Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 27 appraisals
Current opinion in chemical biology
Prediction of protein-protein interactions and co-complex models with deep learning
Protein-protein interactions (PPIs) are fundamental to cellular processes, and essential for understanding biological function and disease mechanisms. In this review, we emphasize recent deep learning-based methods for protein interaction study. Focusing on three closely related tasks of proteome-wide PPI prediction, PPI interface prediction, and PPI co-complex structure prediction, we discuss how emerging concepts and computation approaches have evolved to shape these fields We categorize recent approaches according to their methodological paradigms, summarize their strengths and limitations, and further explore diverse biological and biomedical applications, highlighting how computational methods in PPI prediction, PPI interface prediction, and PPI structure prediction jointly contribute to understanding of complex biological systems.
3 Aug 2026
Read appraisal →CPT: pharmacometrics & systems pharmacology
Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration
Quantitative systems pharmacology (QSP) models require calibration data from literature, yet manual curation is inconsistently documented and large language model (LLM) extraction can hallucinate values and fabricate citations. We present MAPLE (Model-Aware Parameterization from Literature Evidence), which uses structured validation schemas as a collaboration interface between LLMs and modelers. Two schemas span two scales: the SubmodelTarget schema for isolated experiments constraining individual parameters, and the CalibrationTarget schema for clinical and in vivo endpoints constraining the full model. Both separate data extraction from modeling decisions, recording every value with full provenance. Targeted validators catch characteristic LLM errors by matching values to source snippets, resolving DOIs, and executing code. For a pancreatic ductal adenocarcinoma QSP model, we used MAPLE to extract and curate 37 SubmodelTargets and 45 CalibrationTargets. Before any human review, the validators triggered 50 automated retries; every value carries a direct quote from its source and a verified citation; and 11 of 19 parameters are supported by more than one independent source. The LLM drafted usable forward models and code from context, while the modeler supplied the context and scientific judgment it cannot infer, revising forward-model choices in 65% of SubmodelTargets, priors in 46%, and source relevance in all files. This evaluation covers one model in one disease area, by a single group, so it characterizes the framework rather than establishing how broadly it generalizes. MAPLE records the modeler's reasoning in a form that can be re-run and independently checked, so it is not lost when the modeling team changes.
2 Aug 2026
Read appraisal →Journal of chemical information and modeling
Reinforcement Learning-Driven Multiproperty Optimization in Molecular Design Using Multicontext Transcriptome Data
Drug discovery inherently involves multiparameter optimization in the molecular design because drug candidate molecules must meet diverse properties such as bioactivity, synthesizability, and pharmacokinetic properties. This optimization has traditionally relied on iterative manual design and experimental testing, which are labor-intensive and time-consuming. There is therefore a strong incentive to develop computational methods that efficiently design drug-like molecules with multiple favorable properties using chemical and biological data on therapeutic targets. This study proposes a novel computational method for multiproperty optimization in the molecular structure design of bioactive molecules using multicontext (i.e., chemically and genetically perturbed) transcriptome data on human cells. We integrate a molecular generative model conditioned on a transcriptome profile observed with the target gene knockdown or overexpression into a reinforcement learning framework, enabling simultaneous optimization of a quantitative estimate of drug-likeness, synthetic accessibility score, and water/octanol partition coefficient, while accounting for system-level biological effects on a therapeutic target. Using comprehensive benchmarking against established baselines and rigorous validation across multiple metrics, we demonstrate that the proposed method consistently yields molecules with more favorable drug-like characteristics than existing methods. This proposed method can help achieve more efficient identification of novel drug candidates.
29 July 2026
Read appraisal →Journal of chemical information and modeling
Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow
Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-site-insensitive deep-learning predictors. Here we present a pipeline reformulating kinase-substrate modeling as a Bayesian inference problem. By integrating curated data sets and literature evidence parsed by Large Language Models, we converted diverse biological knowledge into structural restraints for the restraint-guided deep-learning model GRASP. For EGFR, BRAF and JNK1, we obtained 336 new phosphorylation-site-specific structure candidates refined by molecular dynamics. These models recapitulate known features, such as JNK1's hydrophobic docking groove, and enabled a Virtual Position Scanning Peptide Array (V-PSPA) to map recognition patches and derive sequence preferences. Cross-referencing predicted interfaces with AlphaMissense pathogenicity scores reveal that the interaction types and distances to the catalytic pocket significantly influence pathogenicity scores. A comparison with clinical mutation data sets further connects pathogenic mutations to the kinase-substrate interface. This high-resolution, high-throughput pipeline can be broadly applicable to kinase specificity studies and general drug discovery.
