AI drug discovery, cheminformatics, bioinformatics, molecular simulation and virtual screening.https://pharmadigest.net/enhttps://pharmadigest.net/en/articles/2026-10-23-aimnet2-cpcm-ligand-strain-solvated/https://pharmadigest.net/en/articles/2026-10-23-aimnet2-cpcm-ligand-strain-solvated/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.Fri, 23 Oct 2026 00:00:00 GMTComputational & AIligand strainneural network potentialAIMNet2computational chemistryconformational energyhttps://pharmadigest.net/en/articles/2026-10-22-cofolding-models-ligand-placement-vs-protein-conformation/https://pharmadigest.net/en/articles/2026-10-22-cofolding-models-ligand-placement-vs-protein-conformation/AlphaFold3, RoseTTAFold3, Boltz-2 and Chai-1 were benchmarked on kinases and Class A GPCRs, families whose pharmacology depends on defined conformational states. Only complexes released after all four models' training cutoffs were used, plus one earlier control structure, to give a prospective test. Correct ligand placement turned out to be systematically decoupled from accurate prediction of the surrounding protein conformation.Thu, 22 Oct 2026 00:00:00 GMTComputational & AIcofoldingAlphaFold3Boltz-2Chai-1RoseTTAFold3computational screeninghttps://pharmadigest.net/en/articles/2026-10-21-sf-ga-pgp-substrate-to-nonsubstrate-redesign/https://pharmadigest.net/en/articles/2026-10-21-sf-ga-pgp-substrate-to-nonsubstrate-redesign/P-gp efflux limits bioavailability and CNS exposure, and ML substrate classifiers rarely translate into concrete design moves. SF-GA builds fragment synergy analysis into the mutation step of a genetic algorithm, biasing replacements toward substructures that co-occur in non-substrates. Against GA and reinforcement-learning baselines at a fixed evaluation budget it was competitive and gave the most synthetically accessible molecules, reporting the removed and inserted fragments for every accepted mutation.Wed, 21 Oct 2026 00:00:00 GMTComputational & AIP-glycoproteineffluxgenetic algorithmmolecular optimizationADMEhttps://pharmadigest.net/en/articles/2026-10-20-kinase-inhibitor-protonation-tautomer-shifts-mcce/https://pharmadigest.net/en/articles/2026-10-20-kinase-inhibitor-protonation-tautomer-shifts-mcce/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.Tue, 20 Oct 2026 00:00:00 GMTComputational & AIcomputational chemistrykinase inhibitorsprotonationtautomershttps://pharmadigest.net/en/articles/2026-10-19-charmm-gui-covalent-ligand-docker-autodock4/https://pharmadigest.net/en/articles/2026-10-19-charmm-gui-covalent-ligand-docker-autodock4/CGUI-CLD is a new CHARMM-GUI module that automates ligand preparation, ligand modification and docking runs for covalent ligands, built on AutoDock4. A knowledge-based library covers 66 warheads and 8 amino acids, so the post-reaction ligand geometry no longer has to be built by hand. Results are presented through a browser interface.Mon, 19 Oct 2026 00:00:00 GMTComputational & AIcovalent inhibitorsmolecular dockingvirtual screeningAutoDock4https://pharmadigest.net/en/articles/2026-10-18-pd-qsar-quantile-potency-extrapolation/https://pharmadigest.net/en/articles/2026-10-18-pd-qsar-quantile-potency-extrapolation/Standard QSAR struggles to predict analogs more potent than anything in its training set, which is exactly the lead-optimization question. Pairwise-Difference QSAR learns activity changes from descriptor differences between compound pairs, then aggregates the predictions. On ChEMBL-derived benchmarks across about a hundred targets with the most potent compounds held out, a low-quantile aggregation beat mean and kNN aggregation, direct QSAR and Chemprop.Sun, 18 Oct 2026 00:00:00 GMTComputational & AIQSARlead optimizationmachine learningChEMBLpairwise modelshttps://pharmadigest.net/en/articles/2026-10-15-del-ml-docking-cofolding-generalizability/https://pharmadigest.net/en/articles/2026-10-15-del-ml-docking-cofolding-generalizability/A