Fragment-based drug discovery
12 articles
Electrophile-first screen yields covalent aminothiazole inhibitors of the E2 enzyme UBE2D2
- An electrophile-first screen, combined with X-ray crystallography, found a covalent hit that engages two cysteines (C85 and C111) of UBE2D2.
- Structure-guided SAR on the amide and ester groups removed binding to C111 while improving engagement of C85.
- The optimized analog inhibited UBE2D2 activity in vitro and suppressed proliferation of A549 lung cancer cells.
E2s have long been written off as undruggable, so seeing a crystal-supported covalent series is a real step. The interesting design move is dialing out the second cysteine, which is exactly what you need before calling anything a probe. I'd now ask how it behaves across other UBE2D family members, since the active-site cysteine is conserved and selectivity will be the hard part.
Covalent fragments reach lysines in the SHP2 allosteric latch site
- Combines an electrophile-first covalent fragment screen with a ligand-first strategy to target lysines in the allosteric latch site of SHP2 phosphatase.
- Computational and experimental analyses support the binding mode, and the work yields what the authors call the first potent, cell-active allosteric covalent inhibitors at this site.
- It shows the latch site is covalently tractable, opening routes to new allosteric SHP2 chemotypes.
Lysine-directed covalent chemistry is far rarer than cysteine, so a workflow that actually delivers cell-active hits on a lysine is the takeaway, more than SHP2 itself. My caveat is reactivity versus stability: lysine electrophiles can be hydrolytically fragile or reversible, so I'd want proteome-wide selectivity data before calling anything a probe.
Fragment-Based Covalent Targeting of Lysines at the Allosteric Latch Site of SHP2 ↗
FragDockRL grows synthesizable ligands from a docked core with reinforcement learning
- FragDock builds molecules from synthetically accessible building blocks via known reactions and scores them by tethered docking that restrains a predefined core pose.
- FragDockRL adds reinforcement learning: rewards from rDock tethered docking followed by SMINA scoring drive a modified Deep Q-Network that guides stepwise molecular growth.
- The method was evaluated on three therapeutically important protein targets, including a receptor tyrosine kinase.
Growing from a fixed core under synthesis constraints mirrors how fragment elaboration actually happens on a project, which beats free generative models that hand you unmakeable structures. Docking-based rewards remain the weak link because they reward size, so I'd judge it by whether the compounds are truly makeable and hold up against a prospective test.
Automated Ligand-Detected NMR Platform Speeds Hit Triage in Drug Discovery
- An automated, high-throughput ligand-detected NMR method targets hit triage in medicinal chemistry screening campaigns.
- The approach is framed as enabling rapid, qualitative binding assessment of screening hits at scale.
- Reads as a platform/methodology contribution focused on throughput and workflow automation rather than a single target or chemical series.
NMR gives you binding mode, not just 'yes it binds' — that's the filter SPR and TSA can't offer, especially for the promiscuous frequent-hitters that eat up your week. If the automation actually holds up at HTS volumes, this slots in as a fast confidence check before compounds go into a co-crystal queue, not a replacement for it.
Fragment Growing Explored to Improve a Natural Cysteine-Sulfoxide BACE-1 Inhibitor
- Alliin, a natural cysteine-sulfoxide that chelates BACE-1's catalytic dyad, has poor pharmacokinetics, limiting its use as a drug lead.
- Researchers used a fragment-growing strategy guided by molecular dynamics simulations to build out from this core, aiming to improve affinity and drug-like properties.
- The campaign remains an early-stage computational effort, not a validated clinical candidate.
Starting from a food-derived fragment like alliin is seductive — built-in tox clearance, cheap chirality — but BACE-1 is where good binders go to die clinically (verubecestat, lanabecestat, atabecestat all crashed on safety/efficacy despite clean biochemistry). Growing a fragment computationally is the easy 80%; without disclosed SPR/ITC or crystallography backing the MD poses, I'd treat the affinity gains as hypotheses, not results.
Rule-Based SMILES Editing Tool Aids Fragment-to-Lead Optimization
- A new method systematically edits SMILES strings—swapping atom types, resizing or opening rings, and grafting fragments—to generate analog ideas for fragment-to-lead optimization.
- First implemented as a KNIME workflow tested on fragments from multiple programs, it's now also available as open-source Python code paired with AutoDock Vina docking.
- This non-learned, rule-based approach complements AI/ML generative design, offering a practical option when training data for a target is scarce.
Honestly this is what I'd do by hand anyway—ring contraction, bioisostere swaps, fragment grafting—just automated and exhaustive instead of me sketching ten ideas on a whiteboard. For a scarce-data fragment series, I'd trust this over a generative model I can't sanity-check. Free KNIME/Python beats begging IT for another SaaS license too.