28 July 2026
Read appraisal →Journal of chemical information and modeling
Machine Learning for RNA-Targeting Drug Design
Targeting RNA with small molecules offers significant therapeutic potential. Machine learning could substantially accelerate preclinical drug discovery, from hit identification to lead optimization. Yet a limitation emerges: drug design machine learning models, designed for proteins, are not readily applicable to RNAs because of fundamental differences between RNAs and proteins in both structural characteristics and interactions with small molecules. RNA-specific approaches have consequently emerged, primarily focusing on binding site identification and virtual screening. In this review, we comprehensively compare machine learning tools for RNA-targeting drug design according to the tasks they address, their methodology and their relevance in RNA-specific contexts. As open challenges will catalyze new method development, we emphasize the need for standardized, drug design-specific evaluation approaches. We provide clear guidelines to establish these standards and present a benchmark assessing the ability of current machine learning models to predict specific drug-RNA interactions.
28 July 2026
Read appraisal →Science advances
Multistage microrobots with pH-responsive release of platelet membrane-coated nanoparticles
Targeted drug delivery in the gastrointestinal tract remains challenging because therapeutics must overcome multiple hierarchical barriers before reaching diseased tissue. Here, we present a multistage delivery platform that integrates magnetic microrobots, a pH-responsive protective coating, and platelet membrane-coated nanoparticles (PNPs) in one platform. A fillable design enables the formation of an internal magnetic layer for microrobot actuation, while the pH-responsive coating protects the cargo during transit and selectively degrades upon pH change, releasing cancer cell-targeting PNPs. In an in vitro colon cancer model that reproduces key gastrointestinal features, including flow, pH variation, and villi-like structures, this strategy increased nanoparticle retention and enhanced cancer cell cytotoxicity compared to nanoparticles administered alone. Ex vivo studies in porcine stomach and intestine further demonstrated robust locomotion on compliant and folded tissue surfaces. These results establish an environment-responsive hierarchical delivery strategy for more precise oral delivery in complex gastrointestinal settings.
20 July 2026
Read appraisal →ACS synthetic biology
LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides
Antimicrobial peptides (AMPs) represent promising alternatives to conventional antibiotics, yet hemolytic toxicity remains a critical barrier to clinical translation, with approximately 70% of known AMPs exhibiting high or moderate hemolytic activity. Existing computational prediction methods are often constrained by deficiencies, including high computational complexities, the inability to capture multiscale sequence patterns, and insufficient generalization across diverse datasets. We present LysePred, a multiscale convolutional neural network to address these deficiencies concurrently by employing parallel branches with exponentially spaced kernel sizes to simultaneously capture local amino acid motifs (bigrams, 4-g) and longer-range amphipathic patterns (8- to 32-g). LysePred achieves top-tier performance on six benchmark datasets, exceeding the performance of the second-best method by 9.13% in MCC and 3.65% in ACC on average, while maintaining exceptional stability (MCC CV < 6.92%, ACC CV < 2.73%). Furthermore, independent validation on the HemoPI2 dataset demonstrates that LysePred delivers highly competitive results with a computationally parsimonious design (∼0.55 M parameters). This represents a reduction in parameter density of 1 to 2 orders of magnitude compared to Transformer-based approaches, such as the 8M-parameter ESM-2 or the 110M-parameter BERT Base while maintaining linear complexity for high-throughput screening. Ablation studies validate that the multiscale architecture contributes meaningfully to performance, with single-scale variants showing up to 13.35% MCC degradation. Interpretability analyses via t-SNE visualization and SHAP feature importance reveal that LysePred learns biologically meaningful representations integrating both local sequence motifs and global structural patterns. LysePred offers a practical, efficient, and interpretable tool for rapid hemolytic toxicity prediction in antimicrobial peptide development. Code and data are available at https://github.com/lincubator/LysePred.
20 July 2026
Read appraisal →Bioconjugate chemistry
Artificial Intelligence for Discovery in Life Sciences
Artificial intelligence is becoming a transformative tool in life sciences, not just by improving the results of existing technologies but also by introducing fundamental new ways of discovery. Initially applied to denoising, segmentation, or pattern recognition, it now extends across microscopy, structural biology, protein engineering, experimental design, and hypothesis generation. In imaging, deep learning enhances fluorescence, cryo-EM, and expansion microscopy and increasingly links optical and non-optical modalities. Beyond imaging, AI accelerates fluorescent probe development, while large language models and multi-agent systems are beginning to synthesize literature, generate hypotheses, and guide experiments. We survey these developments across imaging and non-imaging domains, from microscopy and structural biology to molecular design, hypothesis generation, and autonomous experimentation. We discuss the convergence of AI with tools from chemistry to instrumentation and explain challenges in validation, interpretability, generalizability, and autonomy. We conclude that AI is beginning to connect measurement, design, and reasoning to accelerate biological discovery.
