Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 31 appraisals
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 →Journal of medicinal chemistry
MEG-mod: A Multiview Enhanced Graph Neural Network for Knockdown Efficiency Prediction of Chemically Modified siRNA
Chemical modification is essential for improving the stability and knockdown efficiency of siRNAs. However, the combinatorial complexity of modification types and positions makes rational design difficult. Here, we propose MEG-mod, a deep learning framework for predicting the knockdown efficiency of chemically modified siRNAs. By integrating a literature-reading agent, manual curation, and public database resources, we construct an expanded data set. MEG-mod jointly models sequence context, physicochemical properties, chemical modification features, and duplex structural relationships. A structure-aware Transformer-based graph neural network captures base-pairing and adjacency dependencies within double-stranded siRNAs, while a modification-base fusion module models context-dependent modification effects. MEG-mod outperforms existing methods, achieving a pearson correlation coefficient of 0.9171. The attention mechanism enables interpretable analysis of key modification positions and modification types, and provides recommendations consistent with reported experimental designs. MEG-mod is available as a public web server (https://lmmd.ecust.edu.cn/megmod/), providing a practical tool for prediction and candidate prioritization of chemically modified siRNAs.
13 June 2026
Read appraisal →Journal of medicinal chemistry
Developing Predictive Models by Sharing Predictions - An Investigation of a Federated Learning Approach for ADMET Predictions
Machine learning models for ADMET prediction benefit from large, diverse data sets, yet such data are typically siloed across organizations. Federated learning (FL) enables collaborative modeling while preserving data privacy. Here, we investigate a student-teacher model (STM) framework in which organizations train internal models on proprietary data and share predictions on a public data set to generate pseudolabels for a centralized student model. As a proof of concept, 11 pharmaceutical companies contributed predictions for rat steady-state volume of distribution, yielding a pseudolabeled data set of ∼133,000 compounds. The resulting student model achieved performance comparable to individual teacher models on an external test set (RMSE ≈ 0.51 vs 0.47-0.61). Compared with FL approaches such as MELLODY and Effiris, STM offers a simpler workflow that avoids direct data sharing or iterative collaboration, providing a practical and scalable framework for secure cross-company model development.
13 June 2026
Read appraisal →Journal of medical Internet research
From Virtual Molecules to Clinical Trials: How AI Is Reshaping Preclinical Drug Discovery
Pharmaceutical development is typically a long and arduous process, but advances in artificial intelligence may help streamline each stage of this pipeline. In this News and Perspectives article, JMIR Correspondent Benedette Cuffari reports on recent innovations and their potential implications for drug discovery.
1 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 →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
Multi-objective optimization in population pharmacokinetic model selection and optimization: application of NSGA-II in pyDarwin
The selection of a "good" model usually involves a combination of objective and subjective criteria. Although many aspects of model quality can be expressed numerically, certain desirable characteristics remain difficult-or even impossible-to quantify precisely. Multi-objective optimization (MOO) provides a systematic way to handle this challenge by explicitly incorporating and balancing both objective (measurable) and subjective (judgment-based) considerations when choosing among candidate solutions. The generated Pareto front represents a set of non-dominated models where no single solution can be improved in one objective without sacrificing the performance in another objective. Using the non-dominated sorting genetic algorithm II (NSGA-II), an implementation of MOO, we simultaneously considered objective function value and number of estimated parameters as competing criteria. Concentration measurements of 17-DMAG, quetiapine, clozapine and ziprasidone were applied to build population pharmacokinetic models through traditional stepwise search, machine learning based single-objective hybrid genetic algorithm (SOHGA) and MOO. Local downhill search with MOO was also assessed in this study. While both objectives improved, models with lower objective function value generally contained more estimated parameters. The number of non-dominated solutions for DMAG, ziprasidone, clozapine, and quetiapine was 17, 9, 9, and 13, respectively. The optimal model selected by SOHGA appeared on the Pareto front for DMAG, ziprasidone and clozapine datasets. Overall, MOO provides objective transparency to the cost of tradeoffs between competing model objectives, allowing researchers to better contextualize subjective criteria (e.g., biological plausibility, improvements in diagnostic plots) when aligning model selection with clinical context.