recent NeurIPS competition showed top ML models trained on DEL data failing to generalize to out-of-distribution chemical space; this study tests whether structural modeling can bridge the gap. It benchmarks ML, Glide docking, GALigandDock and Boltz-2 cofolding on three biologically diverse targets screened against DELs built in several synthesis formats. ML does well in-distribution; the paper's focus is how each approach holds up on new chemistry.Thu, 15 Oct 2026 00:00:00 GMTComputational & AIDELmachine learningdockingbenchmarkcofoldinghttps://pharmadigest.net/en/articles/2026-10-12-flexenn-gnn-binding-energy-disordered-proteins/https://pharmadigest.net/en/articles/2026-10-12-flexenn-gnn-binding-energy-disordered-proteins/FlexENN is a message-passing graph neural network built on a local interface graph with geometry, flexibility descriptors and sequence per residue. Two models are trained independently: one for folded complexes and one for complexes with a disordered partner, using a newly built IDP dataset. Benchmarks include tubulin–tubulin interfaces in microtubules and microtubule–MAP complexes.Mon, 12 Oct 2026 00:00:00 GMTComputational & AIgraph neural networkbinding affinity predictionintrinsically disordered proteinscomputational screeninghttps://pharmadigest.net/en/articles/2026-10-10-fragdockrl-reinforcement-learning-fragment-design/https://pharmadigest.net/en/articles/2026-10-10-fragdockrl-reinforcement-learning-fragment-design/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.Sat, 10 Oct 2026 00:00:00 GMTComputational & AIfragment-based designreinforcement learningtethered dockingvirtual synthesishttps://pharmadigest.net/en/articles/2026-10-08-dengue-ns4b-structure-guided-inhibitor-3dqsar/https://pharmadigest.net/en/articles/2026-10-08-dengue-ns4b-structure-guided-inhibitor-3dqsar/Comparative models of the structurally unresolved NS4B across all four dengue serotypes reveal a conserved transmembrane pocket that fits known resistance mutations. Docking of the JNJ-series inhibitors fed predictive field-based 3D-QSAR models, which were used to screen bioisosteres and in-house compounds. The top candidate, BNJ-495, was extremely potent against serotype 2 with a high selectivity index, and its conserved binding contacts suggest broad-spectrum activity.Thu, 08 Oct 2026 00:00:00 GMTComputational & AIdengueNS4B3D-QSARstructure-based designantiviralhttps://pharmadigest.net/en/articles/2026-10-04-despot-anisotropic-scoring-potentials/https://pharmadigest.net/en/articles/2026-10-04-despot-anisotropic-scoring-potentials/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.Sun, 04 Oct 2026 00:00:00 GMTComputational & AIscoring functionknowledge-based potentialdockingvirtual screeningcomputational chemistryhttps://pharmadigest.net/en/articles/2026-10-03-scope-concept-hit-finding/https://pharmadigest.net/en/articles/2026-10-03-scope-concept-hit-finding/Scope defines a chemical space between fragments and elaborated low-molecular-weight library compounds, emphasizing compound quality, ligand efficiency and selected property ranges to keep hits tractable for medicinal chemistry. Scope ring systems (ring-X-ring) organize the space and guide intentional functionalization toward low- to mid-micromolar potency; a roughly 10k-compound set suits medium-throughput SPR, native MS, DSF and biochemical screens. Case studies on trypanosomal CLK1, a 14-3-3/ERα molecular glue project and SARS-CoV-2 Mpro show comparable primary hit rates to diversity sets, with better ligand efficiency after counter-screening.Sat, 03 Oct 2026 00:00:00 GMTComputational & AIhit findingscreening libraryligand efficiencychemical spacemolecular gluehttps://pharmadigest.net/en/articles/2026-10-02-boltz2-cofolding-akr1b10-virtual-screening/https://pharmadigest.net/en/articles/2026-10-02-boltz2-cofolding-akr1b10-virtual-screening/The