Molecular simulation
16 articles
A solvated neural-network potential makes ligand strain estimates usable at library scale
- Conventional strain estimates reference a single gas-phase minimum, which ignores solvent and distorts charged ligands whose internal electrostatics collapse them into compact structures in vacuo.
- AIMNet2-CPCM, a neural network potential trained on DFT-level energies and forces in a continuum solvent, handles neutral and charged species on equal footing.
- GPU batch inference makes a solvated global-minimum search feasible across a full screening library.
- Across roughly 8,000 PDB ligands, solvated strain rises with net charge, with a trend that holds up to a charge of −2.
Strain filters are only as good as the unbound reference, and anyone who has seen a zwitterion fold into a salt bridge in vacuo knows why gas-phase numbers mislead. A solvated, charge-aware potential fast enough for library triage is something I would actually put in a docking post-filter. The caveat is the less reliable behavior at higher negative charge, which matters for acids and phosphates.
Kinase inhibitors often change protonation or tautomer state on binding, MCCE modelling shows
- Multi-conformation continuum electrostatics was applied to 37 co-crystal structures of nine kinase domains with 18 approved inhibitors.
- Protein net charge stayed largely stable on binding, whereas inhibitor charge states frequently shifted up or down.
- Binding can resolve two near-equal solution tautomers into one, creating free-energy differences of a few kcal/mol.
Docking with one fixed protonation state is quietly baked into most of our virtual screening, and this says it can be wrong by kcal/mol for approved drugs. When a scaffold with a tautomerisable heterocycle misbehaves in SAR, I'd now check its bound-state tautomer before blaming the pocket.
Kinase Inhibitors can Change Protonation or Tautomeric State upon Binding ↗
Approved anthracyclines and anthracenediones repurposed as single-molecule photosensitizers
- TD-DFT screening of a curated library of approved anticancer drugs picked out chromophore-bearing candidates with favourable photophysics.
- Mitoxantrone, pixantrone, daunorubicin and epirubicin generated reactive oxygen species on irradiation and showed enhanced cytotoxicity in breast cancer cells.
- One molecule thus supplies both the chemotherapy and the photodynamic effect, avoiding a two-component conjugate.
Starting from compounds with known PK and safety is the smart part: the CMC and tox story is already written. The catch is that these chromophores absorb at wavelengths that penetrate tissue poorly, and ROS from an anthracycline adds to cardiotoxicity worries. I'd want in vivo light-dose windows before getting excited.
Molecular dynamics finds a RhoJ-specific N-terminal pocket, and terconazole disrupts PAK3 binding
- RhoJ is nearly identical to Cdc42 in sequence but carries a unique N-terminal extension, which simulations show forms an allosteric pocket on binding PAK3.
- Structure-based virtual screening against that pocket flagged the antifungal terconazole as a possible binder.
- In live cells, an optimized BRET2 biosensor showed terconazole reduces the RhoJ–PAK3 interaction in a dose-dependent way.
Using a sequence extension absent from Cdc42 to buy selectivity is exactly how I'd attack a conserved GTPase. But terconazole is a repurposed azole with modest credentials as a starting point, and a BRET drop alone can't prove pocket engagement. I'd want mutagenesis of the N-terminal residues and a biophysical binding readout before calling this validated.
Identification and targeting of a novel drug pocket in the tumour angiogenesis regulator RhoJ ↗
DESPOT adds directionality to knowledge-based protein–ligand scoring
- Knowledge-based potentials are interpretable scoring functions, but most treat space around protein atoms as isotropic.
- DESPOT is an all-atom anisotropic potential that assigns atoms to isotropic, axially symmetric or fully anisotropic classes by hybridization and bonding.
- It uses a positionally averaged reference state and a 'void' ligand atom type so regions preferentially left empty can be represented.
- The authors position it as a direction-aware successor to existing knowledge-based scoring.
Directionality is exactly what hydrogen bonds, halogen bonds and pi contacts need and what isotropic potentials blur, so this targets a real weakness in how I triage docked poses. The void atom type is a neat touch for pocket regions that ligands avoid. What I will check before trusting it: performance on my own targets and decoy sets, not just benchmarks, and whether it ranks congeneric series rather than only separating actives from decoys.
