Osteoporosis is a leading cause of age-related morbidity, yet existing antiresorptive and anabolic therapies remain limited by safety concerns, contraindications and poor long-term adherence. CADD522 is a small molecule inhibitor of the RUNX2 transcription factor currently under development for cancer therapy. Here, we investigated whether RUNX2 inhibition could protect against post-menopausal bone loss. In an ovariectomy-induced mouse model, CADD522 (25 mg/kg, three times weekly for eight weeks) enhanced bone formation, preserved trabecular microarchitecture and reduced marrow and peripheral adiposity. Cross-species pharmacokinetic and toxicological studies demonstrated oral bioavailability, favourable short-term tolerability and target engagement despite rapid systemic clearance, while cellular thermal shift assays confirmed direct engagement of RUNX2. Together, these findings identify RUNX2 inhibition as a therapeutic strategy that simultaneously improves skeletal integrity and metabolic homeostasis, supporting further development of CADD522 for osteoporosis and other RUNX2-driven diseases.
Preclinical cancer models do not always recapitulate clinical treatment response, as therapeutic response may depend on tissue context the model does not reproduce. Using obesity-conditioned triple-negative breast cancer as a lens, we argue that the tumor microenvironment acts through dual gates—controlling access and altering behavior—and propose the recapitulation gap as a measurable model-to-patient divergence to guide fit-for-purpose drug development.
Alzheimer's disease (AD) is characterized by progressive metabolic failure, impaired mitochondrial function, and diminished adaptive stress responses, highlighting the need for disease-modifying therapies that restore cellular resilience rather than target downstream pathology. Here, we report the discovery and preclinical validation of C273, a translationally optimized, brain-penetrant mitochondrial complex I (mtCI) modulator developed through medicinal chemistry optimization of our first-generation compounds. C273 retained nanomolar neuroprotective activity against Aβ-induced toxicity while exhibiting favorable drug-like properties, including high oral bioavailability, efficient brain penetration, microsomal stability, minimal CYP and off-target pharmacology liabilities, and selective mild modulation of mtCI. Mechanistic studies demonstrated that C273 activated AMP-activated protein kinase (AMPK) and coordinated antioxidant, autophagic, anti-inflammatory, and mitochondrial quality-control pathways in cultured cells and mouse brain. These responses were absent in AMPKα1/α2-deficient cells, establishing AMPK as an essential mediator, while rotenone pretreatment abolished C273-mediated neuroprotection, supporting engagement of the mtCI quinone-binding site. Repeated administration to wild-type mice for 30 days produced no detectable cardiac or hepatic toxicity. Importantly, C273 activated the same neuroprotective pathways and reduced Aβ and p-Tau levels in induced pluripotent stem cell-derived cerebral organoids from patients with sporadic AD. Together, these findings establish mild modulation of mtCI as a therapeutic strategy to restore metabolic resilience and identify C273 as a promising disease-modifying candidate for AD treatment.
Knowledge of gastrointestinal media is crucial for developing successful oral drug products. Bile acids are an essential component in the intestinal lumen, influencing the behavior of orally administered molecules. This review addresses the various roles of bile acids in drug discovery and development, examines the differences between humans and various preclinical models, and discusses how the physicochemical properties of drug molecules may influence their interactions with bile-containing media.
Structure-based virtual screening (SBVS) is a cornerstone of computer-aided drug design, yet its success depends on selecting a combination of docking tools, scoring function (SF), and ranking strategies. MolDockLab addresses this challenge with an automated, data-driven framework that optimizes SBVS workflows for a protein target, balancing predictive performance and computational efficiency. It systematically explores combinations of five docking engines, 15 SF, and three consensus ranking strategies using a calibration set of ≈ 200 compounds with known bioactivity, and applies the best-correlating workflow to the larger screening library. Final hit selection from the top 1% integrates protein-ligand interaction profiler (PLIP)-derived interaction fingerprints, structural-diversity assessment, and expert visual inspection. In a retrospective evaluation on the epidermal growth factor receptor (EGFR), the chosen pipeline achieved a Spearman correlation of 0.36 and an enrichment factor (EF) at 10% of 1.57, consistent with calibration. Prospectively, for the energy coupling factor transporters (ECF-T)-a challenging transmembrane target with a cryptic binding site and no co-crystallized ligand-the pipeline reached a correlation of 0.45 and enrichment of 3.13. Post-processing enabled in vitro confirmation of two chemically novel inhibitors rivaling the most potent ECF-T inhibitors reported to date.
