Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 17 appraisals
Journal of medical Internet research
Addressing the Key Challenges of Decentralized Clinical Trials in Europe: Multistakeholder Perspective Delphi Study
BACKGROUND: Decentralized clinical trials (DCTs) represent an emerging model in clinical research, accelerated by the restrictions imposed during the COVID-19 pandemic. By leveraging digital technologies and local health care resources, DCTs aim to increase accessibility and reduce participant burden compared to traditional site-based models, which often face recruitment failures and high attrition rates. While various regulatory initiatives in Europe, such as the Accelerating Clinical Trials in the European Union program and the European Medicines Regulatory Network recommendation paper (updated in October 2025), have sought to facilitate their implementation, the widespread adoption of DCTs remains limited due to significant operational, regulatory, and technological challenges, including platform fragmentation and gaps in digital literacy. OBJECTIVE: This study aimed to identify and prioritize actionable solutions to the main challenges of DCT implementation in Europe from a multistakeholder perspective, gathering insights to address specific ethical, legal, and operational barriers. METHODS: Building on a preceding strengths, weaknesses, opportunities, and threats analysis, a 2-round Delphi study was conducted, involving 26 experts in clinical trials, ethics, law, regulation, and patient engagement between March and May 2023. In the first round, 309 open-ended responses were collected via REDCap (Research Electronic Data Capture; Vanderbilt University) surveys and underwent systematic inductive content analysis using ATLAS.ti (ATLAS.ti Scientific Software Development GmbH) with independent double coding. This process resulted in 244 unique proposals that were categorized according to 6 key challenges. In the second round, 39 synthesized proposals were evaluated using a 4-point Likert scale. Consensus was defined as ≥80% agreement on the appropriateness of each proposal. RESULTS: High levels of consensus were achieved, with 32 out of the 39 proposals reaching the threshold and 14 achieving 100% unanimity. Overall, 82% of the proposals were rated as "appropriate" or "very appropriate." Key recommendations included providing support and training for health care professionals, enhancing investigational medicinal product and biological sample logistics through validated technologies, improving collaboration with local health care providers, fostering regulatory harmonization while respecting national specificities, strengthening capacity-building initiatives, and promoting accessible, user-friendly digital tools supported by hybrid trial models. Conversely, proposals such as peer-to-peer participant support and the centralization of ethics reviews at the European Union level failed to reach consensus. CONCLUSIONS: The study offers a prioritized compilation of expert-driven recommendations for overcoming current barriers to DCT implementation in Europe. The adoption of these recommendations could support the development of more inclusive, efficient, and sustainable decentralized research frameworks across diverse health care systems.
12 July 2026
Read appraisal →European journal of medicinal chemistry
Machine learning-based prediction of drug lactation risk: Bridging molecular features and breastfeeding safety
BACKGROUND: Breastfeeding medication safety assessment presents critical challenges due to limited clinical evidence and ethical constraints on lactation studies. While existing computational models focus on predicting pharmacokinetic surrogates such as milk-to-plasma ratios, these endpoints lack direct clinical actionability for therapeutic decision-making. METHODS: We developed LRCpredictor, a computational framework for predicting drug lactation risk based on Dr. Thomas W. Hale's evidence-based Lactation Risk Categories (LRC) system. A dataset of 391 drugs (179 high-risk L4/L5, 212 low-risk L1/L2) was characterized using three complementary molecular representations. Systematic feature engineering with four selection algorithms identified optimal feature subsets. Five ensemble machine learning algorithms were evaluated through cross-validation and independent testing. Multi-level SHAP analysis and structural alert mining were employed to elucidate molecular determinants of lactation risk. RESULTS: The optimal Gradient Boosting Decision Tree (GBDT) model using 35 selected features achieved robust cross-validated performance (AUC = 0.80, MCC = 0.52) and demonstrated notably superior discrimination for extreme risk categories (L1 vs. L5: AUC = 0.85, MCC = 0.62). Analysis revealed lactation risk is governed by the complex interplay of electronic properties, structural topology, electrotopological characteristics, polarizability and drug-likeness attributes. Matched molecular pair analysis demonstrated how specific structural modifications translate into interpretable risk changes. We further identified 18 structural alerts, including five exclusive to high-risk drugs, enabling rapid toxicophore recognition during risk assessment and drug design. CONCLUSIONS: LRCpredictor provides the first interpretable framework for clinically actionable lactation risk prediction with mechanistic molecular insights. The freely accessible web platform (https://lrcpredictor.streamlit.app/) with integrated visual explanations supports evidence-based decision-making for healthcare providers and drug design optimization.