authors tested whether a co-folding model can replace docking for triage, applying Boltz-2 to the cancer-linked enzyme AKR1B10. From a chemically diverse library of 867 candidates, they chose 40 by predicted affinity plus a diversity filter. About 70% of those tested showed inhibition at low-micromolar or better, and several submicromolar inhibitors with novel scaffolds emerged.Fri, 02 Oct 2026 00:00:00 GMTComputational & AIvirtual screeningBoltz-2co-foldingAKR1B10AIhttps://pharmadigest.net/en/articles/2026-09-30-ptm-binding-free-energy-plac-dataset/https://pharmadigest.net/en/articles/2026-09-30-ptm-binding-free-energy-plac-dataset/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.Wed, 30 Sep 2026 00:00:00 GMTComputational & AIMM/GBSAmolecular dynamicspost-translational modificationcomputational screeningdatasethttps://pharmadigest.net/en/articles/2026-09-27-generative-ai-stat3-inhibitor-colorectal-cancer/https://pharmadigest.net/en/articles/2026-09-27-generative-ai-stat3-inhibitor-colorectal-cancer/A pocket-conditioned, autoregressive generative AI model was used to design candidate molecules directly within the STAT3 binding pocket, rather than screening pre-existing compound libraries. This structure-based generative approach produced a potent small-molecule STAT3 inhibitor that advanced to biological evaluation. The inhibitor showed activity against colorectal cancer, where STAT3 hyperactivation is a recognized driver of tumor growth and survival.Sun, 27 Sep 2026 00:00:00 GMTComputational & AIstat3colorectal cancergenerative aide novo designstructure-based drug designoncologyhttps://pharmadigest.net/en/articles/2026-09-24-bayesian-flow-networks-molecular-design/https://pharmadigest.net/en/articles/2026-09-24-bayesian-flow-networks-molecular-design/Bayesian flow networks design molecules for protein pockets by jointly generating atom types and 3D coordinates. The two channels behave differently: atom types shift abruptly at a defined point, while coordinates sharpen gradually, a pattern consistent across model variants. Timing refinement to these transition points yields a training-free method matching baseline docking performance on a standard benchmark, with no extra predictor needed.Thu, 24 Sep 2026 00:00:00 GMTComputational & AIgenerative AIstructure-based drug designvirtual screeningde novo designBayesian flow networksCrossDocked2020https://pharmadigest.net/en/articles/2026-09-24-fragment-optimization-smiles-strategies/https://pharmadigest.net/en/articles/2026-09-24-fragment-optimization-smiles-strategies/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.Thu, 24 Sep 2026 00:00:00 GMTComputational & AIfragment-based drug discoveryFBDDcheminformaticsSMILES manipulationhit-to-lead optimizationopen-source toolshttps://pharmadigest.net/en/articles/2026-09-23-ultralarge-virtual-screening-perspective/https://pharmadigest.net/en/articles/2026-09-23-ultralarge-virtual-screening-perspective/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.Wed, 23 Sep 2026 00:00:00 GMTComputational & AIvirtual screeningultra-large librariescomputational chemistryhit discoverymake-on-demandhttps://pharmadigest.net/en/articles/2026-09-23-multiparadigm-docking-benchmark-cofolding/https://pharmadigest.net/en/articles/2026-09-23-multiparadigm-docking-benchmark-cofolding/Compares physics-based docking, deep-learning co-folding, and hybrid rescoring methods on how well they generalize to unfamiliar protein targets and chemical scaffolds. Builds on the recent Runs-n-Poses benchmark, which found that classical physics-based docking held an edge over several co-folding tools on truly novel targets. Assesses pose accuracy and ranking performance to offer practical guidance on choosing a docking strategy based on target novelty.Wed, 23 Sep 2026 00:00:00 GMTComputational & AImolecular dockingco-foldingbenchmarkdeep learningstructure-based drug designpose