16 July 2026
Read appraisal →International journal of cancer
Drug repurposing in oncology: Bridging computational discovery to clinical application.
Drug repurposing, the identification of new therapeutic applications for existing drugs, has emerged as a pragmatic and cost-efficient strategy to accelerate oncology drug discovery. Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic-proteomic signature reversal, and network-based modeling, have enabled rapid identification of repurposable agents by mining multi-omics and historical pharmacological data. Experimental pipelines spanning high-throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3-kinase/Ak strain transforming/mechanistic target of rapamycin, mitogen-activated protein kinase/extracellular signal-regulated kinase, wingless-related integration site/β-catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi-omics datasets, explainable artificial intelligence, patient-derived organoids, and clustered regularly interspaced short palindromic repeats-based genetic screens now offers unprecedented precision in identifying context-specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. This approach bridged preclinical insight with clinical application, potentially transforming cancer therapeutics through rational, data-driven innovation.
15 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Physically intelligent capsule robots with embodied memory and logic in the gastrointestinal tract
Miniaturized medical robots offer a promising solution for minimally invasive measurements and interventions in the gastrointestinal (GI) tract. Clinical assessment of GI disorders is commonly guided by threshold-based physiological indicators, including pressure, temperature, and pH, which motivate event-triggered strategies for personalized medicine. However, identifying homeostatic dysregulation and enabling in-situ therapy remains challenging, because ingestible robotic systems must tightly integrate sensing, decision-making, and actuation under severe constraints of size, power, and biosafety. Inspired by the autonomy of microorganisms that operate without neural processing, this work introduces physically intelligent capsule robots (PI Capbots) that enable homeostatic monitoring and targeted delivery within the GI tract, without relying on centralized electronic control. Through embodied stimuli-responsive memory and logic, PI Capbots effectively distill rich, detailed, and redundant physiological information into a small set of decoupled and event-triggered outputs suitable for operations in in vivo environments. In each PI Capbot, multistable metamaterials encode intraluminal pressure as mechanical memory, programmable hydrogels implement orthogonal sensing and logic operations, and helical fibers enable multimodal locomotion. Ex vivo and in vivo studies in large animal models demonstrate the efficacy, robustness, and reproducibility of PI Capbots, highlighting its potential for their translational medical applications.
15 July 2026
Read appraisal →Cell host & microbe
Mining the code of life for new antibiotics.
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
10 July 2026
Read appraisal →European journal of medicinal chemistry
Accurate and interpretable ADMET prediction: Integrating structural, geometric, and global molecular context representations
Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles early in drug discovery is essential to avoid late-stage failures. However, most current computational models depend on single-view molecular representations. This restricts them from capturing the complex interplay between 2D topology, 3D conformations, and specific functional groups that collectively drive structure-pharmacokinetic relationships. Here, we present LGSM, a comprehensive deep learning framework for integrative molecular property prediction. By fusing structural (2D topology), geometric (3D conformational descriptors), and sequence-derived semantic representations, LGSM provides accurate ADMET predictions. Specifically, a language model-based encoding of SMILES sequences is utilized to capture the global molecular context, seamlessly complementing local functional group characteristics. By fusing these different levels of information, our architecture accounts for both local chemical environments and broader spatial relationships. We evaluated this approach across four public ADMET datasets. To mimic real-world drug design scenarios where new chemotypes are constantly explored, we applied a strict scaffold split. Under these conditions, LGSM consistently outperformed standard computational baselines. Beyond predictive accuracy, we prioritized chemical interpretability. By combining the model's internal analysis with molecular docking, we show that LGSM successfully flags key substructures responsible for metabolic liabilities or transporter recognition. This transparency makes the framework a practical tool to guide rational lead optimization in medicinal chemistry.