Computational screening of giga-scale chemical spaces opens a cost-effective path to high-quality hit identification, providing entry points for drug discovery. As these on-demand spaces grow and successful applications multiply, rigorous blind benchmarks like CACHE Challenges provide important performance metrics for computational tools. Here, we report the first application of the V-SYNTHES2 synthon-based screening approach to the 173-billion-compound Enamine xREAL Space, a 16-fold expansion beyond its previous benchmarks, demonstrating near-linear computational scaling with only a 10-15% increase in cost relative to the 11-billion-compound REAL Space. We applied this workflow in CACHE Challenge #2, targeting the RNA-binding site of NSP13 (SARS-CoV-2), and CACHE Challenge #4, targeting the tyrosine kinase-binding domain of CBLB, both pockets lacking established pharmacology and representing extreme hit-finding challenges. Under blinded, independently validated conditions, V-SYNTHES2 ranked among the top-performing submissions: the 8% hit rate for NSP13 exceeded the field average of 2.3% and placed the approach among the top three workflows, while for CBLB, one compound meeting predefined hit criteria was identified. These results demonstrate that V-SYNTHES2 maintains robust performance at giga-scale on ligand-depleted targets, precisely the conditions where data-driven approaches would face fundamental limitations, and establish a quantitative performance baseline for synthon-based screening of hundred-billion-compound chemical spaces.
The Trikafta drug combination, comprising the corrector tezacaftor (VX-661), the potentiator ivacaftor (VX-770) and the dual corrector/potentiator elexacaftor (VX-445), has been FDA-approved for treatment of cystic fibrosis caused by ~300 cystic fibrosis transmembrane conductance regulator (CFTR) mutations. Nevertheless, several CFTR variants exhibit limited response to Trikafta. To address this therapeutic gap, we investigated whether the potentiator activity of VX-445 can complement the VX-770 and preclinical “co-potentiators” activity in partially responsive CFTR mutants. Functional clustering of clinical and preclinical potentiator profiles suggests that VX-445 represents a distinct potentiator class, an inference supported by its additivity with both VX-770/VX-770-like potentiators and co-potentiators across five CFTR mutants in bronchial epithelia. This concept was further validated in gene-edited 16HBE and primary human nasal epithelia, expressing G551D-, N1303K-, and W1282X-CFTR, the 3rd, 4th, and 6th most common CF-mutations, respectively, and was confirmed at the single-channel level. Moreover, we present the development of a novel series of co-potentiator compounds that are derived from our previously described 4172 corrector scaffold, which exhibit low micromolar potency. Our findings suggest that triple potentiation can significantly enhance functional restoration of poorly responsive gating mutants, thereby uncovering novel avenues for therapeutic development.
Metastatic castration-resistant prostate cancers (mCRPC) remain dependent on androgen receptor (AR) signaling despite resistance to androgen-deprivation and AR-targeted therapies. Induced proximity-based therapeutics employ event-driven pharmacology to target AR, potentially overcoming resistance mechanisms. Herein, we highlight recent advances in induced proximity strategies targeting AR in mCRPC.
Drug discovery has progressed to using advanced AI-driven platforms. These systems rely heavily on static structures and overlook an essential aspect: protein motion—conformational transitions and local fluctuations—is essential for function, as “no motion, no function.” Thus, this review discusses recent progress in measuring protein conformations and dynamics and introduces a new concept, “quantitative conformation-activity relationship (q-CAR),” aimed at developing drugs that target disease-specific protein conformations in the future.
Abstract The generalizability of co-folding models for protein–ligand structure prediction remains unclear. Here, we benchmark Boltz, a state-of-the-art co-folding model, using a curated set of ligand-bound human G protein-coupled receptors (GPCRs) from families unseen during training. We show that while Boltz generally predicts receptor backbones accurately, ligand poses can contain significant errors that lead to a limited ability to reproduce experimental affinity data when tested with FEP +. We further show that physics‑based refinement of Boltz models can correct ligand poses to near‑experimental accuracy and rescue FEP+ performance to that of the native structure. These results highlight the strengths and limitations of co-folding methods and motivate a workflow that pairs them with physics-based refinement and validation before high-stakes decisions in drug discovery.
Molecular glues are small molecules that induce ternary complexes between a protein of interest and a partner protein, creating therapeutic mechanisms distinct from traditional inhibitors. This minireview examines binding-site architectures of clinically tested molecular glues and extends these insights to ubiquitin-family partner proteins. Structural analysis suggests recurring recognition features, while emerging computational methods may accelerate discovery and optimization of ubiquitin-directed glues for therapeutic applications.