18 May 2026
Read appraisal →Computers in biology and medicine
Generative deep learning-driven de novo design of targeted MAP4K6 inhibitors
The discovery of selective small-molecule inhibitors for pharmacologically underexplored kinases remains a critical barrier to precision drug development, particularly under data-sparse conditions, where ligand annotations are scarce. Here, we present MolEvoRNN, a generative deep learning (GDL) model that enables the de novo design of chemically valid, structurally diverse, and target-adapted compounds in low-data regimes. Through transfer learning and temperature-controlled sampling, MolEvoRNN efficiently navigates the kinase-relevant chemical space while preserving scaffold novelty, drug-likeness, and synthetic tractability. Applied to MAP4K6, a poorly characterized serine/threonine kinase implicated in hepatocellular carcinoma (HCC), MolEvoRNN generated >49,000 chemically valid molecules with >80% novel Bemis-Murcko scaffolds. MolEvoRNN outperformed the baseline GDL models across key generative metrics, including diversity (1.00), uniqueness (1.00), novelty (1.00), and internal diversity (0.869), demonstrating broad chemical exploration with minimal redundancy. A multilayered virtual screening cascade combining QSAR modeling, pharmacophore mapping, molecular docking, MM-GBSA binding energy estimation, and 1000 ns molecular dynamics simulations identified three lead candidates MINK-5730, MINK-13510, and MINK-16516 with high predicted affinity, favorable ADMET properties, and robust dynamic stability. Unlike end-to-end optimization pipelines, MolEvoRNN decouples generation from evaluation, promoting methodological transparency and rigorous post-hoc triaging. This study establishes a scalable, generalizable paradigm for rational small-molecule design in underexplored target spaces, bridging generative deep learning with structural pharmacology to accelerate early-phase therapeutic discovery.
17 May 2026
Read appraisal →Journal of molecular modeling
Carbonic anhydrase under the computational lens: a review of advances, challenges, and future directions
CONTEXT: Carbonic anhydrases (CAs) are zinc metalloenzymes responsible for the catalysis of the reversible hydration of carbon dioxide and play pivotal roles in many physiological and pathological processes. The complexity of CA isoforms with distinct expression patterns and catalytic characteristics has aroused fierce interest in the development of isoform-selective inhibitors and activators for therapeutic purposes and diagnostics. In this review, a systematic overview of computational and theoretical methods utilized to determine the structure, mechanism, inhibition, and activation of CAs was given. It reviews the evolution and utilization of computational methods in deciphering catalytic and proton transfer mechanisms as well as active-site interactions into atomic resolution and also features recent developments in drug design and screening strategies engineered for CA isoforms, particularly those involved in cancers and Alzheimer's disease. This study can be a reference for multidisciplinary researchers and inspire the future rational design of CA modulators. METHODS: This review summarizes the literature that employed QM and QM/MM by using like B3LYP, BE0-D3BJ, and MD simulations, GROMACS and Desmond packages, and various force fields: OPLS-AA, AMBER14, and CHARMM36. Molecular docking was performed using such as Glide, AutoDock Vina, and GOLD. Machine learning and AI techniques including SVM, LightGBM, SHAP, and neural networks were also reviewed. This review addresses the limitations in these models as well as prospects with newly emerging instruments such as polarizable force fields and real-time DFT as well as explainable AI frameworks, too. Incorporating data-driven insights with physics-based simulations, the next era of work is primed to provide predictive, scalable platforms for rational design of next-generation CA modulators.
15 May 2026
Read appraisal →Journal of computer-aided molecular design
Dynamic stability-driven machine learning improves binding pose identification on human serum albumin.