7 June 2026
Read appraisal →Cancer chemotherapy and pharmacology
A randomized, open-label, two-way crossover clinical trial to evaluate the food effect on pharmacokinetics and safety of DHP107 in patients with advanced solid tumors: the FEEL study
PURPOSE: The phase 1, randomized, open-label, two-way crossover FEEL study assessed the effect of food on the pharmacokinetics of DHP107, an oral paclitaxel with a novel lipid formulation designed to be systemically absorbed without Cremophor EL, in patients with advanced solid tumors. METHODS: Patients were randomized 1:1 to DHP107 (200 mg/m2 orally twice daily on Days 1, 8, 15 every 28 days for two cycles) treatment sequences: fasted-fed (first DHP107 dose administered after overnight fast; n = 13) and fed-fasted (first DHP107 dose administered after a high-fat meal; n = 12). The primary objective was to evaluate the effect of food on DHP107 pharmacokinetics. The secondary objective was DHP107 safety; efficacy was also assessed (both in all patients regardless of dosing sequence). RESULTS: Mean systemic exposures were similar for paclitaxel and its metabolites under fed and fasted conditions. Geometric mean ratios (fed:fasted) for paclitaxel were: maximum concentration (Cmax), 101.00% (95% CI 91.93-110.95%); area under the curve (AUC0-72), 102.92% (95% CI 95.04-111.45%); and AUC0-inf 104.76% (95% CI 96.44-113.80%), meeting criteria for no food effect. Common adverse events across the study period included diarrhea (fasted-fed 61.5%; fed-fasted 41.7%), vomiting (fasted-fed 30.8%; fed-fasted 8.3%), and neutropenia (fasted-fed 23.1%; fed-fasted 16.7%). Two fed-fasted patients (16.7%) had partial responses and six had stable disease (50.0%); seven fasted-fed patients had stable disease (53.8%). CONCLUSION: This study suggests that the pharmacokinetics of DHP107 are unaffected by food and DHP107 can be taken with or without food. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT04675528; 107CS-7.
31 May 2026
Read appraisal →mAbs
Prediction of antibody non-specificity using protein language models and biophysical parameters
The development of therapeutic antibodies requires optimizing target binding affinity and pharmacodynamics, while ensuring high developability potential, including minimizing non-specific binding. In this study, we address this problem by predicting antibody non-specificity by two complementary approaches: (1) antibody sequence embeddings by protein language models (PLMs) and (2) a comprehensive set of sequence-based biophysical descriptors. We benchmark the PLM embeddings against interpretable, sequence-derived biophysical descriptors and use fragment-specific models (variable heavy (VH) and variable light (VL) chains, concatenated regions, and individual complementary-determining regions (CDRs) to identify region-level determinants of non-specificity in functional antibodies that bind defined targets. These models were trained on previously published human and mouse antibody data and tested on three public datasets. We show that non-specificity is best predicted from the VH domain and heavy-chain CDRs. These region-level analyses show that heavy-chain features, especially H-CDR3, dominate the predictive signal. The top performing PLM, a VH domain-based Evolutionary Scale Modeling 1 v LogisticReg model, resulted in 10-fold cross-validation accuracy of up to 71%. While predictive accuracy is comparable to classical machine-learning baselines, PLM-based embeddings provide sequence-context representations that yield consistent region-level attribution and provide complementarity to the classical models by enhancing the reliability of predicted non‑specificity scores when used in combination. Our biophysical descriptor-based analysis identified the isoelectric point as a key driver of non-specificity, consistent with previous reports. Our findings highlight the importance of biophysical properties in predicting antibody non-specificity and highlight the potential of PLMs for the development of antibody-based therapeutics. These conclusions are robust to alternative class definitions and are not driven by VH-VL mutational bias. We illustrate the generalizability and practical use of the PLM approach by extending it to therapeutic antibodies and nanobodies, providing a tool for early-stage developability assessment, and we make the models publicly available.