predictionhttps://pharmadigest.net/en/articles/2026-09-22-del-ml-physics-generalizability-benchmark/https://pharmadigest.net/en/articles/2026-09-22-del-ml-physics-generalizability-benchmark/The study benchmarks machine learning, physics-based docking, and cofolding methods for predicting protein-ligand binding using DNA-encoded library (DEL) screening data. Methods compared include state-of-the-art ML models alongside physics-based docking tools (Schrodinger Glide, Rosetta GALigandDock) and the cofolding method Boltz-2. Ground-truth binding data came from ultralarge DEL selection experiments across three biologically diverse protein targets and multiple DEL synthesis formats. The work follows a NeurIPS competition in which even top-performing ML models trained on DEL data failed to generalize to out-of-distribution (OOD) chemical space. ML models performed well in-distribution, but out-of-distribution hit discrimination depended on the specific target and ligand context, with no single method consistently dominating across cases. The authors conclude that benchmark performance alone cannot predict OOD generalization and release an open-source package, DEL-iver, for evaluating binding-prediction methods against DEL screening data.Tue, 22 Sep 2026 00:00:00 GMTComputational & AIDNA-encoded librariesDELmachine learningcomputational screeningdockingcofoldinghit findinghttps://pharmadigest.net/en/articles/2026-09-22-hbv-capsid-ai-multi-objective-optimization/https://pharmadigest.net/en/articles/2026-09-22-hbv-capsid-ai-multi-objective-optimization/Investigators describe a multi-objective lead-optimization campaign built on the sulfamoylbenzamide (SBA) chemotype exemplified by NVR 3-778, an earlier clinical-stage HBV capsid assembly modulator (CAM). An AI-guided computational workflow was integrated with iterative synthesis to simultaneously optimize antiviral potency alongside physicochemical and ADME/drug-like properties across the series. The campaign identified 11B-6 as the optimized lead, reported to combine stronger anti-HBV potency with improved drug-like characteristics relative to earlier series analogues. Mechanistically, 11B-6 functions as a capsid assembly modulator, engaging the HBV core (capsid) protein to misdirect nucleocapsid assembly and interfere with viral replication. The work positions 11B-6 as a candidate for further preclinical evaluation within the sulfamoylbenzamide CAM class for chronic hepatitis B treatment.Tue, 22 Sep 2026 00:00:00 GMTComputational & AIhepatitis Bcapsid assembly modulatorAI-driven designhit-to-leadantiviralhttps://pharmadigest.net/en/articles/2026-09-22-protac-oral-absorption-computational-score/https://pharmadigest.net/en/articles/2026-09-22-protac-oral-absorption-computational-score/PROTACs (proteolysis-targeting chimeras) are large, polar, conformationally flexible molecules that fall outside classical Rule-of-5 space, making oral absorption difficult to predict from standard physicochemical filters alone. The paper introduces a fully computational scoring approach for oral absorption, intended to support rational, in silico PROTAC design earlier in the discovery process rather than relying mainly on measured properties. The score is built from computed molecular descriptors likely spanning size, polarity/lipophilicity balance, flexibility, and conformational behavior such as intramolecular folding — features previously linked to permeability in beyond-Rule-of-5 compounds. By combining these descriptors into a single predictive metric, the method aims to help chemists rank or triage PROTAC candidates for oral-absorption potential before synthesis. The authors position the score as a practical design-stage tool for guiding linker, warhead, and E3-ligand modifications toward improved oral bioavailability, complementing rather than replacing experimental permeability assays.Tue, 22 Sep 2026 00:00:00 