Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 40 appraisals
Journal of pharmacokinetics and pharmacodynamics
Diffusion models for virtual populations and pharmacometric simulations
To evaluate diffusion model-based artificial intelligence approaches for generating virtual populations with physiological determinants of drug dosing (PDODD) and pharmacokinetic (PK) profiles. A denoising diffusion probabilistic model (DDPM) was applied to a 31-variable dataset of PDODD covariates (18 continuous, 13 binary) from the National Health and Nutrition Examination Survey and compared to a tabular variational autoencoder (TVAE). For nivolumab PK data (12,000 patients, 13 time points, 5 covariates), sequence-based diffusion model (SDM) and a time-aware diffusion model (TDM) with temporal self-attention were evaluated. The predictive performance of the TDM was evaluated by imputing masked time points. All models were trained and tested on 80%:20% partitions of the data using univariate, bivariate, and multivariate distributional similarity metrics. The diffusion model satisfactorily approximated the univariate distributions of continuous PDODD biomarkers (mean Kolmogorov-Smirnov D-statistic, KSD = 0.014), disease status frequencies (mean absolute error, MAE = 0.31%), and preserved bivariate correlations (MAE = 0.033). DDPM outperformed TVAE for categorical variables (0.31% vs. 1.07% MAE) and correlation (0.033 vs. 0.091 MAE). For nivolumab PK, SDM has KSD of 0.047 and a relative error of 1.36%. TDM accurately imputed missing PK timepoints (KSD = 0.014), reconstructing masked Day 1, Peak concentration (Cmax) Dose-9, and Terminal phase concentrations with MAE of 0.43%, 0.25%, and 0.76%, and correlations ≥ 0.999. Diffusion models demonstrated strong performance in generating cross-sectional PK covariate data and longitudinal PK profiles, capturing complex distributional and temporal dependencies. Diffusion-based approaches provide a flexible and robust framework for virtual simulations in pharmacometrics.
1 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 computer-aided molecular design
Artificial intelligence in impurity prediction: current landscape, challenges, and future directions
Earlier identification, regulation of impurities in pharmaceutical products is critical throughout the medication development process because they have a high impact on drug quality, safety, and regulatory approval. Traditional analytical methods like LC-MS and NMR are frequently used, but they are labour intensive, time consuming and have limited capacity to detect unknown or trace level impurities. New opportunities to forecast impurity formation before experimental observation have been enabled by developments in cheminformatics and computer modelling. This study covers advanced chemical representation techniques, data generation, data curation, and model validation with highlighting recent advances in machine learning and deep learning algorithms for impurity prediction. In association with forecasting synthetic by products, degradation products, and harmful contaminants, traditional QSAR techniques, and contemporary deep learning models such as graph neural networks, transformer based architectures, and generative frameworks are examined. Additionally, discussed the increasing significance of explainable models for regulatory body acceptability. Lastly, newly developed advanced techniques like digital twins, automated impurity profiling, integrated reaction degradation modelling, and real-time monitoring are examined, demonstrating how computational methods are transforming impurity assessment from a reactive effort into a predictive and preventive procedure.
23 July 2026
Read appraisal →Journal of chemical information and modeling
PegaPlus─Interactive Machine Learning by Human Observation for Efficient Clustering and Analysis of Structure-Activity Data
In the early stages of drug discovery, the identification of molecular series with lead-like properties is essential for structure-activity relationship studies. A common first step is to cluster data derived from high-throughput screening (HTS). This data then needs to be exhaustively refined by an expert to identify bioactive compound classes. Here, we present the novel, interactive, and visual approach named PegaPlus for the analysis of HTS data, facilitating the incorporation of expert knowledge with the strategy of learning by observation. PegaPlus uses a tailored variant of a stochastic proximity embedding algorithm to visualize the clustered data in a two-dimensional plane via a web interface. The user can iteratively modify the proposed compound clustering by dragging data points in the drawing plane. At each step, an online support vector machine learns and improves its clustering predictions by incorporating user modifications and the molecular features of the compounds. The visualization is also updated based on the user's input and molecular similarity. PegaPlus was developed with the exploratory process in mind and allows the user to freely organize molecules on the drawing plane, or filter them by various molecular properties. Our evaluation shows that PegaPlus reduces the number of refinement steps needed by half in comparison to the purely manual approach on multiple data sets. These results demonstrate how an interactive machine learning approach can reliably support medicinal chemists and automate time-consuming, manual refinement tasks. The PegaPlus web server is openly available at pegaplus.zbh.uni-hamburg.de.