29 May 2026
Read appraisal →Journal of computer-aided molecular design
ADMET-vault: an interactive framework for real-time ADMET prediction and molecular optimization
Drug discovery pipelines face persistent challenges, as candidates that show promising results in early laboratory studies are found ineffective in clinical trials-often because their absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles were not well understood early enough. In this study, an interactive machine-learning based platform, ADMET-Vault, is presented, which brings ADMET prediction directly into the molecular design process, allowing researchers to assess and optimize drug candidates during the molecule design phase itself. ADMET-Vault integrates multiple molecular representations including physicochemical descriptors, molecular fingerprints, and graph neural network embeddings. The platform evaluated 12 benchmark datasets from Therapeutics Data Commons using scaffold-based validation. ADMET-Vault demonstrated consistent predictive performance across multiple ADMET endpoints. Strong results were observed for clearance related properties, intestinal absorption, hepatotoxicity, and aqueous solubility. Moreover, regression showed mean absolute error value that ranged from 0.28 (Caco-2 permeability, expressed as log cm/s) to 7.68 (plasma protein binding rate, expressed as % bound), with Spearman's rank correlation coefficients between 0.44 and 0.66. Classification tasks showed area under the receiver operating characteristic curve values ranged between 0.87 and 0.99. ADMET-Vault's main contribution is not just in its predictive accuracy. It combines reliable machine learning models with a live molecular editor. This allows researchers to see how a structural change impacts drug-like properties instantly. This interactive design-predict-optimize loop fills a long-standing gap in early-stage drug discovery workflows.
27 May 2026
Read appraisal →Journal of pharmacokinetics and pharmacodynamics
Development of a novel rabeprazole modified release formulation to reduce the dosing frequency: A trial and triumph with physiologically based biopharmaceutics modeling.
Advanced in silico tools, such as physiologically based biopharmaceutics models (PBBM) and physiologically based pharmacokinetic models (PBPK), play a pivotal role in model-informed formulation development (MIFD). In the present study, these methodologies were utilized in the development of a novel rabeprazole modified-release (MR) formulation for improved patient compliance by reducing the dosing frequency relative to existing delayed-release (DR) formulation. A MR formulation containing combination of delayed release and pulsatile release components was designed based on the hypothetical dissolution targets. A PBBM was first established using literature-derived clinical data and used to simulate the performance of formulations with varied dissolution profiles. Initial MR formulation prototypes (once a day) were evaluated in pilot-1 bioequivalence (BE) study against reference formulation (twice a day) but results indicated infra bioequivalence outcome. After pilot-1 study, the formulation and the dissolution method were optimized and then model was refined to predict the pilot 1 outcomes. The validated model was then employed to select suitable pilot-2 prototypes and to prospectively predict their BE outcomes under fasted and fed conditions. The predicted BE ratios were in good agreement with the observed pilot-2 study results. Therefore, the same model was used to make prospective predictions with increased subjects to understand the in vivo performance of pivotal test formulation and lead to the successful bioequivalence outcome, product commercialization, ultimately aiding in increased patient compliance. This work highlights the value of a model-informed strategy in accelerating formulation optimization, reducing development timelines and costs, and strengthening decision-making within pharmaceutical development programs.
18 May 2026
Read appraisal →Journal of chemical information and modeling
In Silico Prediction of Potential Bile Salt Export Pump Inhibitors Using Pharmacophore Models and Consensus Machine Learning
The ATP-dependent bile salt export pump (BSEP) is a transporter responsible for moving bile salts from hepatocytes into bile canaliculi. Inhibition of BSEP is a known risk factor of cholestatic-drug-induced liver injury (DILI) probably caused by the accumulation of toxic bile salts in the liver. Since DILI is a major cause for attrition during drug development and postmarketing withdrawal or black box warnings, an IC50 of BSEP higher than 25 μM is advised. Here, we describe the investigation of several in silico approaches to map potential BSEP inhibitors. A consensus machine learning classification model is presented, which accurately predicts BSEP inhibitors and can assist in the prioritization of in vitro testing. An applicability domain was derived for the model, and it was validated by in vitro measurements of predicted BSEP inhibitors where the model was able to correctly flag eight out of 11 compounds.