DESPOT: Direction-Enhanced Scoring POTentials for Scoring of Protein–Ligand Interactions ↗
PLAC Dataset Tests Whether MM/GBSA Can Predict How PTMs Shift Ligand Binding
- The authors curated PLAC, an experimentally validated set of ligand activity changes caused by post-translational modifications; affecting PTMs tend to sit closer to the ligand than neutral ones.
- They benchmarked end-point MM/GBSA protocols across MD lengths and internal dielectric constants.
- Longer MD with a higher dielectric gave the best correlation with experiment, while a lower dielectric classified enhance/weaken/neutral better.
Nobody builds SAR around a PTM state, yet it can quietly explain why a potent binder fails in cells. A curated benchmark is worth more to me than yet another scoring tweak, because we finally have something to test against. The dielectric trade-off between ranking and classification is a useful warning: pick the protocol for the question you are actually asking.
Free Energy Calculations Assess Covalent Inhibitor Binding to Cruzain and Cathepsin L
- Uses relative binding free energy (FEP-type alchemical) calculations to compare warhead-bearing covalent inhibitors across two related cysteine proteases.
- Cruzain, the key protease of Trypanosoma cruzi (Chagas disease), and human cathepsin L, its close homolog, are modeled in parallel.
- Comparison highlights selectivity for the parasitic target versus off-target reactivity with the human enzyme, guiding optimization.
FEP on a reactive warhead is still shakier than on reversible binders — parameterizing the covalent bond is where these methods quietly break. Still, cat L cross-reactivity has sunk more cruzain leads than weak potency ever did, so a validated ΔΔG ranking that flags off-target binders before synthesis is real triage value — worth having when neglected-disease budgets don't cover redundant chemistry.
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.
Charting a Path Through Billion-Compound Virtual Screening
- Commentary piece examining the rapid growth of make-on-demand virtual compound libraries, now spanning tens of billions of molecules, and the practical challenges this scale poses for computational screening.
- Discusses how researchers prioritize and search these vast chemical spaces efficiently, weighing computational cost against the risk of overlooking promising hits.
- Considers how such large-scale screening approaches fit into early-stage hit-finding workflows in drug discovery.
Scale for its own sake buys nothing if the scoring function can't discriminate at that volume — past a few hundred million compounds, enrichment from most current physics or ML scorers plateaus fast. This piece is refreshingly honest that the triage problem isn't solved; given how thin the specifics are, that caution is the most I'd take from it right now.
Stress-Testing AI Co-Folding Screens: Lessons from a Keap1–Nrf2 Case Study
- Multi-stage virtual screening cascades that combine AI co-folding models with learned-affinity (ML) scoring are now standard practice in computational drug discovery.
- Each stage's output score is typically taken at face value and used to advance or discard candidates without independent validation of what it actually captures.
- This preprint systematically evaluates, stage by stage, whether cascade scores provide the information practitioners need to trust them, using the Keap1-Nrf2 protein-protein interaction as a case-study target.
- The authors characterize failure modes that arise at different stages of the cascade, offering a quality-control perspective on where and why these pipelines can mislead.
Every screening campaign now runs some flavor of co-fold-then-ML-score cascade, and honestly most of us take the ranked list at face value because checking each stage is a pain. That's the exact habit this preprint is poking at, using Keap1-Nrf2 — a notoriously flat, shallow PPI interface that's already brutal for docking and pharmacophore methods — as a stress test where scoring artifacts should show up fast. If the abstract's framing holds, the real message isn't "don't use these tools," it's "don't trust a single composite score without knowing what each stage is actually measuring" — pose confidence, affinity ranking, and physical plausibility are not the same thing, and conflating them is how false positives sneak to the top. Worth revisiting once the full methodology and failure-mode breakdown are out; details here are still thin.
Benchmark Study Reveals Blind Spots in AI-Based Virtual Screening for Keap1-Nrf2
- A new benchmarking study examines a virtual-screening pipeline that chains structure-based co-folding models with learned-affinity scoring, an increasingly common setup for computational hit-finding.