Accurate identification of ligand binding modes on human serum albumin (HSA) is important for understanding drug-protein interactions that influence pharmacokinetics, including site-specific competition and protein-binding displacement. However, because HSA is a large, multisite binding protein that permits diverse binding configurations, conventional molecular docking often struggles to distinguish crystal-like poses from nonspecific surface-binding poses using static scoring functions alone. Here, we developed a machine-learning framework for HSA binding-pose identification that incorporates dynamic stability information derived from short molecular dynamics (MD) simulations. Time-dependent interaction patterns observed during MD trajectories, including contact persistence, distance fluctuations, and interaction energy-related descriptors, were quantified as interpretable features and integrated with docking scores. Using Leave-One-PDB-Out cross-validation on 31 HSA-ligand crystal structures, models incorporating MD-derived features significantly outperformed the docking score-only model under both pose-level (root-mean-square deviation [RMSD] ≤ 2.5 Å) and site-level (RMSD ≤ 5.0 Å) criteria (Holm-corrected p < 0.001). External validation using a temporally independent nateglinide-HSA complex further supported the utility of the framework for discriminating plausible binding modes within the HSA setting. Notably, informative dynamic features were obtained from 5 ns MD simulations, indicating that early-stage dynamic behavior is sufficient to improve pose discrimination. These findings show that short MD simulations can provide practically useful and interpretable features for machine-learning-based binding-pose identification on HSA. While broader validation will be required to establish applicability beyond HSA, the proposed framework offers a practical strategy for improving binding-mode analysis in pharmacokinetically relevant HSA studies.
15 May 2026
Read appraisal →Journal of chemical information and modeling
DRHIN: An Integrated and Interactive Web Server for Drug Repositioning
Drug repositioning (DR) identifies new therapeutic uses for approved drugs, reducing development burdens and offering safer treatment options for patients. While high-throughput technologies generate complex, large-scale multiomics data, existing DR tools struggle to comprehensively analyze the resulting biological networks. To address this challenge, we present DRHIN, an integrated, interactive web server for DR over heterogeneous information networks (HINs) using advanced deep learning techniques. DRHIN integrates transcriptomics, proteomics, and microbiome data, incorporating eight biological entities and 19 association types to build diverse HINs and elucidate the underlying molecular mechanisms. It includes 19 state-of-the-art graph representation algorithms, enabling flexible training, comparison, and evaluation of heterogeneous network data. The platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible. Leveraging high-performance computing, DRHIN efficiently processes million-scale networks, ensuring practical applicability in real-world scenarios. The web server is freely accessible at http://drhin.tianshanzw.cn.
13 May 2026
Read appraisal →mAbs
Machine learning predictions of IgG1 and IgG4 self-association and high-concentration solution properties
To meet the widespread demand for subcutaneous delivery of antibody therapeutics, candidates with low viscosity, high solubility, and/or low aggregation propensity in concentrated formulations must be identified. Moreover, early identification of candidates with low self-association increases the likelihood of success at later stages of the development process. Here, we experimentally profile the self-association behavior of a panel of clinical-stage antibodies as a function of pH, excipient content, and antibody isotype. We find that acidic formulations (pH 5) with proline (200 mM) are most effective at suppressing self-association for both IgG1 and IgG4 variants. Moreover, our self-association measurements are correlated with antibody viscosity measurements and inversely correlated with antibody recovery after their concentration using membrane filters. Notably, we developed interpretable machine learning-based classifier and regressor models for predicting IgG1 and IgG4 self-association and demonstrated that they identify antibodies with favorable high-concentration properties. These findings are expected to improve the antibody development process by facilitating the identification of drug-like molecules during their discovery and optimization.
13 May 2026
Read appraisal →PloS one
DeepDRP: Dose-response predictions of drug pairs using deep learning based on data-driven feature representation and dose-response curve characteristics
Combination therapies have become a cornerstone of modern medicine, offering improved treatment outcomes and reduced side effects compared to monotherapies. However, the efficacy and safety of drug combinations depend heavily on the specific doses of each component, making the optimization of dosing regimens a crucial yet challenging task. Despite the importance of dose optimization, few computational methods address this challenge. Here, we propose DeepDRP, a novel approach that integrates two complementary models: the global model, which is trained using all available combinations, and the local model, which utilizes only samples with similar information to queries. The DeepDRP architecture comprises three main modules: the first module has three embedding networks to extract features from drugs, doses, and cell lines, and then predicts synergy using the fused knowledge. The second module constructs a graph using the input query and the set of similar retrieved training samples fed into a semi-supervised graph convolutional network to predict the synergy value. Finally, these two models are aggregated to have the final predicted value. To evaluate the proposed approach, it is applied to the NCI-ALMANAC dataset and O'Neil dataset. The obtained results denote that the proposed method achieves superior performance with respect to the other approaches.