14 July 2026
Read appraisal →ACS biomaterials science & engineering
AI-Driven Design and Clinical Translation of Nucleotide-Peptide and Peptide-Drug Conjugates
While nucleotide-peptide conjugates and peptide-drug conjugates demonstrate a considerable evolution as next-generation therapeutics, the successful translation of these complex agents relies on advances in bioconjugation strategies. By anchoring nucleotides or pharmaceutical payloads to multifaceted peptide scaffolds, researchers have engineered molecular platforms with dual mechanisms of action. However, to fully realize the potential of these hybrid systems, further refinements are required to ensure both in vivo stability and retention of the desired biological activities over time. This comprehensive review examines ongoing progress toward optimizing conjugation chemistry for the development of stable and efficacious NPC and PDC candidates. Through evaluation of fundamental design principles and their clinical relevance, it aims to accelerate the journey of these multilayered biomolecular constructs from theory to practice. It details various strategies to mitigate challenges such as enzymatic degradation and rapid systemic clearance, including covalent linkages (e.g., aminoacylation and thiol oxidation) and noncovalent interactions (e.g., ligand-receptor binding). Central to this emerging field is the integration of artificial intelligence and deep learning, which are rapidly transforming the design and development of NPCs and PDCs. AI-driven tools enable predictive modeling of peptide-drug interactions, intelligent linker optimization, and in silico screening of bioactive scaffolds-thus redefining the pipeline for precision therapeutics with improved selectivity and translatability. The potential of hybrid nanostructures formed by peptide-DNA conjugates for advanced drug delivery and diagnostic applications is also discussed. Furthermore, the clinical translation of PDCs demonstrates their promise in treating conditions such as cancer, neurodegenerative diseases, and inflammatory disorders, alongside the ongoing challenges regarding peptide availability, linker optimization, and scalable synthesis. The review concludes by discussing future directions of NPCs and PDCs, highlighting the role of interdisciplinary approaches in advancing precision medicine and addressing complex therapeutic needs.
14 July 2026
Read appraisal →Nucleic acids research
DeepCYP: an integrated deep learning web server for the holistic 'pathway-site product' prediction of CYP450 metabolism
CYP450 (cytochrome P450)-mediated drug metabolism is a critical determinant of pharmacokinetics and clinical safety, making comprehensive metabolic profiling essential for rational drug discovery. Here, we present DeepCYP (https://deepcyp.scbdd.com), a freely accessible deep-learning web server for end-to-end CYP450 metabolic profiling. Trained on an expanded dataset and a mechanism-based reaction rule library, DeepCYP uses a multi-task graph neural network (GNN) combined with multi-scale descriptors. Operating directly on 2D molecular graphs, this architecture bridges the entire "pathway-site-product" continuum across nine major CYP isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4) within a unified pipeline. Benchmarking demonstrates that DeepCYP outperforms established tools, including FAME3, SMARTCyp, and BioTransformer 3.0, improving Top-1 and Top-2 ranking metrics by over 10%. Furthermore, the server supports high-throughput batch processing, capable of evaluating ~280 molecules per minute. DeepCYP also enhances interpretability through an interactive visualization interface, featuring susceptibility radar charts and dynamic transformation tables. By translating abstract predictions into biological insights, DeepCYP provides a practical tool to accelerate lead optimization and mitigate toxicity risks.