- The cascade was applied to the Keap1-Nrf2 protein-protein interaction, a notoriously hard-to-drug target due to its flat, polar interface.
- Instead of trusting only the final ranked output, the authors assessed each stage separately against experimentally meaningful signal.
- They identify specific points where a stage's score diverges from that signal, a failure mode that can mislead downstream compound prioritization.
- The paper proposes per-stage control checks practitioners can run before relying on a cascade's final hit list, framing the work as a methodology and reliability study rather than a report of a new inhibitor.
Keap1-Nrf2 is exactly the flat, polar PPI where a co-folding model will happily spit out a confident-looking pose and the affinity scorer will rank it #1 for the wrong reasons — so a paper that pries the cascade apart stage-by-stage is more useful to me than another "we found a hit" claim. What I actually want out of this: a checklist to run on my own vendor or in-house pipeline before I let its top 20 dictate what goes on the synthesis queue. If the co-folding confidence and the affinity score are quietly diverging, that's exactly the failure mode that burns weeks on compounds that never had real signal behind them. No new chemical matter here, no binding mode to chase — just a reminder that for a target this treacherous, the ranked list is not the QC step. Filed under "read before ordering," not "read for the inhibitor."
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 ↗
AI Screening Pipeline Flags Repositioned CASP4 Inhibitor Candidates for Alzheimer's
- CASP4, an inflammatory caspase linked to Alzheimer's disease, is the target of a new computational drug-repositioning pipeline built to screen DrugBank compounds for potential inhibitors.
- The workflow combined docking-based prioritization with ChemBERTa language-model embeddings and physicochemical descriptors to train machine-learning classifiers.
- A Random Forest classifier and regressor were used to identify likely active compounds and rank them by predicted potency.
- Downstream cheminformatics, PK-PD evaluation, and molecular docking narrowed the pool to five repositioning candidates.
- Molecular dynamics simulations and MM/PBSA free-energy analysis were then applied to assess binding stability, identifying two compounds (DB05316 and DB00519) with predicted binding compared favorably to the reference drug donepezil.
- The authors present the pipeline as a general strategy for prioritizing repurposed compounds against novel drug targets.
A five-stage funnel — docking, ChemBERTa embeddings, Random Forest, then MD/MM-PBSA — down to two compounds looks disciplined, but each stage has its own error rate, and stacking filters doesn't guarantee the errors cancel out. MM/PBSA rescoring of structurally unrelated ligands is notoriously unreliable for absolute ranking; chain enough filters and you can end up polishing false positives rather than eliminating them. Where this actually sits in the AI hit-finding wave: not novel chemistry, but faster triage of known-compound space, which is a real and growing niche. The genuine advantage is DrugBank sourcing — DB05316 and DB00519 already carry ADMET, safety, and formulation data, so there's no synthesis or fresh tox package standing between candidate and IND-enabling work. None of that substitutes for a binding assay. Until CASP4 activity is confirmed biochemically, this is a ranked hypothesis list, not a hit.
ChemBERTa-Guided Virtual Screening Identifies Repurposable CASP4 Inhibitors for Alzheimer's Disease
- Ligand-based virtual screening pipeline mined DrugBank for CASP4 inhibitor candidates, combining docking-score prioritization with SMILES-derived ChemBERTa embeddings and physicochemical descriptors.
- A Random Forest classifier trained on the hybrid ChemBERTa/physicochemical feature set separated active from inactive compounds with high accuracy and enriched actives among top-ranked hits.
- A companion Random Forest regressor, trained on experimental pIC50 values, ranked candidates by predicted potency.
- Cheminformatics filtering, PK-PD evaluation, and molecular docking narrowed the field to five DrugBank-listed candidates.
- Molecular dynamics simulations and MM/PBSA free-energy analysis confirmed stable binding poses for the top candidates.
- Positions the workflow as a drug-repositioning route toward CASP4-targeted Alzheimer's disease therapy, a caspase linked to neuroinflammation and pyroptosis.