13 May 2026
Read appraisal →Immunologic research
An integrated AI-driven vaccine design process: a systematic review of workflows from generative design to translational prediction
Traditional vaccine development faced significant hurdles, including lengthy timelines and high costs, which hindered rapid responses to pathogens. Although the emergence of AI offered transformative potential, the necessity for a fully integrated workflow was often overlooked in studies focusing on individual tools. This review addressed a critical gap by synthesizing AI technologies across the vaccine design process, focusing on the integrated workflow from antigen discovery to clinical translation. A systematic framework was required to connect disparate tools and ensure seamless transitions. Consequently, this study provided a comprehensive roadmap for pandemic preparedness and vaccine discovery. A systematic analysis based on the PRISMA framework (2015-2024) was conducted, and 19 landmark articles were reviewed.It was demonstrated that the paradigm shift from predictive to generative AI offered unprecedented opportunities for developing novel antigens and adjuvants with superior immunogenicity. Synthesis of the literature revealed rapid progress toward sophisticated deep learning. Transformer models and Protein Language Models emerged as dominant for epitope prediction, while AlphaFold2 became the standard for structural modeling. The advent of generative AI for de novo antigen design represented the leading edge of the discipline. Additionally, AI-enhanced molecular dynamics and digital twin simulations accelerated clinical validation and manufacturing scalability. The "Integrated AI Workflow for Vaccine Design and Development" was emphasized as a comprehensive system and a prerequisite for sustainable innovation. Overall, this analysis served as a strategic roadmap for utilizing AI as a transformative framework for next-generation vaccine discovery and pandemic preparedness.
12 May 2026
Read appraisal →Therapeutic innovation & regulatory science
Applications and Challenges of Artificial Intelligence and Big Data in Drug Regulation
Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU®, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.
9 May 2026
Read appraisal →Drug development research
Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation
Drug discovery remains a lengthy, costly, and high-risk endeavor, often requiring over a decade from target identification to clinical translation. Artificial intelligence (AI) is reshaping this paradigm by enabling more efficient and accurate decision-making across the discovery and development pipeline. Advances in machine learning, deep learning, and natural language processing now support target identification, hit finding, lead optimization, and drug repurposing with unprecedented speed and precision. AI-driven insilico platforms further enhance early-stage predictability by forecasting toxicity, pharmacokinetics, and developability, thereby reducing late-stage attrition. This review critically examines the evolving role of AI in modern drug discovery and its expanding impact on pharmaceutical formulation development and personalized medicine. Collaborative models between AI developers and the pharmaceutical industries, essential for accelerating translational outcomes, are also highlighted. Finally, key challenges, including algorithmic transparency, data quality, interoperability, and regulatory acceptance, are discussed, along with future directions for harnessing AI's full potential in pharmaceutics.
6 May 2026
Read appraisal →Journal of the American Society of Nephrology : JASN
Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence
KEY POINTS: Artificial intelligence models effectively generalized across studies and animal models and reduced translational gaps when applied to human biopsies. Artificial intelligence assistance reduced study evaluation turnaround times by up to 90% versus manual whole slide imaging scoring, matching expert-level performance. Self-supervised learning captured diabetic kidney disease-relevant features and mitigated expert-specific bias. BACKGROUND: Assessment of pathology end points in animal models of diabetic kidney disease is time-consuming and prone to expert bias. In addition, the sparsity of human kidney biopsy data hinders the development of translational models from animals to humans. METHODS: We developed an artificial intelligence (AI)-driven workflow to streamline histopathologic assessments in animal models of diabetic nephropathy. Our approach ( 1 ) detected glomeruli in whole slide images, ( 2 ) enabled fast expert scoring through an annotation tool, and ( 3 ) automated scoring. By leveraging unlabeled preclinical data for self-supervised learning, we enhanced AI scoring performance, reduced expert bias, and enabled the translation of AI scoring from animal models to human biopsies. To translate AI models from preclinical studies to human biopsies, we introduced a method that adjusted the feature extractor to human-specific features during inference without the need for annotated examples. RESULTS: Our annotation tool streamlined glomerular scoring, reducing turnaround time by 80%. Supervised AI models outperformed expert agreement and further reduced turnaround time by 90%, demonstrating generalization across studies involving both the same and different animal models. Without supervision, the self-supervised model achieved a κ value of 0.78, effectively identifying glomerular changes without guidance. Incorporating self-supervised learning into supervised training improved performance to κ=0.84 and reduced bias compared with individual experts ( P < 0.001). Our translational approach achieved a κ value of 0.63 on human glomeruli, although the model was trained exclusively on mouse glomeruli scores, reducing the translational gap by 45%. CONCLUSIONS: In this study, we accelerated and enhanced pathology readouts in a real-life pharmaceutical industry setting. We show that AI-assisted scoring reduced pathologists' workload and expedited study assessments. Self-supervised learning captured intrinsic properties of kidney morphology without expert annotation and reduced expert bias and translational discrepancies, greatly facilitating translational activities in drug development for patients with diabetic kidney disease.