6 July 2026
Read appraisal →Molecular biology reports
Recent advances in the detection and functional analysis of circRNAs with short-read RNA sequencing-based methods
Circular RNAs (circRNAs) were first identified approximately 50 years ago in pathogenic viroids as single-stranded, covalently closed RNA molecules. Initially considered by-products of splicing, circRNAs are now recognised as an important class of regulatory RNAs involved in microRNA sponging, RNA-protein interactions, and cellular pathways. Their closed-loop structure, generated through backsplicing, confers resistance to exonucleolytic degradation and contributes to their stability. Owing to their tissue- and disease-specific expression, circRNAs have emerged as promising biomarkers for cancer, neurodegenerative disorders, and cardiovascular disease. Over the past decade, numerous bioinformatics tools utilising RNA-sequencing (RNA-seq) data have been developed for circRNA detection and analysis. Detection methods have evolved from manual split-read inspection to automated identification of the back spliced junction, while annotation pipelines now resolve the genomic origins and structural characteristics of circRNAs. Because individual circRNA callers vary considerably in sensitivity and specificity, a combined usage of tools in circRNA detection has become the preferred strategy for generating high-confidence datasets. Beyond their non-coding functions, increasing evidence suggests that some circRNAs possess protein-coding potential through open reading frames, cap-independent translation mechanisms, internal ribosome entry sites (IRESs), and N6-methyladenosine modifications. A new generation of bioinformatic tools can now assess the protein-coding potential of circRNAs, integrating the above features, as well as machine learning and deep learning approaches refining these predictions. This review summarises recently developed short-read RNA-seq bioinformatics tools for circRNA detection, consensus calling, annotation, and protein-coding potential prediction, with a particular focus on advances from the past five years that facilitate the identification of translatable circRNAs.
5 July 2026
Read appraisal →Journal of neurology
The dual role of mTOR in multiple sclerosis pathophysiology: a systematic review
Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelination, neuroinflammation, and progressive neurodegeneration. The mechanistic target of rapamycin (mTOR) pathway plays a key role in regulating immune responses, cell metabolism, autophagy, and repair processes. Although the role of mTOR in neurodegeneration has been explored in previous reviews, a systematic assessment of its function in MS across different models is still lacking. This systematic review aimed to examine the role of mTOR signaling in the pathophysiology of MS. Following Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, we screened preclinical and clinical studies using two databases and assessed the risk of bias specific to the study types. A total of 189 records were identified, of which 90 met the inclusion criteria for qualitative analysis. Studies using in vitro and in vivo (mainly rodent models, both sexes) models of MS, as well as MS patient tissue or data, consistently demonstrated that mTOR is involved in the MS-related processes neuroinflammation, myelination, autophagy, gliosis, mitochondrial dysfunction, and oxidative stress. mTOR inhibition reduces pro-inflammatory signaling and may enhance autophagy, offering neuroprotection. In contrast, activation of mTOR promotes remyelination by enhancing oligodendrocyte differentiation and maturation. These remyelinating effects may be masked in inflammatory environments, because activation of mTOR supports immune cell expansion and glial reactivity, inducing inflammation and oxidative stress. Overall, our findings underscore a dual role of mTOR in MS pathology, with important implications for disease stage and timing of intervention. Although mTOR is mechanistically important in MS, its therapeutic modulation is unlikely to be readily clinically translatable without a substantial risk of unintended and context-dependent effects.
5 July 2026
Read appraisal →The Journal of physiology
A deep learning-enabled toolkit for the 3D segmentation of ventricular cardiomyocytes
Segmentation of cardiomyocytes in microscopic 3D volumes is key to our understanding of cardiac (patho-)physiology; however, it poses substantial experimental and analytical challenges. Therefore, researchers often resort to inferring 3D information from 2D segmentations, which can lead to biased and incorrect conclusions. Deep learning-based methods are showing promise with respect to robustly segmenting objects in volumes acquired using various imaging modalities; yet, they have not been applied to high-resolution 3D cardiomyocyte segmentations, and suitable open-source tools and datasets are lacking. Here, we present a deep learning-enabled toolkit for segmentation of individual cardiomyocytes in 3D confocal microscopy volumes. We include a dataset of 73 volumes with expert annotations, covering seven species, including mouse, human, and elephant, and containing samples generated under different experimental conditions, such as post-myocardial infarction and ex vivo slice cultures. The toolkit additionally contains an image restoration workflow to address imaging-related artefacts, such as spatially varying blur. Our automatic cardiomyocyte segmentation workflow achieved an adapted Rand error of 0.063 ± 0.034 (∼94% voxel-pair agreement) on the test set. Our semi-automatic workflow reached a throughput of 3 cells min-1 on a challenging, previously unseen dataset. The toolkit and data are open-source and accessible through a dedicated graphical user interface. In summary, we provide an accessible toolkit enabling researchers to extract quantitative data on cardiomyocyte microstructure from 3D confocal image stacks of cardiac tissue. Given the size and diversity of our dataset, we expect our methods to perform well across species and experimental conditions, facilitating high-quality 3D reconstructions of large numbers of individual cardiomyocytes. KEY POINTS: 3D cardiomyocyte microstructure is a key determinant of cardiac function in health and disease. However, reliable extraction and quantification of 3D cardiomyocyte cytoarchitecture pose significant experimental and computational challenges. We present an effective experimental protocol and a deep learning-enabled toolkit for sample preparation and 3D analysis of cardiomyocyte morphology in ventricular myocardium. Our method is validated across seven species (mouse to human) and in samples prepared in diverse experimental conditions from a range of models, including myocardial infarction and ex vivo tissue culture, highlighting the robustness and versatility of our workflow. Our open-source dataset and toolkit enable large-scale analyses and extraction of realistic 3D geometries of ventricular microstructure. These can be used to explore a host of research questions and provide a new resource for modelling cardiac function at the cellular level.