13 July 2026
Read appraisal →Molecular pharmaceutics
Computational Identification of Active Drug Metabolites for Human Protein Targets
Understanding drug metabolism helps mitigate toxicity risks and anticipate pharmacological effects beyond the parent compound. Here, we explore high-confidence in vitro human bioactivity annotations from the public domain associated with a curated data set of drug-metabolite pairs. Our findings show that 26% of all drug-metabolite-target combinations contain metabolites with retained or increased bioactivity relative to their parent drugs, showing a high proportion of active metabolites with potencies below 100 nM. Moreover, these active metabolites display similar predicted physicochemical and ADME properties to their parent drugs, indicating that their biological activity may be preserved in vivo. Two key features of the identified active metabolites are noted: (1) the metabolite is more likely to be formed via an O-demethylation or an N-demethylation at a tertiary nitrogen, and (2) these drug-metabolite pairs are frequently active against the membrane receptor target class. To support further research in this area, we make our data compilation, including pairs with identified active metabolites and recorded bioactivity data, openly available.
7 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 →Advanced drug delivery reviews
Artificial intelligence and machine learning in smart vaginal formulation development
Vaginal drug delivery in women's health remains underutilized and insufficiently studied, largely due to the complexity and dynamic nature of the vaginal microenvironment. Variations in vaginal pH, hormonal levels, and microbiota composition introduce significant biological variability, complicating formulation design and contributing to inconsistent therapeutic outcomes and poor patient adherence. Conventional vaginal formulations often fail to account for these individual differences, highlighting the need for more adaptive and predictive approaches. Emerging advances in artificial intelligence (AI) and machine learning (ML) offer promising strategies to address these challenges by enabling multi-parameter, data-driven formulation development that explicitly considers biological variability. Despite their transformative potential, the application of AI and ML in vaginal drug delivery remains limited. This review addresses this gap by examining the emerging role of AI/ML in the design, optimization, and development of next-generation vaginal formulations, including mucoadhesive, mucus-penetrating, bio-responsive, and controlled-release systems. Key factors influencing formulation performance, such as vaginal microbiota composition, are discussed, and ethical, regulatory, and technical challenges relevant to integrating AI/ML into vaginal drug delivery are also considered. Although research is in its early stages, existing studies indicate that AI and ML integration can enhance predictability, efficiency, and reproducibility in vaginal formulation development. Finally, we highlight the potential of developing personalized medicines through the integration of AI/ML and digital health monitoring devices to create patient-centered vaginal drug delivery systems, thereby advancing therapeutic outcomes and transforming women's health.
1 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 →Journal of chemical information and modeling
BioTD: An Online Database of Biotoxins
Biotoxins, mainly produced by venomous animals, plants, and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia. A number of venom-derived drugs are already available on the market, with many more candidates currently undergoing clinical and laboratory studies. However, drug design resources related to biotoxins are insufficient, particularly because of a lack of accurate and extensive activity data. To fulfill this demand, we developed the Biotoxins Database (BioTD). BioTD is the largest open-source database for toxins, offering open access to 14,607 data records (8,185 activity records), covering 8,975 toxins sourced from 5,220 references and patents across over 900 species. The activity data in BioTD are categorized into five groups: Activity, Safety, Kinetics, Hemolysis, and other physiological indicators. Moreover, BioTD provides data on 1,532 mutants, refines the whole sequence and signal peptide sequences of toxins, and annotates disulfide-bond information. All of the data in the database can be downloaded for free. Given the importance of biotoxins and their associated data, this new database is expected to attract broad interest from diverse research fields in drug discovery. BioTD is freely accessible at http://biotoxin.net/.
23 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 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 controlled release : official journal of the Controlled Release Society
Toward animal-free approaches in subcutaneous drug delivery: A pain management case study.
Subcutaneous (s.c.) administration offers practical advantages for long-acting drug delivery, yet its complex tissue environment, lack of standardized models, and limited regulatory guidance pose challenges for predicting pharmacokinetics (PK). Here, we present a complementary set of experimental and computational approaches to reduce reliance on animal testing while maintaining translational relevance. Ex vivo skin models captured the influence of tissue variability, while 3D-printed constructs and molded agarose hydrogels enabled standardized, reproducible in vitro drug release studies. In parallel, machine learning models trained on curated rodent datasets predicted key PK parameters with strong agreement to in vivo data, providing an alternative that bypasses additional animal experiments and resource-intensive assays. Finally, a liposomal carprofen formulation case study demonstrated the translational potential of these methods in veterinary applications. Together, these strategies illustrate routes toward animal-free drug development: simplified experimental systems that mimic s.c. release and computational models that predict systemic PK. By combining them conceptually, we outline a framework that advances the 3Rs principles while supporting mechanistic and predictive understanding of s.c. drug delivery.