5 May 2026
Read appraisal →Journal of medicinal chemistry
Automated Molecular Design in BRADSHAW, Applied to the Optimization of ERAP1 Inhibitors
Generative design and machine learning are increasingly prevalent in medicinal chemistry. To pilot the comprehensive use of automated molecular design on a project, the BRADSHAW platform was used to optimize a series of inhibitors of Endoplasmic Reticulum Aminopeptidase 1 (ERAP1), an emerging target in cancer immunotherapy and autoimmune diseases. Through four consecutive iterations applying in silico molecular generation, property prediction and filtering, we conducted a multiparameter optimization of potency, physicochemical properties and pharmacokinetics. Continuous refinement of Machine Learning (ML) models led to improved scoring accuracy and compound quality, culminating in identification of in vitro and in vivo tool molecules. We also discuss our reflections on the pilot and integration of automated design into medicinal chemistry projects, including observations of the human factors resulting from increased use of computational design, and recommendations for future projects.
25 Apr 2026
Read appraisal →Journal of molecular modeling
HopWD-DTA: a novel framework for drug-target affinity prediction fusing multi-hop neighborhoods and deep features
CONTEXT: Accurate prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery, but it remains a significant challenge. While deep learning methods have shown promise, many existing models struggle with representing long-range protein relationships within protein-protein interaction (PPI) networks, often suffering from performance degradation as graph neural network layers deepen. To overcome this limitation, we developed HopWD-DTA, a novel framework designed to effectively capture both local and global protein features. The core innovation lies in integrating multi-hop neighborhood information from PPI networks with deep structural features of proteins and drugs. Comprehensive evaluations on the Davis, KIBA, and Human benchmark datasets demonstrate that HopWD-DTA achieves state-of-the-art performance, significantly improving DTA prediction accuracy over existing cutting-edge solutions. METHODS: The HopWD-DTA framework consists of separate branches for protein and drug feature extraction. For proteins, structural features from residue contact maps are first encoded using a Graph Convolutional Network (GCN). These features, along with InterPro annotations, are then embedded into a PPI network. We introduce a multi-hop neighborhood serialization technique, which generates a sequence of feature matrices representing different neighborhood scopes. This sequence is processed by a Variational Autoencoder (VAE) to learn a robust protein representation. For drugs, molecules are represented as graphs from SMILES strings and encoded via a GCN, followed by a novel Wide-and-Deep Path (WDPATH) module to capture both macroscopic and microscopic features. The final protein and drug features are concatenated and fed into a multilayer perceptron for affinity prediction. The model was implemented using PyTorch and RDKit.
15 Apr 2026
Read appraisal →Journal of chemical information and modeling
Compressing Chemistry Reveals Functional Groups
We introduce the first formal large-scale assessment of the utility of traditional chemical functional groups as used in chemical explanations. Our assessment employs a fundamental principle from computational learning theory: a good compression of data should reveal a good explanation. We introduce an unsupervised learning algorithm based on the Minimum Message Length (MML) principle that searches for substructures that compress around three million biologically relevant molecules. We demonstrate that the discovered substructures contain most human-curated functional groups as well as novel larger patterns with more specific functions. We also run our algorithm on 24 specific bioactivity prediction data sets to discover data set-specific functional groups. Fingerprints constructed from data set-specific functional groups are shown to significantly outperform other fingerprint representations, including the MACCS and Morgan fingerprint, when training ridge regression models on bioactivity regression tasks.