4 July 2026
Read appraisal →Journal of hematology & oncology
Large language model-guided CAR-T in silico platform for cytokine optimization in liver cancer with low antigen density
CAR-T cell therapy has shown remarkable success in hematologic malignancies but remains limited in solid tumors such as liver cancer due to antigen heterogeneity, low target antigen density, and an immunosuppressive tumor microenvironment (TME). Cytokine engineering can enhance CAR-T persistence and effector function; however, the optimal cytokine payload may vary depending on tumor type, target antigen expression level, and microenvironmental context, making systematic experimental comparison time-consuming and labor-intensive. Here, we applied a large language model (LLM)-based CAR-T in silico platform to systematically evaluate cytokine engineering strategies, including IL-2, IL-7, IL-12, IL-15, and IL-18, in glypican-3 (GPC3)-targeted CAR-T cells for liver cancer. We used cytokine selection as a biologically grounded benchmark to test whether the platform could recover known CAR-T cell-relevant cytokine biology and support future novel predictions. Computational predictions identified IL-15 as the most effective enhancer, particularly against tumor cells with low GPC3 expression. Guided by these results, we generated cytokine-armored GPC3 CAR-T cells and performed in vitro and in vivo validation. IL-15-engineered CAR-T cells exhibited superior proliferation, persistence, and serial cytotoxicity against GPC3-low liver cancer cells. In human liver cancer xenograft models, IL-15-enhanced CAR-T cells achieved improved tumor control compared with conventional and other cytokine-engineered CAR-T cells. The recovery of IL-15 served as a positive benchmark supporting the validity of the LLM-guided CAR-T in silico workflow. Collectively, this study establishes an LLM-guided framework, schema-constrained for rational cytokine selection in CAR-T engineering and identifies IL-15 as a potent enhancer for targeting antigen-low liver cancers.
3 July 2026
Read appraisal →CPT: pharmacometrics & systems pharmacology
A Narrative Review of Artificial Intelligence for Drug Repurposing: Lessons From COVID-19 and Oncology (2020-2025)
Drug repurposing presents a cost-effective and time-efficient strategy to identify new therapeutic applications for existing drugs. Recent advances in artificial intelligence, including machine learning, deep learning, knowledge graphs, and natural language processing, have revolutionized this field by enabling automated discovery of drug-disease associations. This review examines the role of artificial intelligence in drug repurposing, drawing insights from two critical case areas: Coronavirus disease 2019 and oncology. We explore current trends, methodological frameworks, and technological innovations in artificial intelligence-driven drug repurposing, as well as challenges and emerging future directions. The findings of this paper underscore the transformative potential of artificial intelligence in biomedical research and justify its continued integration in pharmaceutical pipelines.
1 July 2026
Read appraisal →Science advances
A battery-free wireless intelligent aligner for spatially resolved, closed-loop theranostics of chronic oral diseases
Intelligent wearable theranostic systems are advancing personalized health care by enabling continuous monitoring and proactive therapy. However, managing chronic oral diseases such as caries and periodontitis is challenging due to the oral cavity's complex, spatially heterogeneous microenvironments and limited space for powered intraoral electronics. To address this, we developed a wireless, battery-free, and wearable intelligent therapeutic aligner (WiB-ITA) that integrates a butterfly-inspired dual-sided flexible integrated theranostic electrode array (iTEA) to simultaneously monitor compartment-matched pH and nitrite and electrochemically gate on-demand antimicrobial release. The system is powered and commanded via a relay-assisted near-field communication (NFC)-Bluetooth low energy (BLE) dual-hop link, enabling stable battery-free operation across tooth positions and full-arch usability. Validated from in vitro to preliminary human studies, WiB-ITA establishes a scalable, compartment-matched intraoral closed-loop theranostic framework. This integrated theranostic platform represents a breakthrough in personalized oral health care and provides a paradigm for managing other chronic conditions.