GMTComputational & AIPROTACtargeted protein degradationoral bioavailabilitycomputational ADMEdrug-likenesshttps://pharmadigest.net/en/articles/2026-09-21-reaps-geometric-deep-learning-peptide-design/https://pharmadigest.net/en/articles/2026-09-21-reaps-geometric-deep-learning-peptide-design/Researchers developed REAPS, a geometric graph neural network for designing peptide binder sequences that models the target receptor as a fully observable, all-atom structural context, rather than relying on backbone geometry alone as typical inverse-folding methods do. In head-to-head evaluations against the widely used inverse-folding model ProteinMPNN, REAPS showed improved sequence recovery, structural fidelity, and interface-level biophysical properties for both linear and macrocyclic peptide binders. The team paired REAPS with structural hallucination in a closed-loop de novo design pipeline to generate novel peptide candidates without requiring a known starting scaffold. Candidates produced through this pipeline were synthesized and tested against the neurokinin-3 receptor (NK3R), where they showed experimentally confirmed functional activity.Mon, 21 Sep 2026 00:00:00 GMTComputational & AIpeptide bindersde novo designgeometric deep learningmacrocyclic peptidesinverse foldingNK3Rhttps://pharmadigest.net/en/articles/2026-09-19-keap1-nrf2-virtual-screening-stress-test/https://pharmadigest.net/en/articles/2026-09-19-keap1-nrf2-virtual-screening-stress-test/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.Sat, 19 Sep 2026 00:00:00 GMTComputational & AIvirtual screeningco-folding modelsKeap1-Nrf2AI drug discoverycomputational chemistrypreprinthttps://pharmadigest.net/en/articles/2026-09-19-scope-chemical-space-hit-finding/https://pharmadigest.net/en/articles/2026-09-19-scope-chemical-space-hit-finding/"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.Sat, 19 Sep 2026 00:00:00 GMTComputational & AIhit-findinglibrary designligand efficiencyfragment-based drug discoverymolecular gluescreening methodologyhttps://pharmadigest.net/en/articles/2026-09-18-keap1-nrf2-cofolding-screening-cascade-benchmarks/https://pharmadigest.net/en/articles/2026-09-18-keap1-nrf2-cofolding-screening-cascade-benchmarks/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.Fri, 18 Sep 2026 00:00:00 GMTComputational & AIvirtual screeninghit-findingKeap1-Nrf2co-foldingcomputational chemistrybenchmarkinghttps://pharmadigest.net/en/articles/2026-09-18-taar1-agonists-structure-based-discovery/https://pharmadigest.net/en/articles/2026-09-18-taar1-agonists-structure-based-discovery/TAAR1 is a G protein-coupled receptor pursued as a non-dopaminergic drug target for schizophrenia and antipsychotic therapy. A structure-based virtual screen docked about 65 million candidate molecules against an active-state model of TAAR1. Of 55 top-ranked hits tested experimentally, 14 showed genuine TAAR1 agonist activity, ranging from mid-nanomolar to micromolar potency. Hit optimization then yielded agonists with nanomolar potency, and cryo-EM structures confirmed the binding poses the docking had predicted. Three optimized agonists showed high brain exposure and potency described as exceeding that of ulotaront, a clinical-stage TAAR1 antipsychotic candidate. In mice, all three normalized amphetamine-induced disruption of pre-pulse inhibition, a schizophrenia-relevant behavioral measure, without causing catalepsy, a common antipsychotic side effect.Fri, 18 Sep 2026 00:00:00 GMTComputational & AITAAR1GPCRvirtual screeningantipsychoticcryo-EMhit optimizationschizophreniaulotaronthttps://pharmadigest.net/en/articles/2026-09-18-stellar-fragment-based-peptide-docking/https://pharmadigest.net/en/articles/2026-09-18-stellar-fragment-based-peptide-docking/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.Fri, 18 Sep 2026 00:00:00 GMTComputational & AIfragment-based dockingvirtual screeningpeptide