11 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 →Cell
Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes
Identifying drugs that reverse disease-associated transcriptomic features has been widely explored for drug repurposing, but its potential for de novo drug discovery remains underexplored. Here, we present gene expression profile predictor on chemical structures (GPS), a deep-learning-based drug discovery platform, guided by transcriptomic features, that screens large compound libraries and optimizes lead molecules. We first develop a model that captures transcriptomic perturbation signatures solely from chemical structures and deploy it to library compounds. We refine scoring methods and employ a tree-search method for optimization. By incorporating structure-gene-activity relationships, we uncover drug mechanisms from transcriptomic data. We evaluate GPS across multiple diseases and conduct extensive validation in two cases. In hepatocellular carcinoma, we discover two unique compound series with favorable cellular selectivity and in vivo efficacy. In idiopathic pulmonary fibrosis, we identify one repurposing candidate and one novel anti-fibrotic compound by reversing gene expression of multiple distinct cell types derived from single-cell transcriptomics.
5 May 2026
Read appraisal →Annals of biomedical engineering
Stacking Ensemble Machine Learning for Cardiac Safety Assessment Using hiPSC-CM MEA Data
PURPOSE: Prediction of Torsades de Pointes (TdP) risk using hiPSC-CM assays remains challenging, as many models fail to capture nonlinear patterns and exhibit unstable performance across different drugs. We examined whether a stacking ensemble can improve robustness when only two simple Δ F P D c -derived predictors are available. METHODS: Two electrophysiological predictors were derived from MEA-based FPDc measurements: the maximum change ( Δ F P D c ) and the interpolated Δ F P D c at Cmax. These features were used to train a stacking model comprising random forest (RF), XGB, and a shallow artificial neural network (ANN). Performance was assessed on 16 unseen CiPA reference compounds using AUC, likelihood ratios, pairwise accuracy, and classification error. Class weighting addressed class imbalance, and likelihood-based metrics assessed diagnostic consistency. RESULTS: The stacking ensemble consistently outperformed all single classifiers. The XGB-based meta-classifier achieved perfect discrimination for both AUC1 and AUC2 (1.000), with pairwise accuracy reaching 1.000 and classification error remaining below 0.125 across repeated evaluations. CONCLUSIONS: Combining a simple MEA-derived feature set with a stacking architecture provides a more reliable framework for early TdP risk assessment than single-model approaches. External validation using additional MEA datasets and the integration of interpretable modeling strategies will be important for future translational use.
4 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 medicinal chemistry
An Interpretable, Thermodynamics-Based Deep Learning Framework for Predicting and Optimizing Drug Membrane Permeability
Cellular membranes serve as selective barriers, and membrane permeability is crucial for drug pharmacokinetics. While in vitro and in vivo methods exist, predicting and designing membrane permeability remains challenging. We developed a thermodynamics-based deep learning framework to analyze the structure-permeability relationship, based on the concept that interactions between membrane lipids and small molecules influence permeability. We determined the membrane penetration thermodynamics of 8,239 compounds using coarse-grained molecular dynamics simulations and created interpretable graph neural network models to predict and design drug membrane permeability. As a proof-of-concept, we designed a novel nasal-administered melatonin analog, MT-A2, optimized for permeability. Compared with melatonin, MT-A2 showed superior nasal absorption, prolonged brain retention, and enhanced sleep efficacy. Our results provide a promising approach for predicting and designing membrane permeability, aiding in the development of drugs with better pharmacokinetics.