14 Apr 2026
Read appraisal →Journal of chemical information and modeling
ToxPLTC: Peptide Toxicity Prediction by Integrating Pretrained T5 Protein Language Model and Text Convolutional Neural Network
Peptide-based therapeutics show promising potential in treating a range of diseases, such as diabetes, cancer, and chronic pain. However, critical challenges, including peptide toxicity, immunogenicity, and stability deficiencies of peptides, have become major obstacles to their direct clinical application. Traditional toxicity testing methods based on a wet lab are not only costly but also time-consuming. In contrast, the classification model based on deep learning provides a new technical path for the efficient identification of peptide toxicity. In this study, we propose a deep learning framework called ToxPLTC, which employs the ProtT5 protein language model based on the Transformer architecture for pretraining peptide sequences, adopts the borderline SMOTE algorithm to handle an imbalanced training set data, and utilizes a text convolutional neural network combined with a fully connected layer for classification. Additionally, visualization analysis, motif analysis, and mutation-scan analysis are performed to understand the function of each module and enhance the interpretability of our model. The applicability domain is constructed based on the K-NN strategy to define the effective prediction range of our model to ensure the reliability of model predictions. The ToxPLTC model achieves a balanced accuracy of 93.02% on independent test set 1 and 88.04% on independent test set 2. Experimental results demonstrate that our model outperforms existing models on independent test sets and has good generalization ability. The ToxPLTC model possesses great potential as a valuable and robust tool for peptide-based drug development. The source data sets and codes can be available at the following GitHub repository: https://github.com/yunyunliang88/ToxPLTC.
14 Apr 2026
Read appraisal →Trials
The use of complex clinical trials: a regulatory review.
BACKGROUND: Complex clinical trials offer flexibility in evaluating multiple treatments or diseases simultaneously. These trials often feature adaptive designs, common controls, and the potential to add new arms. However, the increased complexity raises methodological challenges particularly regarding multiple testing or the adequate choice of control groups. Despite guidance from regulatory bodies like the European Medicines Agency and the U.S. Food and Drug Administration, uncertainties remain about the regulatory acceptance of these trials. METHODS: This systematic review examines scientific advice procedures of products in the remit of the Paul-Ehrlich-Institut for complex clinical trials to highlight key concerns and regulatory feedback. We identified 30 scientific advice procedures corresponding to 29 different complex clinical trials. RESULTS: Our findings reveal an increasing number of complex trial designs proposed by applicants. A lack of multiplicity control due to multiple arms was generally considered acceptable by regulators in exploratory trials and if cohorts can be considered independent. Additionally, the use of common control groups is frequently proposed by applicants. The review underscores the importance of pre-planning for new treatment arms, appropriate multiplicity control, and the definition of control groups in trial designs. CONCLUSION: Overall, our findings suggest that regulatory concerns regarding complex trials largely align with those in traditional trial designs, though their complexity requires careful, case-by-case consideration. Early engagement with regulatory agencies can be crucial to ensure the successful design and implementation of these trials.
12 Apr 2026
Read appraisal →Cell
Generation of antigen-specific paired-chain antibodies using large language models.
The traditional process of antibody discovery is limited by inefficiency, high costs, and low success rates. Recent approaches employing artificial intelligence (AI) have been developed to optimize existing antibodies and generate antibody sequences in a target-agnostic manner. In this work, we present MAGE (monoclonal antibody generator), a sequence-based protein language model (PLM) fine-tuned for the task of generating paired human variable heavy- and light-chain antibody sequences against targets of interest. We show that MAGE can generate novel and diverse antibody sequences with experimentally validated binding specificity against SARS-CoV-2, an emerging avian influenza H5N1, and respiratory syncytial virus A (RSV-A). MAGE represents a first-in-class model capable of designing human antibodies against multiple targets with no starting template.
1 Dec 2025
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