What actually sells this to me is the DrugBank sourcing, not the screening funnel itself: the five candidates that survive docking, ChemBERTa/RF classification, and MM/PBSA already come with known ADMET and need no de novo synthesis to reach an animal model — that's a tractability shortcut a genuine hit-finding campaign rarely gets. CASP4's pocket is the usual caspase catalytic Cys-His dyad, so the real question the MD trajectories should answer is whether these scaffolds actually engage that dyad or just sit in a peripheral exosite; binding energy alone won't tell me that. Selectivity is the open flag — caspase-1/3/4 share fold homology, and off-target caspase hits in a neuroinflammation context could cut the wrong way. Methodologically, ChemBERTa embeddings layered onto classical descriptors for RF triage is a sane upgrade over pure docking-score ranking, though I'd want held-out chemotype validation before trusting the pIC50 regressor outside its training distribution.
Reversible-Work Metadynamics Delivers Fast Hit Triage — and Exposes a Memorization Blind Spot in AI Co-Folding
- Virtual screening often yields false positives from fast, approximate scoring functions, while accurate free-energy calculations to filter them remain too costly for routine high-throughput use.
- The authors introduce CTMD (c(t)-based metadynamics), which ranks binding stability using the nonequilibrium reversible-work estimator c(t) from short, independent metadynamics runs, without requiring converged binding free energies.
- Across diverse targets and chemotypes, CTMD shows robust early enrichment while staying fast, transferable, and largely tuning-free.
- Benchmarking AI co-folding, the authors find Boltz-2's enrichment tracks similarity to its training data and persists even after active-site modifications, pointing to memorization rather than true binding physics.
- CTMD is offered as an open-source middle ground between quick docking/co-folding and costly free-energy methods, cutting wasted discovery-campaign resources.
A triage tier between docking and full FEP is exactly what's been missing from my workflow. Standard docking scores are too noisy to prioritize synthesis, and running FEP on every virtual-screening hit is a non-starter for budget and turnaround — CTMD's short, independent metadynamics runs with the c(t) estimator could slot right into that gap and actually change which compounds we send to synthesis, without a cluster's worth of compute. Open-source and largely tuning-free means it's adoptable without a specialist babysitting it. The Boltz-2 finding is the one I'd flag to my team before anyone leans on AI co-folding for real decisions: enrichment tracking training-set similarity even after active-site mutation means it may be pattern-matching known chemotypes, not scoring genuine binding physics. That's a trap for exactly the novel scaffolds we're chasing.
Early-Enrichment Hit Discovery via Reversible-Work c(t) Estimation in Metadynamics (CTMD) ↗
Many Virtual Screening 'Improvements' May Not Be Real, Pre-Registered Study Finds
- The preprint asks a foundational methods question: given the size of a typical structure-based virtual screening benchmark, how large a difference in AUROC or enrichment between two protocols can actually be distinguished from statistical noise.
- Rather than assuming benchmark comparisons are inherently reliable, the authors first directly measure these resolution limits.
- They then pre-registered six candidate interventions to docking/scoring protocols and tested each against the measured resolution limits, rather than reporting post-hoc 'wins.'
- The pre-registration design is meant to guard against selective reporting, a known issue where small, benchmark-specific gains get presented as general improvements.
- This is a methodology/rigor study, not a new-inhibitor or target-specific paper; it addresses how virtual screening improvements should be evaluated.
- Practical implication: many published claims of improved virtual screening performance may fall within the noise floor of typical benchmarks, so hit-finding teams should treat single-benchmark improvement claims cautiously.
Every VS vendor pitch should have to clear this paper's bar. If a 'protocol improvement' can't beat the measured noise floor of AUROC/enrichment on a benchmark our size, it's not an improvement — it's a coin flip dressed up in a webinar slide. Six pre-registered interventions is a high bar; realistically most of us won't pre-register anything before a campaign, but I can at least ask collaborators to report effect sizes against a resolution limit, not just a headline AUROC bump. Sharpest implication for me: the co-folding hype (Boltz-2 and friends) needs the same treatment — if those models are just pattern-matching PDB-adjacent chemotypes, their 'wins' over classic docking/FEP on public benchmarks could be memorization, not generalization. I'm not swapping FEP for a black box on this evidence alone.