25 Apr 2026
Read appraisal →mAbs
Multi-modal feature learning to prioritize ADCs with favorable half-life in mice
Favorable pharmacokinetic (PK) properties of antibody-drug conjugates (ADCs) enable higher systemic exposure and are associated with improved clinical efficacy, yet ADC PK optimization remains largely empirical and resource-intensive. We developed an in silico framework to predict half-lives of monoclonal antibodies (mAbs) and ADCs in C57BL/6 mice using multi-modal feature learning. To address structural complexity of ADCs, each compound was deconvoluted into three components: (1) antibody features represented using sequence descriptors, AlphaFold-based structural properties, or protein language model (PLM) embeddings; (2) conjugation-related features, including drug-to-antibody ratio and conjugation sites; and (3) physicochemical properties of linker-payloads. XGBoost models were trained using experimental half-life data from 118 mAbs and ADCs. Structure-based and PLM approaches achieved strong cross-validation (Pearson's r = 0.76), outperforming sequence-only models. In temporal validation of 32 mAbs and ADCs, the structure-based models demonstrated robust generalization (r = 0.79). PLM embeddings achieved comparable rank ordering (Spearman's ρ up to 0.81 for ESM-2), but lower quantitative accuracy relative to structure-based models. Overall, our study presents a scalable machine learning framework for early PK screening of mAbs and ADCs prior to synthesis and in vivo testing.
19 Apr 2026
Read appraisal →Cancer cell
HS-20093, a B7-H3-targeted antibody-drug conjugate in lung cancer: Results from the ARTEMIS-001 phase 1a/b trial
This phase 1a/b study (NCT05276609) evaluated the safety, pharmacokinetics, and efficacy of B7-H3-targeted antibody-drug conjugate HS-20093 (GSK5764227) in 306 patients with previously treated advanced solid tumors. In phase 1a, 12.0 mg/kg was established as the maximum tolerated dose. Among 236 lung cancer patients who received 8.0 or 10.0 mg/kg HS-20093, the most frequent grade ≥3 treatment-related adverse events (AEs) included decreased neutrophil (25.5% vs. 50.5%) and white blood cell counts (19.7% vs. 42.4%), and anemia (16.8% vs. 34.3%), respectively. Treatment-related interstitial lung disease and AEs leading to death occurred in 3.4% and 3.8% of patients, respectively. Among response-evaluable patients, the confirmed objective response rate was 52.3% (95% CI: 39.5, 64.9) for extensive-stage SCLC (ES-SCLC, N = 65) and 22.4% (95% CI: 16.0, 29.8) for non-small cell lung cancer (NSCLC, N = 152) patients, with comparable rates between 8.0 mg/kg and 10.0 mg/kg dose cohorts across both groups. These results support further development of HS-20093, with 8.0 mg/kg selected for phase 3 trials.
16 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 →Journal of chemical information and modeling
EviCYP: In Silico Prediction of Cytochrome P450 Substrates Based on Vector Quantization and Evidential Deep Learning
The accurate identification of cytochrome P450 (CYP) substrates is crucial in drug discovery and safety assessment, as these enzymes mediate the metabolism of most clinical drugs. However, existing computational models are often limited by data quality issues and lack the ability to quantify prediction uncertainty, hindering their reliable application. To address these challenges, we present EviCYP, a novel prediction framework that integrates evidential deep learning with vector quantization (VQ). We first constructed a high-quality data set by curating 4388 substrates and 2880 nonsubstrates from 1629 publications, and supplemented it with 3728 pseudonegative samples, resulting in 10,996 samples spanning nine major CYP isoforms. The EviCYP architecture processes multimodal molecular representations and enzyme sequences through dedicated encoders, compresses features via VQ to reduce redundancy, and employs an evidential layer to output both class probabilities and an uncertainty estimate. On an internal test set, EviCYP achieved an average AUROC of 0.9500. Notably, the model's uncertainty quantification is highly reliable, with high-uncertainty predictions strongly correlating with classification errors. This work provides a robust and trustworthy computational tool for CYP substrate prediction.