We conducted a phenotypic screen of 20,000 central nervous system (CNS)-biased small molecules for their ability to stimulate differentiation of mouse oligodendrocyte progenitor cells (OPCs) into oligodendrocytes. We identified a lead hit (CN045) with an EC50 of 40 nM in the OPC differentiation assay and a chemical scaffold conducive to modifications. In OPC differentiation assays, CN045 demonstrated higher potency than a known OPC differentiation compound Triiodothyronine (T3). CN045 promoted myelin-like ensheathment of engineered nanofibers by mouse and human OPCs and significantly increased remyelination in white and gray matter regions of mouse brain following cuprizone/rapamycin-induced demyelination. In terms of pharmacokinetics, CN045 is CNS-penetrable with low cytotoxicity. CN045 has a short half-life in vivo, but its chemical scaffold is conducive to structural modifications that can improve its metabolic properties. Collectively, these results demonstrate that CN045 is an attractive lead candidate for enhancing OPC differentiation and remyelination in multiple sclerosis patients.
Despite the prevalence of neuropathic pain and the opioid crisis, there has been little progress in the development of non-opioid alternative therapies. Protein kinases orchestrate multiple key pathophysiological events in the progression of pain signaling pathways. This review highlights druggable kinases and the associated scaffolds as well as molecular modalities to reverse mechanical allodynia and thermal hyperalgesia in neuropathy models whether preclinically or clinically.
The realm of molecular design, particularly for large molecules, presents unique challenges and opportunities in drug discovery and materials science. Large molecule design is inherently more complex and less explored compared to designing small molecules, adding significant difficulty in generative modeling. We aim to establish strong baselines for better scalability, efficiency, and generative performance in this domain. We evaluate the scalability and performance of generative AI models, initially effective for small molecule design, in generating large molecules for potential drugs in gene-based therapies, immunotherapies, hormonal regulators, and targeted cancer therapies. Our findings indicate that computational strategies and model architectures designed for small molecules may not readily extend to large molecular structures. To address these limitations, we explore masked language modeling strategies alongside advanced tokenization methods, including Atom-Pair Encoding (APE), to enhance generative AI models. We probe how incorporating such strategies, particularly the APE tokenization method that explicitly captures structural and chemical characteristics, can significantly improve design capabilities for complex molecular structures. Overall, our results demonstrate both the potential and challenges of deep generative modeling for large molecules and how the proposed enhancements may bridge the gap in generating large molecules when novel discovery is the ultimate goal.
The interaction between T-cell receptors (TCRs) with the peptide-bound major histocompatibility complex (MHC) intricately impacts the functional specificity of T-cell-mediated adaptive immune response. Consequently, implication in immunotherapy has contributed to the ever-growing computational methods for TCR recognition, which have recently attracted structure-based approaches due to advancements in protein structure modeling. Despite access to structural information of the predicted binding interface, graph neural network (GNN)-based TCR-pMHC binding specificity classifiers tend to show poor accuracy for samples with unseen peptides. In this work, we comprehensively assess the potential factors that critically impact the generalization performance of classifiers trained with computationally predicted structures. Specifically, our experiments focus on analyzing the sensitivity of such predictors to the interaction features in the TCR-pMHC interface and the structural uncertainty. Building on the analysis, we demonstrate how the design of classifier architecture with auxiliary training objectives can improve the generalization performance to novel peptides not yet seen during model training. Overall, our work highlights the challenges of unseen peptide generalization from different perspectives of the GNN-based classifier paradigm, showcasing the strengths and weaknesses of the current state-of-the-art approaches in the generalization landscape.
Abstract Global food supply strongly depends on honeybee pollination services, which are threatened by insecticides and pests such as parasitic Varroa destructor mites. Chemical varroacides/acaricides are hampered by resistance development, necessitating the development of sustainable and environmentally friendly alternatives, with arthropod venom peptides being considered promising sources of acaricidal toxins. With only a few acaricidal venom peptides being reported, we performed a systematic topical screening of 50 arthropod venoms against V. destructor, with 78% of the venoms causing 100% mortality after 24 h. Deconvolution of the venoms from the Tasmanian cave spider Hickmania troglodytes and the Giant Japanese funnel-web spider Gigathele gigas led to identification of the varroacidal peptides Ht1a and Gg1a. Topical application of Ht1a and Gg1a reduced varroa mite but not honeybee survival, despite Ht1a inhibiting voltage-gated sodium channels from varroa and honeybee with equal potency. Ht1a and Gg1a were inactive against human skeletal muscle (hNaV1.4), cardiac (NaV1.5), neuronal NaV channel isoforms, and human voltage-gated calcium channel CaV2.2. At human α3β2/4 nicotinic acetylcholine receptors, Gg1a was inactive while 10 µM of Ht1a partially blocked nicotine-mediated Ca2+ influx. Our data reveal Ht1a and Gg1a as promising candidates for the development of novel varroa mite treatments of honeybee hives.
