dissecting larger molecules into fragments, and also includes a list of 11 library vendors.

 

Other methods

Among experimental methods, few can match the throughput of fluorescence techniques, the subject of an open-access Heliyon review by Neelagandan Kamariah and colleagues at inSTEM & NCBS in Bangalore. It has short sections on “fluorescence polarization (FP) and anisotropy (FA), Förster resonance energy transfer (FRET), time-resolved Förster resonance energy transfer (TR-FRET), fluorescence lifetime (FLT), protein-induced fluorescence enhancement (PIFE), fluorescence thermal shift assay (FTSA) and microscale thermophoresis.” It also describes applications to GPCRs, protein-protein interactions, and other biological systems.

 

Native mass spectrometry (nMS), which we wrote about most recently in 2022, is the subject of an RSC Med. Chem. review by Louise Sternicki and Sally-Ann Poulsen at Griffith University. Sally-Ann is a leading expert in nMS, and the paper describes the technique and how it compares to other fragment-finding methods. It also includes a nice table summarizing 17 studies published between 2013-2023 that used nMS for FBLD; a 2013 review of nMS covered earlier examples.

 

Covalent Fragments

Mass spectrometry plays a prominent role in finding covalent fragments, as discussed in an open-access SLAS Discovery review by Simon Lucas and colleagues at AstraZeneca. “Covalent hits and where to find them” also describes other biophysical, biochemical, cellular approaches, and even DEL screening. It also discusses covalent libraries (which we wrote about earlier this month) and successful examples such as the discovery of sotorasib. In my opinion the researchers succeed in their “hope that this review will help serve as a useful roadmap to those seeking to drug the undruggable.”

 

A concise open-access review in Curr. Opin. Struct. Biol. by Katrin Rittinger and collaborators at The Francis Crick Institute and GSK focuses on using covalent fragments to assess target tractability, specifically ligandability and functionality. Target-based, proteome-wide and function-first approaches are summarized, and the researchers also discuss the importance of negative control compounds such as inactive enantiomers.

 

Continuing the theme of “assayability,” Micah Niphakis (Lundbeck) and Ben Cravatt (Scripps) review “ligand discovery by activity-based protein profiling” (ABPP) in a Cell Chem. Biol. paper. Because ABPP is usually conducted in cells or cell lysates, full-length proteins are assayed in their native environment, facilitating the discovery of allosteric ligands as in the case of WRN, which we wrote about earlier this year. The paper summarizes multiple examples of finding covalent ligands for challenging targets, and also highlights future challenges such as increasing throughput and targeting residues beyond cysteine.

 

Reversible covalent inhibitors are the topic of an open-access review by Dustin Duncan and colleagues at Brock University in ACS Chem. Biol. The researchers argue that reversible covalent inhibitors may cause less accumulation of the off-target adducts that could form with irreversible inhibitors. The paper includes a figure showing reversible covalent warheads, details on how to characterize them, a nice summary of general considerations, and success stories for JAK3, BTK, and proteases.

 

Targets

Covalent ligands have been particularly important for cancer targets, as reviewed by Xiaoyu Zhang (Northwestern University) and Ben Cravatt (Scripps) in an open-access Annual Review of Cancer Biology paper. The focus is on use of chemical proteomics “to expand the druggability of cancer proteomes.” The paper presents examples of finding and characterizing covalent ligands for a variety of oncology targets including KRAS^G12C.

 

E3 ligases are briefly mentioned, and this target class is the focus of an Expert Opin. Drug Discov. review by Jongmin Park and colleagues at Kangwon National University. As we’ve discussed recently, the 600 or so human E3 ligases are potentially valuable for targeted protein degradation applications such as PROTACs. The review focuses on “fragment-based approaches to discover ligands for tumor-specific E3 ligases.” In addition to summarizing successes against targets such as BCL6 and XIAP, it includes a list of 113 tumor-specific E3 ligases and another list of 52 E3 ligases that are overexpressed in certain tumors.

 

The “impact of fragment-based drug design on PROTAC degrader discovery” is also the subject of a review in Trends in Analytical Chemistry by Xiaoguang Lei and colleagues at Shenzen Bay Laboratory. Here, the focus is more on using FBLD to discover ligands for target proteins rather than for E3s. For example, the researchers describe how the fragment-derived drug navitoclax was used as a starting point for developing DT2216, a clinical-stage BCL-x_L degrader.

 

As Vicki Nienaber noted more than a decade ago, fragment-based drug discovery is ideally suited for targeting the central nervous system, particularly when combined with a ruthless focus on molecular properties. This is the topic of an open-access review in Front. Chem. by Michael Kassiou and collaborators at University of Sydney, CSIRO, and Vast Bioscience. After a brief summary of FBLD the researchers present case studies published since 2015, the last year this topic was reviewed. We’ve covered quite a few on Practical Fragments, including apoE4, Notum, and PDE10A.

 

Other

We mentioned allostery above, and in an open-access FEBS Open Bio. article Andrea Bellelli and collaborators at Sapienza University of Rome and elsewhere ask “is allostery a fuzzy concept?” Digging into half-century old publications from Jacques Monod, the researchers conclude that the concept was “born with an original sin: two definitions.” Indeed, the first mathematical model did not even apply to monomeric proteins. Most readers of this blog will probably be satisfied with the notion that an allosteric ligand is one that binds outside of an active site, but it is worth remembering that “allostery is an umbrella that covers more than a single reaction mechanism and cannot be defined by a single mathematical expression.”

 

Structure comes up frequently on Practical Fragments, but James Fraser (UCSF) and Mark Murcko (Disruptive Biomedical) remind us in Cell that “structure is beauty, but not always truth.” We’ve written multiple posts about getting misled by crystal structures, and in this brief commentary the authors provide “four harsh truths: a structure is a model, not experimental reality; representing wiggling and jiggling is hard; in vitro can be deceiving; drugs mingle with many different receptors.” They conclude that “truth is a molecule that transforms the practice of medicine.”

 

Stumbling towards truth is a little easier with the help of a good chemical probe, and in Nucleic Acids Res. Paul Workman (Institute of Cancer Research) and collaborators provide an updated description of The Chemical Probes Portal. This free community resource now contains 803 probes against 570 targets, including 28 covalent ligands and 51 degraders. Moreover, 332 of the probes have structurally related negative controls. Importantly, the Portal also includes 258 “Unsuitable” compounds that are insufficiently potent or selective to serve as chemical probes. Checking this list can save you valuable time when reading papers about unfamiliar targets.

 

Finally, a brief open-access interview with Nobel Laureate Katalin Karikó in Issues in Science and Technology is an inspiring reminder that “you learn more from failure,” and that the pleasure of doing science can be its own reward.

 

Thanks for reading. Good luck in 2025, and remember that the sun is always out there, even when you can neither feel nor see it.

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?