14 Apr 2026
Read appraisal →Journal of chemical information and modeling
Multiscale-Aware Graph Embedding Approach Uncovers LC-61, a Potent Anti-Leishmania infantum Compound
Visceral leishmaniasis caused by Leishmania infantum remains a lethal disease with few therapeutic options, necessitating innovative computational methods and approaches to accelerate drug discovery. Here, we present a graph neural network (GNN) framework incorporating well-established multiscale mechanisms to improve the identification of novel antileishmanial compounds. Across two classificatory antileishmanial data sets, our GNNs demonstrated significant improvements in predictive performance, with area under the receiver operating characteristic curve (AUC) increases of 2.2-29.2% on the imbalanced data set (activity cutoff: 1 μM) and 3.4-22.5% on the balanced data set (activity cutoff: 10 μM) compared to default GNNs. Subsequently, the framework was applied to screen a library of approximately 1.3 million compounds, pinpointing LC-61 as a potent antileishmanial agent with nanomolar activity against intracellular L. infantum (IC50 = 0.076 μM) and minimal cytotoxicity to macrophages (THP-1 CC50 = 157 μM). A comprehensive in vitro ADME profiling revealed that LC-61 combines high solubility at both acidic and physiological pH (>28 μg/mL), balanced lipophilicity (eLogD = 4.07), and favorable passive permeability (PAMPA = 4.86 × 10-6 cm/s), while exhibiting lower microsomal stability. Overall, our GNN framework effectively accelerated the discovery of LC-61, a novel and biologically validated hit suitable for hit-to-lead optimization.
14 Apr 2026
Read appraisal →International journal of pharmaceutics
From permeability to prediction: evolving strategies for evaluating oral drug absorption
Oral drug administration, preferred for patient compliance and convenience, encounters significant bioavailability challenges arising from complex physicochemical, physiological, and formulation-related barriers. Traditional models for evaluating absorption exhibit limitations in physiological relevance and predictive accuracy. This review critically examines evolving methodologies for oral drug absorption assessment, from classical permeability assays to emerging predictive platforms, emphasizing integration of mechanistic research with predictive modeling to advance intelligent platforms and clinical translation. While classical permeability models including parallel artificial membrane permeability assay (PAMPA), Caco-2, Ussing chambers, and intestinal perfusion remain indispensable for early screening, their limited physiological relevance and inability to elucidate mechanistic pathways constrain predictive accuracy. Mechanistic approaches such as lymphatic transport studies and advanced tracking techniques using fluorescence, Single Photon Emission Computed Tomography (SPECT), or Positron Emission Tomography (PET) imaging reveal critical biological pathways but suffer from scalability challenges. Emerging platforms provide transformative potential, as organoid-on-a-chip offer physiologically relevant and high-throughput alternatives, while integrated computational modeling enables multiscale prediction, from molecular interactions to systemic pharmacokinetics. Future oral absorption assessment requires converging in vitro biomimetics, computational modeling, and Artificial intelligence (AI) within unified platforms. Achieving this requires standardized in vitro models, robust data integration, and alignment with regulatory to accelerate translational drug development.
12 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 →Journal of medicinal chemistry
Rational Design of Broad-Spectrum Non-Nucleoside Reverse Transcriptase Inhibitors via Pharmacophore-Oriented Generative Artificial Intelligence
Guided by the pharmacophore-oriented molecular generation platform PhoreGen, we employed rilpivirine (RPV) as a lead compound to generate 300 structurally diverse analogs that preserve key pharmacophoric features. Subsequent drug-likeness evaluation, molecular docking score, and synthetic feasibility led to the identification of compound No.102 (A19), which displayed potent inhibition activity against WT HIV-1 (EC50 = 3.15 nM) and low cytotoxicity (CC50 > 335 μM). Subsequent structure-activity relationship optimization identified A24, which demonstrated robust activity against clinically relevant drug-resistant HIV-1 mutants (EC50 = 2.07-28.8 nM). Notably, compared to RPV, A24 exhibited significantly reduced cytotoxicity (CC50 = 82.3 μM vs. 3.98 μM) and enhanced potency against the Y188L (EC50 = 28.8 nM vs. 79.4 nM) and F227L + V106A (EC50 = 19.0 nM vs. 81.6 nM) mutants. Pharmacokinetic evaluations revealed that A24 exhibited attenuated CYP enzyme inhibition (IC50 ≥ 1.88 μM), reduced hERG-related toxicity (IC50 = 2.633 μM), and improved metabolic stability (human t1/2 = 39.1 min). Collectively, these favorable properties position A24 as a promising NNRTI for further development.