Fragment Optimization─Simple SMILES String Manipulation Strategies to Optimize a Hit or Fragment ↗
TrmD as an Antibacterial Target: A Review of Small-Molecule Inhibitor Discovery Strategies
- TrmD, a SPOUT-family bacterial tRNA methyltransferase without a human counterpart, is highlighted as a validated antibacterial target amid rising antimicrobial resistance.
- The review catalogs strategies used to discover small-molecule TrmD inhibitors—fragment-based design, high-throughput and virtual screening, click-chemistry-enabled direct-to-biology methods, molecular hybridization, and prodrug approaches.
- It synthesizes structural features and structure-activity relationships across these diverse chemical scaffolds to inform future inhibitor optimization.
TrmD's lack of a human ortholog is the real selling point here — a wide therapeutic window without the usual off-target liability, which is rare enough in antibacterial discovery to justify the review's existence. The click-chemistry/direct-to-biology entries jump out as the underexploited lane: fast SAR turnaround without carrying every analog through full synthesis, exactly what a fragmented, scaffold-diverse target like this needs before someone finally nails spectrum and PK.
TrmD: A Promising Antibacterial Target and Its Small-Molecule Inhibitors ↗
Lysine-Targeted Covalent Fragments Open a New Allosteric Route to Inhibit SHP2
- Researchers built a combined screening pipeline pairing covalent fragment screening with a ligand-first design strategy to find chemical matter that reacts with lysine side chains rather than the more commonly targeted cysteines.
- The work focuses on the allosteric "latch" region of SHP2 phosphatase, a regulatory site distinct from the enzyme's catalytic pocket and from previously drugged allosteric tunnel sites.
- Computational modeling and experimental characterization were used together to guide fragment selection and to understand how covalent attachment at latch-site lysines affects the protein's conformational behavior.
- The approach yielded inhibitors described as the first potent, cell-active covalent compounds acting through this latch site, indicating the pocket can support functional, drug-like engagement rather than only structural characterization.
- The findings position lysine-directed covalent chemistry as a viable route for allosteric SHP2 modulation and provide a starting chemotype and site for further medicinal chemistry optimization.
Lysine-directed covalency is the real bet here, more than the new pocket itself: warhead chemistry and selectivity triage are far less mature than for cysteine acrylamides, so the first thing I'd pull up is proteome-wide lysine reactivity data before trusting the cell activity. The latch site itself is also genuinely new — it sits apart from where SHP099/TNO155-type allosteric inhibitors already bind, so this isn't retreading crowded IP. Getting cell-active hits straight out of fragment screening is a strong signal for this pocket's shape or a hyperreactive lysine — but fragment-to-lead on Lys chemistry means slower SAR cycles than we're used to with Cys warheads. Real value: a fresh chemotype and site for a target whose allosteric space was starting to feel exhausted.
Fragment-Based Covalent Targeting of Lysines at the Allosteric Latch Site of SHP2 ↗
New "Scope" Chemical Space Yields Ligand-Efficient Hits Across Three Targets
- "Scope" describes a mid-sized (10k-50k compound) chemical space positioned between minimal fragments and fully elaborated low-molecular-weight libraries, designed to maximize ligand efficiency and medicinal chemistry tractability.
- The authors introduce "Scope ring systems" (ring-X-ring) as a core organizing concept for building libraries and guiding functionalization toward sufficient potency.
- A 10k-compound Scope screening set — evaluated by SPR, native mass spectrometry, DSF, and biochemical assays — matches general diversity sets' primary hit rates even at higher screening concentrations, while showing superior ligand efficiency and higher hit rates after counter-screening.
- Case studies on trypanosomal CLK1 kinase, a 14-3-3/estrogen receptor-alpha molecular glue project, and SARS-CoV-2 main protease show the set yields diverse, tractable, ligand-efficient hits across varied binding sites.
The "sweet spot between fragments and full libraries" pitch isn't new — DEL, FBDD, and house libraries have all claimed it before. What's actually new in Scope is the ring-X-ring organizing logic: forcing vectors off two ring systems rather than one flat scaffold gives a built-in growth vector for SAR from hit one, the part fragment campaigns usually bolt on later. The number that matters to me isn't the hit rate, it's "higher hit rate after counter-screening" — that's fewer SPR artifacts and aggregators wasting my week. The real test is the 14-3-3/ERα glue case: PPI/glue targets chew up conventional libraries because there's no deep pocket to reward LE. If a rigid, moderately-sized scaffold set can seed a glue hit, that's the transferability claim worth watching, not the Mpro or CLK1 wins.
New Fragment-Based Docking Method Targets Long, Flexible Peptide Ligands
- Researchers built STELLAR, a new docking workflow aimed at a persistent problem: current tools poorly handle long, highly flexible biopolymer ligands, including peptides exceeding roughly ten subunits, because of their vast conformational freedom.
- STELLAR splits such ligands into smaller fragments, docks each one separately, then reassembles them into a full-length, physically realistic pose, using established software including GNINA, RDKit, and GROMACS for refinement.