27 June 2026
Read appraisal →Journal of chemical information and modeling
Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives
Previously, we reported a dual combination based on 4-hydroxycoumarin and dodecanedioic acid that could synergistically bind to human serum albumin (HSA). However, optimizing this combination remains challenging and could often be guided by empirical selection and extensive experimental screening, which may limit the chemical diversity and suboptimal affinity. In this study, we established a systematic artificial intelligence framework that integrates computational optimization with wet-lab synthesis and experimental validation, enabling improvement of the dual combination while preserving the core chemotypes. We first trained a machine learning classifier on curated HSA binding data and used it as an external scoring function to guide reinforcement learning-driven scaffold decoration with LibINVENT, enabling goal-directed generation of coumarin derivatives and fatty acid derivatives. Candidate molecules were prioritized through multiparameter filtering and diversity-aware selection, followed by synthesis and experimental validation using surface plasmon resonance. The optimized representatives show nanomolar HSA binding and enhanced affinity compared to the original ligands. Molecular docking and molecular dynamics simulations further provide a mechanistic rationale for the affinity improvements by revealing additional stabilizing interactions and more favorable binding energetics at the corresponding HSA sites. Besides, the optimized coumarin derivative (CD1) is a warfarin-derived coumarin analogue, yet it did not show detectable anticoagulant activity in an acute clotting time assay, whereas warfarin did. Overall, this work demonstrates a practical AI-guided route to expand chemical diversity and improve affinity for a synergistic HSA binding combination.
23 June 2026
Read appraisal →Journal of chemical information and modeling
A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1
Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme implicated in cancer immune escape and remains an attractive therapeutic target despite recent clinical setbacks. We report a fully reproducible hierarchical virtual screening framework integrating scaffold-aware machine learning, ensemble docking, consensus scoring, and molecular dynamics simulations for robust prioritization of IDO1 inhibitors. A curated ChEMBL data set of IDO1 inhibitors was subjected to strict standardization, duplicate removal, and activity binarization at pChEMBL ≥6. Models were trained using scaffold-based splitting and nested cross-validation to prevent chemical series leakage. An ensemble of Random Forest, XGBoost and SVM classifiers achieved balanced predictive performance (ROC-AUC ≈0.88-0.89) with applicability domain filtering to ensure reliability. Prospective screening of FDA-approved drugs yielded 39 compounds within the applicability domain predicted as active. These were further evaluated through ensemble docking against multiple IDO1 crystal structures using GNINA with CNN rescoring. Consensus strategies were systematically benchmarked, demonstrating that best-Z-score aggregation outperformed mean, rank-based, and weighted methods in enrichment factor (EF) metrics. Two top-ranked candidates were subjected to 300 ns molecular dynamics simulations, revealing stable binding modes and persistent interactions with key catalytic residues. This study demonstrates that hierarchical integration of scaffold-aware machine learning with structure-based ensemble strategies enhances robustness and reduces false positives in virtual screening campaigns. The proposed workflow is generalizable and supports reproducible candidate prioritization in computational drug discovery. The complete implementation, including data processing, model training, and analysis steps, is provided as a fully executable Jupyter notebook available at https://github.com/rocco-b/IDO1-inhibitors-ML-and-docking-data.
23 June 2026
Read appraisal →Science advances
Discovery of TYR inhibitors from de novo molecular generation to dual-track lead optimization: 'Competition' between AI and chemists.
This study introduces a unified framework combining artificial intelligence (AI)-directed de novo molecular generation with dual-track lead optimization-comprising expert-guided strategies and AI-driven pathways-to discover tyrosinase (TYR) inhibitors for hyperpigmentation disorders. Using a reinforcement learning (RL)-based generative model, the lead compound AI10 was identified. Subsequent optimization followed two parallel routes. The expert-guided approach yielded AI10-m15 as the most potent TYR inhibitor, with notable antipigmentation activity and excellent cellular safety profiles. In contrast, the AI-driven pathway explored broader chemical spaces, generating unconventional chemotypes, exemplified by the potent TYR inhibitor AI10-a2, highlighting AI's capacity to uncover nonintuitive activity cliffs despite greater output variability. Systematic comparison revealed that the AI model offers exploratory diversity, whereas expert-guided optimization provides predictable improvements in activity and developability. In summary, starting from an AI-generated lead and subsequently integrating both expert-guided and AI-driven structural optimization strategies, these findings further underscore that combining AI technologies with experts' medicinal chemistry insights can substantially accelerate the discovery of viable candidate compounds.