Class A G protein-coupled receptors (GPCRs) constitute the majority of the clinical GPCR targets and are implicated in a wide range of neuropsychiatric disorders. Dimerization of class A GPCRs often introduces unique modes of allosteric modulation, leading to the reprogramming of the downstream signaling relative to individual protomers. In this review, we highlight the experimental evidence and pharmacological properties of class A GPCR dimers involved in mental disorders. We further elucidate the molecular basis of dimer assembly and activation through a comprehensive structural analysis of currently available class A GPCR dimers. Current strategies and therapeutic potential of dimer-specific targeting are also discussed. Together, these insights provide a framework for understanding class A GPCR dimer function and pave the way for the development of dimer-informed therapeutics.
Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics against bacterial infections. However, the discovery of AMPs is impeded by the limitations of biochemical screening and the difficulty computational approaches face in balancing efficacy with structural diversity. We proposed an integrated “generation-evaluation-validation” framework to facilitate de novo discovery of AMPs. First, we constructed a soft prompt-tuned ProtGPT2 to efficiently generate candidates AMPs with both novel structures and promising therapeutic potential. Secondly, we adopted a multiple-choice learning ensemble model that enables high-confidence evaluation of candidates via a dynamic voting network. Finally, antimicrobial experiments were used to validate the activity of top-ranked de novo AMPs by monitoring bacterial surface changes. Out of nine candidates, four exhibited potent strain-specific activity, while two demonstrated broad-spectrum efficacy. All tested AMPs exhibited strong biofilm inhibition, potent membrane disruption, and minimal hemolysis, indicating significant therapeutic potential. With strong generalizability and versatility beyond AMPs, the proposed framework’s modular design will facilitate adaptation to diverse peptide design tasks in the future. By integrating soft prompt tuning, multimodal ensemble learning, and experimental verification, this framework presents a practical and scalable strategy for rapid, resource-efficient de novo peptide discovery, particularly suited for applications where experimental throughput and cost are critical constraints.
Modern AI (Artificial Intelligence) methods offer new opportunities in pharmacology by enabling improved modeling of disease mechanisms and drug action learned from large and heterogeneous biological datasets. A central challenge is developing models that can jointly integrate disparate biomedical modalities. We introduce MAMMAL (Molecular Aligned Multi Modal Architecture and Language), a foundation model for cross-modal learning, designed to address the challenges associated with drug discovery tasks. MAMMAL was pre-trained on 2 billion samples across protein and antibody sequences, small molecules, and gene expression profiles, and supports classification, regression, and generative tasks on cross-modal inputs. Across eleven benchmarks covering multiple stages of the drug discovery pipeline, MAMMAL achieves state-of-the-art performance on nine tasks and competitive results on two. In an antibody-antigen binding benchmark, fine-tuned MAMMAL prediction scores significantly outperform AlphaFold3 confidence scores, used here as a reference proxy for binding likelihood, in five of seven antigen targets. The MAMMAL framework and pretrained models are publicly available to support open and collaborative research.
Alzheimer's disease (AD) is characterized by diminished capacity to mount adaptive cellular stress responses required to maintain energy homeostasis and proteostasis. An emerging therapeutic strategy is to restore adaptive stress responses by inducing mild energetic stress through inhibition of mitochondrial complex I (mtCI). However, pharmacological inhibition of the respiratory chain has remained challenging, as it can induce bioenergetic failure rather than beneficial signaling. Here, we describe C273, a brain-penetrant small molecule that delivers controlled, weak attenuation of mtCI activity to therapeutically restore endogenous adaptive stress pathways. This work establishes a first-in-class mechanism in which calibrated activation of multifaceted adaptive mechanisms enhances cellular resilience, rather than impairing mitochondrial function. Structure-activity relationship optimization yielded a compound with high potency against Aβ-induced cellular toxicity, strong selectivity for mtCI, and favorable drug-like properties. C273 demonstrated excellent oral bioavailability, metabolic stability in mouse, rat, and human microsomes, minimal CYP liabilities, and a clean ancillary pharmacology profile in the Eurofins CEREP44 panel. In vivo, C273 readily crosses the blood-brain barrier and activates AMP-activated protein kinase (AMPK), initiating a coordinated hormetic response characterized by enhanced antioxidant defenses, suppression of inflammatory signaling, induction of autophagy, and increased mitochondrial biogenesis and turnover. Genetic deletion of AMPKα1/α2 abolished these responses, establishing AMPK as a critical mediator of C273 activity. Pharmacological competition experiments further confirmed the target, as pretreatment with non-toxic concentrations of rotenone blocked C273 interaction with the quinone-binding site of mtCI and eliminated its neuroprotective effects. Repeated oral administration of C273 (20-80 mg/kg/day) to wild-type mice for one month produced no detectable cardiac or hepatic toxicity, indicating a favorable in vivo safety margin. Importantly, C273 activated these mechanisms and reduced Aβ and p-Tau levels in induced pluripotent stem cell-derived cerebral organoids from patients with sporadic AD. Collectively, these results establish controlled mtCI modulation as a therapeutic strategy and position C273 as a promising disease-modifying candidate for AD.