10 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 →Journal of computer-aided molecular design
Target-aware molecule SMILES generation using a large language model with retrieval-augmented generation, multi-turn memory, and a predictive model
Given the vastness of chemical space and the cost and time requirements of high-throughput screening, resource-efficient computational strategies are needed to prioritize candidate drug molecules. Here, we evaluated whether an open-weight large language model (LLM) augmented with retrieval-augmented generation (RAG), multi-turn memory, and a pretrained drug-target interaction predictor can generate target-aware molecules without task-specific retraining. Protein-ligand-pKi examples retrieved from BindingDB, Davis, and KIBA were used as contextual guidance, while DeepPurpose provided the optimization signal during iterative SMILES refinement. Our approach produced a statistically significant increase in predicted pKi across successive multi-turn memory iterations, indicating that retrieval- and memory-guided refinement may improve target-conditioned molecular generation. The proposed framework also showed favorable molecular generation characteristics, with novelty reaching 100%, diversity up to 0.882, and uniqueness up to 1.0, suggesting that retrieval did not reduce the process to simple reproduction of known ligands but instead supported exploration of new regions of chemical space. In addition, the approach yielded supportive docking results consistent with the generation of chemically relevant candidate ligands. However, the increase in predicted affinity was accompanied by trade-offs in molecular quality, including reduced validity and drug-likeness in later iterations. Taken together, these findings suggest that this framework provides a flexible and comparatively resource-efficient strategy for target-aware de novo molecular design, while further multi-objective optimization and independent validation remain necessary.
31 May 2026
Read appraisal →ChemMedChem
A 24-Day MicroCycle Journey to Interleukin-17 Inhibitors: From Library Design to Central Core Prioritization and Capping Group Identification
We herein report an application of the MicroCycle platform, which leverages advanced machine learning models, automation, and miniaturization of processes to accelerate the exploration of chemical space through libraries. This approach was applied to a novel thiazole-based series targeting interleukin-17, enabling rapid prioritization of central cores and identification of compelling new capping groups, thereby guiding further optimization efforts.
27 May 2026
Read appraisal →Journal of chemical theory and computation
MolSculptor: An Adaptive Diffusion-Evolution Framework Enabling Generative Drug Design for Multitarget Affinity and Selectivity
The rational design of molecules with tailored activity/selectivity across multiple protein targets is crucial for developing therapies for complex diseases like cancer, yet it remains a formidable challenge. While deep generative models show immense promise, their application to these tasks faces fundamental challenges, as they struggle to incorporate the structural information on multiple distinct protein pockets and require vast multitarget data sets or specialized expert knowledge that are rarely available. Here we introduce MolSculptor, an adaptive diffusion-evolution framework designed to generate inhibitors for any combination of on- and off-targets, circumventing the need for target-specific training data or prior expert knowledge. MolSculptor unifies both de novo design and lead optimization, provides a versatile workflow applicable to different stages of drug discovery, and allows for direct conditioning on key drug-like properties. It integrates a 3D-aware surrogate model to enable flexible guidance for any set of specified on- and off-targets. Furthermore, MolSculptor employs an active learning protocol to adaptively refine this guidance, ensuring high performance even in data-scarce scenarios. We demonstrate MolSculptor on a series of challenging multitarget and selective inhibitor design tasks, where it significantly outperforms state-of-the-art methods in generating high-quality candidates that satisfy all complex constraints. Notably, many of the generated molecules exhibit predicted affinity profiles superior to those of experimentally validated references. Using MolSculptor, we successfully designed and synthesized a novel and potent dual-target inhibitor for castration-resistant prostate cancer (CRPC), whose inhibitory activity was confirmed through wet-lab validation. MolSculptor provides a powerful and generalizable paradigm for designing ligands with complex, multitarget activity profiles, paving the way for data-efficient solutions to complex therapeutic problems.