- Tested against known peptide-protein complexes from the Propedia database and the Protein Data Bank, the workflow reproduced experimentally observed binding poses with high accuracy.
- Compared with other state-of-the-art docking tools, it runs mainly on CPUs, scales linearly with ligand length, executes faster, and includes a ranking metric suited to high-throughput screening of long-chain polymer-protein interactions.
Ten residues is roughly where my patience with standard docking runs out — past that, chains have so much conformational freedom that I end up either restraining everything by hand or giving up on the exercise. Fragment-then-reassemble is a sensible workaround, and leaning on GNINA/RDKit/GROMACS instead of a black-box neural net means I can actually inspect intermediate poses. CPU-only, linear scaling is the detail that matters most day-to-day: no GPU queue, no cloud bill, just run it on the workstation overnight for a stapled-peptide library. Most ML docking tools give me a score with no real confidence handle for ranking; a purpose-built ranking metric for long-chain ligands is genuinely missing elsewhere. That said, Propedia/PDB benchmarks are known complexes — reproducing a crystal pose isn't the same as correctly ranking novel binders against a target with no solved structure. Worth a pilot run, not a blind swap-in.
STELLAR: A Fragment-Based Docking Workflow for Highly Flexible Long-Chain Biopolymers ↗
Dynamic Combinatorial Chemistry Delivers a Reversible, Sub-Picomolar Trivalent Thrombin Inhibitor
- A PNA-templated dynamic combinatorial chemistry (DCC) platform assembles a three-fragment library (~125,000 possible combinations) using DNA/PNA hybridization handles for unbiased equilibration.
- Thrombin itself templates the library, selecting and amplifying the best-binding fragment combination; size-exclusion filtration coupled with MALDI mass spectrometry gives a full selection-to-readout cycle in under an hour.
- The selected trivalent assembly engages thrombin's active site plus both exosites simultaneously, driving exceptionally high (sub-picomolar-range) binding affinity and near-stoichiometric inhibition.
- Unlike covalent thrombin inhibitors, binding is fully reversible — pharmacologically relevant for an anticoagulant, where reversibility matters for managing bleeding risk.
- Demonstrates target-templated DCC as a rapid route to multivalent, exosite-spanning inhibitors, even against a well-studied target.
Sub-picomolar affinity out of a 125,000-member DCC library gets my attention immediately — that's an avidity effect, not a single tight pocket, and it's essentially bivalirudin's active-site/exosite-1 logic pushed one contact further to include exosite II as well. Exosite engagement should also buy real selectivity over trypsin-like serine proteases that don't share thrombin's exosite topology, though I'd want that confirmed head-to-head against fXa and trypsin. My hesitation is tractability: a DNA/PNA-templated trivalent assembly is a fantastic proof-of-concept and chemical tool, not yet a lead — that's a long way from oral. Against DOACs' one-pocket, small-molecule simplicity, this wins on affinity and reversibility but loses badly on drug-likeness; the real test is whether target-templated DCC can be steered toward a druggable combination, not just the tightest binder.
Discovery of a Reversible Sub-Picomolar Thrombin Inhibitor Using DCC ↗
Covalent Molecular Glue Locks Kalirin-Rac1 Complex to Halt GTPase Activation
- Researchers combined XChem crystallographic fragment screening with in silico screening, then merged fragment hits into covalent leads targeting the Kalirin-Rac1 GEF-GTPase complex.
- Instead of directly competing with GTP/GDP for Rac1's binding site, the covalent compounds act as molecular glues, locking Kalirin and Rac1 into an inactive ternary complex that blocks nucleotide exchange.
- Two resulting inhibitors, RS-009 and MC-278, reduce Kalirin's GEF activity through this dual action: partial nucleotide competition plus stabilization of the non-productive complex.
- Biochemical selectivity panels and cellular activity-based protein profiling show the compounds can distinguish among different GEF-GTPase pairs rather than acting indiscriminately.
- The authors frame this as an alternative to direct GTPase inhibition that could offer finer spatiotemporal control, positioning RS-009/MC-278 as chemical probe candidates.
Locking down the non-productive Kalirin-Rac1 complex rather than competing head-on for the nucleotide pocket is the smarter play — GTP/GDP affinity is sub-nanomolar, and pan-Rac1 nucleotide-pocket tools like NSC23766 have never been clean. It's the SOS1-KRAS playbook applied to Rho biology, not a new idea, but a good one to borrow. Selectivity across GEF-GTPase pairs is the actual prize, not a burden — Rho-family homology is brutal, so an interface-driven selectivity handle beats trying to differentiate active-site chemistry. What I'd interrogate: fragment merging from XChem hits into covalent leads has a high attrition rate, so two validated compounds (RS-009, MC-278) is a real result, not a given. Before calling these clean probes I want warhead kinetics and full-proteome ABPP, not just the GEF-GTPase panel.
Discovery of a molecular glue inhibitor that stabilises a non-productive Kalirin-Rac1 complex ↗