21 June 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review
Histological analysis is a cornerstone of pre-clinical respiratory disease research. It enables assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows. To date, its use in pre-clinical respiratory models has not been synthesised.We conducted a scoping review following the Joanna Briggs Institute guidelines. We searched MEDLINE and Embase (inception - January 2025) for pre-clinical studies using AI to analyse histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate.Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Haematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Pre-processing methods (e.g. stain normalisation) were common, but annotation and training practices were inconsistently reported. AI model performance was generally high (accuracy ≥90%; seven studies); however, validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported.AI tools are poised to transform histological analysis in pre-clinical respiratory research. The field will be able to further harness AI to automate pre-clinical respiratory histological analysis by addressing gaps that we have identified in validation, transparency, and standardisation.
12 June 2026
Read appraisal →The AAPS journal
New Frontiers of Drug Development Through the Use of New Approach Methodologies
On November 2025, AAPS PharmSci 360 convened a symposium that included experts in the application of various types of New Approach Methodologies (NAMs). They shared their experiences and insights on topics that included microphysiological systems (MPS), the use of 3D organoids, and in silico tools. MPS systems were explored from the perspective of their testing and qualification through an academia-industry-government tissue chip testing consortium whose mission is to perform context-of-use-based testing of MPS and conduct comparative evaluations to support the use of MPS systems in safety assessments. 3D organoids were described and insights shared on how and when they may provide an appropriate tool for evaluating drug safety and effectiveness, and their use in supporting personalized medicine. Regarding in silico tools, examples were provided to describe their growing utility in predicting drug toxicity, population variability, and its potential benefits over traditional evidence-based toxicology. These tools include in silico models (e.g., physiologically based pharmacokinetic models), Artificial Intelligence (AI), and Machine Learning (ML) which, unlike other aspects of model informed drug development, reduce reliance on predefined models and allows for the integration of diverse sources of data. Computationally, these tools can generate predictions in hours where it would otherwise have taken weeks or months (and extensive experimentation). Within this meeting report, we highlight the key issues discussed during that symposium and share additional aspects to consider when developing a NAMs-based roadmap for specific contexts of use.
1 June 2026
Read appraisal →Neuroscience
Therapeutic efficacy of engineered exosomes in Alzheimer's disease: A systematic review and meta-analysis of preclinical animal models
Engineered exosomes are modified extracellular vesicles designed to enhance targeting and cargo delivery, and they have been proposed as a therapeutic strategy for Alzheimer's disease. We systematically reviewed preclinical animal studies evaluating engineered exosomes, synthesized evidence from comparisons with disease models and with natural exosomes, and reported the study in accordance with the PRISMA 2020 checklist. Outcomes included spatial learning and memory assessed by the Morris water maze, amyloid beta pathology, tau phosphorylation, and neuroinflammatory markers. Random effects meta-analyses suggested that engineered exosomes improved Morris water maze performance and reduced amyloid beta burden and pro-inflammatory cytokines compared with natural exosomes, whereas evidence regarding tau phosphorylation was limited and largely qualitative, and the overall certainty of evidence was low to very low. These findings support further investigation of engineered exosomes, but conclusions should be interpreted cautiously until confirmed by rigorously designed and blinded preclinical studies and clinical trials with standardized protocols.
25 May 2026
Read appraisal →Journal of the Egyptian National Cancer Institute
Preclinical efficacy of drug delivery systems in colon cancer therapy: a systematic review and meta-analysis of in vivo animal studies
BACKGROUND: Drug delivery systems (DDS) offer a promising strategy to enhance the therapeutic index of chemotherapeutic agents in colon cancer by improving tumor targeting, circulation time, and controlled drug release. Despite extensive preclinical research, a quantitative synthesis evaluating the efficacy of DDS and the influence of design parameters remains lacking. METHODS: We conducted a systematic review and meta-analysis of preclinical in vivo studies assessing DDS-based chemotherapy in murine models of colon cancer. A comprehensive search of PubMed, EMBASE, Scopus, Google Scholar, Cochrane CENTRAL, and ClinicalTrials.gov was performed through October 15, 2025. Outcomes included tumor growth inhibition, with subgroup analyses examining the effects of DDS platform, chemotherapeutic agent, targeting strategy, ligand type, and route of administration. RESULTS: Twenty-three studies comprising 25 experiments and 539 animals were included. Overall, DDS-based therapies significantly reduced tumor growth compared with controls ((WMD: - 557.7; 95% CI: - 716.8 to - 398.5; I²=88%; p < 0.001) and free-drug administration ((WMD: - 276.3; 95% CI: - 367.6 to - 185.1; I²=78%; p < 0.001). Both targeted and non-targeted DDS significantly reduced tumor growth compared with free drug treatment, while targeted DDS showed significantly greater efficacy than non-targeted systems (WMD: - 240.3; 95% CI: - 399.4 to - 81.25; I²=59%; p = 0.003). Although no statistically significant differences were observed between DDS platforms or chemotherapeutic agent subgroups, micelle-based systems and DDS formulations incorporating SN-38 or doxorubicin tended to show greater tumor growth reduction. Intravenous administration demonstrated significantly greater efficacy than intraperitoneal delivery, while hyaluronic acid- and aptamer-based targeting strategies achieved the largest tumor growth inhibition. Risk-of-bias assessment indicated moderate methodological quality, with variability in reporting of randomization, blinding, and sample size calculations. CONCLUSIONS: DDS-based chemotherapy consistently improves antitumor efficacy in preclinical colon cancer models, with targeting strategy, platform type, chemotherapeutic agent, and administration route influencing outcomes. These findings support the rational design of DDS platforms and underscore their translational potential. Rigorous preclinical study design and standardized reporting of efficacy and safety are essential to facilitate clinical translation.