dockingcomputational chemistrybiopolymerhigh-throughput screeninghttps://pharmadigest.net/en/articles/2026-09-17-chemberta-ai-screening-casp4-inhibitors/https://pharmadigest.net/en/articles/2026-09-17-chemberta-ai-screening-casp4-inhibitors/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.Thu, 17 Sep 2026 00:00:00 GMTComputational & AIvirtual screeningAI/ML drug discoverydrug repositioningChemBERTahit findingmolecular dynamicshttps://pharmadigest.net/en/articles/2026-09-16-chemberta-casp4-virtual-screening-alzheimers/https://pharmadigest.net/en/articles/2026-09-16-chemberta-casp4-virtual-screening-alzheimers/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.Wed, 16 Sep 2026 00:00:00 GMTComputational & AIvirtual screeningChemBERTadrug repurposingAlzheimer's diseasemolecular dynamicshttps://pharmadigest.net/en/articles/2026-09-14-akr1b10-inhibitors-boltz2-cofolding/https://pharmadigest.net/en/articles/2026-09-14-akr1b10-inhibitors-boltz2-cofolding/AKR1B10 is an aldo-keto reductase enzyme overexpressed in several cancer types, making it an attractive but challenging drug target. Researchers used Boltz-2, an AI-based protein-ligand co-folding model, to computationally screen a large, chemically diverse compound library and prioritize a small, diverse shortlist of candidates for lab testing. Experimental testing of that shortlist produced an unusually high hit rate, with most of the tested compounds showing real inhibitory activity against AKR1B10. Several confirmed hits reached submicromolar potency and carried chemical scaffolds not previously reported as AKR1B10 inhibitors. The results support co-folding AI models as a practical, resource-efficient way to convert computational predictions into experimentally validated drug leads, reducing reliance on brute-force screening.Mon, 14 Sep 2026 00:00:00 GMTComputational & AIvirtual screeningAI drug discoveryco-folding modelAKR1B10hit findinghttps://pharmadigest.net/en/articles/2026-09-12-mapvis-antibiotic-virtual-screening/https://pharmadigest.net/en/articles/2026-09-12-mapvis-antibiotic-virtual-screening/Antimicrobial resistance is narrowing treatment options for pathogens like Staphylococcus aureus and Neisseria gonorrhoeae, motivating new computational discovery tools. MAPViS, a multimodal deep-learning framework, fuses molecular graph, fingerprint, and descriptor/3D geometric features via gated fusion, pretrained on ~10 million molecules using hierarchical pseudolabel classification and masked graph contrastive learning. After fine-tuning, it jointly predicts antibacterial activity against S. aureus and N. gonorrhoeae plus cytotoxicity risk in three human cell lines (HepG2, HSkMC, IMR-90). On benchmark and external validation data, MAPViS showed strong early-enrichment performance for S. aureus, with all ten top-ranked candidates confirmed experimentally active, while generalization to N. gonorrhoeae was more modest. The authors describe MAPViS as a scalable tool for joint activity-toxicity modeling to support antibiotic virtual screening.Sat, 12 Sep 2026 00:00:00 GMTComputational & AIvirtual screeningantibiotic discoveryantimicrobial resistancemachine learningcytotoxicity predictionhttps://pharmadigest.net/en/articles/2026-09-11-ctmd-metadynamics-hit-triage/https://pharmadigest.net/en/articles/2026-09-11-ctmd-metadynamics-hit-triage/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.Fri, 11 Sep 2026 00:00:00 GMTComputational & AIhit-findingvirtual screeningmetadynamicscomputational chemistryAI co-foldinghttps://pharmadigest.net/en/articles/2026-09-11-virtual-screening-benchmark-resolution-limits/https://pharmadigest.net/en/articles/2026-09-11-virtual-screening-benchmark-resolution-limits/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.Fri, 11 Sep 2026 00:00:00 GMTComputational & AIvirtual screeninghit-findingbenchmarkingcomputational chemistrydrug discovery methodology