10 Apr 2026
Read appraisal →Chemical communications (Cambridge, England)
Innovative integration of molecular docking and machine learning for drug discovery: from virtual screening to nanomolar inhibitors
This review highlights our group's systematic approach to integrating molecular docking, pharmacophore modeling, and machine learning methodologies for the rational discovery of bioactive leads. We describe innovative strategies including docking-based data augmentation, ligand-receptor contact fingerprints, genetic algorithm-guided feature selection, and SHAP-based model interpretation. These approaches have enabled the discovery of nanomolar inhibitors against multiple therapeutic targets including STAT3, TTK, LSD-1, and HER2. The presented workflow demonstrates how machine learning (ML) can be synergistically combined with traditional computer-aided drug design methods to achieve efficient scaffold hopping and identify novel chemotypes with potent biological activities.
10 Apr 2026
Read appraisal →mAbs
Deep learning assessment of nativeness and pairing likelihood for antibody and nanobody design with AbNatiV2.
Most immune-system created antibodies balance good binding and stability with low toxicity and self-reactivity. Quantifying the nativeness of a candidate sequence - its likelihood of belonging to natural immune repertoires - has thus emerged as a valuable strategy for hit selection from synthetic libraries, optimization and humanization, and for guiding de novo design toward developable candidates. We previously introduced AbNatiV, a transformer-based VQ-VAE for nativeness assessment, which proved effective across multiple nanobody engineering tasks. However, AbNatiV1 operated on unpaired sequences, limiting applicability to conventional VH-VL antibodies. Moreover, its performance on nanobody nativeness was constrained by the limited number and diversity of nanobody repertoires available at the time. Here, we sequenced new camelid repertoires, curated additional recent datasets, and present AbNatiV2: an enhanced architecture comprising various models each trained on ≥20 million sequences. AbNatiV2 improves nanobody nativeness classification across held-out and diverse test sets, and more robustly detects nativeness changes upon CDR grafting. We also introduce p-AbNatiV2, a cross-attention model fine-tuned on 3.7 million paired human sequences. p-AbNatiV2 provides residue- and sequence-level humanness for VH/VL pairs and learns pairing-likelihood via noise-contrastive training. On held-out tests, it assigns the native pair a higher score in 74% of cases, substantially outperforming recent pairing models. Together, AbNatiV2 and p-AbNatiV2 extend nativeness assessment and engineering to both nanobodies and conventional antibodies, supporting design decisions at single-residue, Fv-sequence, and paired-domain levels. AbNatiV2 is available as downloadable software and webserver.
8 Apr 2026
Read appraisal →Pharmacology research & perspectives
Improving Population Pharmacokinetic Modelling with Artificial Patients using Generative Artificial Intelligence
In population pharmacokinetics (PopPK), non-linear mixed effects (NLME) models are used to simultaneously describe a drug's pharmacokinetics (PK) and dynamics (PD) in a patient population using systems of ordinary differential equations. In this field, machine learning is mainly used for data preparation, hypothesis generation, predictive modeling, and model validation. Some approaches to integrate artificially generated information have already been explored, but real-world application is still limited. We therefore conducted a proof-of-concept study to analyze the ability of generative artificial intelligence (AI) to create artificial patient profiles to augment PopPK data sets and assess their influence on parameter estimates. We defined the pharmacokinetic parameters of a hypothetical drug and simulated the concentration curves of 20 patients. We then trained Wasserstein Generative Adversarial Networks (WGANs) with gradient penalty (GP) to generate artificial patients. The data distribution of original and artificial patients was statistically indistinguishable as shown by the Maximum Mean Discrepancy test. Therefore, the WGAN-GP is neither overfitted, that is producing only single instances of artificial patients, nor underfitted, that is producing unrealistic artificial patients. We then combined different shares of original and artificial patients in separate data sets to build and compare PopPK model estimates. Addition of artificial patients led to narrower confidence intervals, indicating more robust parameter estimates, and accentuated the allometric effect of weight on the volume of distribution. In conclusion, we provide a proof-of-concept that generative AI can be used to augment pharmacokinetic data sets, with preliminary evidence suggesting improved parameter estimation.
4 Apr 2026
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