21 May 2026
Read appraisal →Journal of neuro-oncology
cGAS-STING agonists in preclinical glioblastoma animal models: a systematic review of tumor microenvironment modulation and survival outcomes
BACKGROUND/PURPOSE: Glioblastoma (GBM) is characterized by an immunosuppressive tumor microenvironment and poor responsiveness to immune checkpoint blockade, driving interest in cGAS-STING pathway activation to stimulate antitumor immunity. We performed a systematic review of preclinical in vivo studies evaluating STING agonists in glioblastoma, with emphasis on immune microenvironment effects and survival outcomes. METHODS: A systematic review of PubMed, Cochrane, and Embase was conducted through February 2025 following PRISMA guidelines. Studies were included if they evaluated STING agonists in live animal models of GBM and reported survival outcomes or tumor microenvironment changes. Two reviewers independently screened studies and extracted data. Risk of bias was assessed qualitatively based on study design and reporting characteristics. Given heterogeneity in models, treatments, and outcomes, results were synthesized descriptively. RESULTS: Fourteen studies met the inclusion criteria, spanning synthetic and natural cyclic dinucleotides and non-cyclic STING agonists. Across diverse delivery methods, including intracranial injection, hydrogels, and nanoparticle systems, STING agonists were associated with increased CD8 + T-cell and NK cell infiltration and repolarization of tumor-associated macrophages toward pro-inflammatory phenotypes. Several studies reported prolonged survival, including long-term tumor clearance in select models. Combination therapies with immune checkpoint inhibitors or radiotherapy showed synergistic effects in some studies. CONCLUSIONS: STING agonists can enhance anti-tumor immune responses and prolong survival in preclinical GBM models by promoting cytotoxic lymphocyte recruitment and remodeling the tumor-associated myeloid landscape. However, translation remains challenging due to limitations of current models, the limited clinical traction STING modulation has thus far achieved in other cancer types, incomplete understanding of cell-type-specific STING activation in the brain, and the possibility that sustained STING signaling may drive maladaptive inflammation rather than durable antitumor immunity.
12 May 2026
Read appraisal →Scientific reports
scDecorr: feature decorrelation based representation learning enables self-supervised alignment of multiple single-cell experiments
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in complex biological systems. However, analyzing and integrating scRNA-seq data poses unique computational challenges due to sparsity, high variability, and technical batch effects. Here, we propose a novel framework called scDecorr for robust representation learning and data integration for scRNA-seq analysis. Our approach leverages the idea of feature decorrelation-based self-supervised learning (SSL) to obtain efficient low-dimensional representations of individual cells without relying on cell-type annotations. By maximizing similarity among distorted embeddings while decorrelating their components, scDecorr captures the biological signature while eliminating technical noise. Furthermore, scDecorr incorporates unsupervised domain adaptation to bridge the gap between batches with different distributions, enabling effective integration of scRNA-seq data from diverse sources. Our framework achieves domain-invariant representations by learning cell embeddings independently across domains and employing domain-specific batch normalization. We evaluate scDecorr on a variety of single-cell datasets and demonstrate its ability to integrate batches without losing the inherent biological variance, thereby facilitating optimal clustering. The representations generated by scDecorr also exhibit robustness in label transfer tasks, allowing for effective transfer of cell-type labels from reference to query datasets. Overall, scDecorr offers a powerful tool for efficient analysis and integration of large and complex scRNA-seq datasets, advancing our understanding of cellular processes and disease mechanisms. The code is available here https://github.com/hayatlab/scdecorr .
3 May 2026
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