27 May 2026
Read appraisal →Journal of chemical theory and computation
MolXProt: A Cross-Attention Transformer-Based Graph Neural Network for Protein-Ligand Binding Affinity Prediction
Accurate and fast prediction of drug-target binding affinities (DTAs) is key for drug discovery; however, many methods, such as docking and empirical scoring, fail when generalizing to unseen cases. In this study, we introduced the MolXProt architecture, a novel transformer-based graph neural network that integrates graph ligand representations with protein language models using bidirectional multihead cross-attention. Our model is shown to be scalable to over 100000 protein-ligand pairs of mixed data sets, achieving 50% of predictions within ±1.0 kcal/mol and 80% within ±2.0 kcal/mol. We show that the architecture can explicitly learn residue-atom interactions while being computationally friendly via protein-token compression. By mapping token-residue interactions, we demonstrated that the model learns key binding pocket residues in benchmark complexes, such as CDK2-Staurosporine and DHFR-Methotrexate, but under-represents the hydrogen-bonding networks. Our calibration bias analysis revealed that the model overpredicted strong binders and underpredicted weak binders, which are linked to data imbalances and heteroscedastic noise. A simple posthoc isotonic correction partially mitigated the bias. Latent space analysis showed that the model learned continuous binding affinity manifolds without split leakage. Our work highlights a novel architecture that offers unique insights into binding mechanisms via transformer-based cross-attention and is computationally inexpensive.
27 May 2026
Read appraisal →Journal of computer-aided molecular design
In silico discovery of novel small-molecule PD-L1 inhibitors through a multi-stage computational workflow integrating machine learning and molecular dynamics
Cancer immunotherapy targeting the PD-1/PD-L1 pathway has transformed modern oncology; however, developing small-molecule inhibitors as viable alternatives to monoclonal antibodies remains a major challenge. In this study, an integrated computational framework is established in which machine learning, molecular docking, molecular dynamics simulations, and binding free-energy calculations are combined to enable the discovery and optimization of novel PD-L1 inhibitors. A validated ML-QSAR model was constructed using the XGBoost algorithm (R2_train = 0.925, R2_test = 0.743) on a dataset of 74 known inhibitors with consistent assay conditions. Through virtual screening of FDA-approved drugs, Pralatrexate was subsequently identified as a promising repurposing candidate, demonstrating a higher predicted binding affinity than the reference inhibitors. Structure-based modification of Pralatrexate yielded the derivative D1, which exhibited improved computational binding properties across all evaluation methods. Molecular dynamics simulations indicated that the D1-PD-L1 complex achieved greater stability than both the free protein and the reference complex, with reduced RMSD fluctuations and preserved key interactions with tyrosine residues Tyr56(A/B). MM-GBSA calculations further confirmed D1's superior binding affinity (-86.21 kcal/mol vs. - 73.65 kcal/mol for the reference), and predicted IC50 values suggested enhanced inhibitory potential. This multi-stage computational workflow effectively integrates machine learning predictions with atomic-level binding analyses, providing a robust platform for accelerated drug discovery. The optimized derivative D1 thus represents a promising candidate for experimental validation and further development as a potential cancer immunotherapeutic agent.
26 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 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
MechMemDyn: Coupling Frustration Analysis with Membrane Dynamics to Target the TREM2-DAP12 Complex Interface
The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes a pivotal therapeutic target for Alzheimer's disease. However, the intrinsic plasticity of its interface poses a formidable challenge for structure-based discovery of protein-protein interaction stabilizers (PPI stabilizers). Conventional approaches, ranging from static molecular docking to geometric deep learning, fail to capture the subtle interfacial energetics required for stabilizing this assembly, often leading to erroneous activity predictions. Here, we present MechMemDyn, a novel predictive framework that uniquely integrates protein frustration analysis with membrane-embedded molecular dynamics (MD) simulations. To our knowledge, this is the first systematic application of frustration analysis to rationalize PPI-stabilizing activity and guide ligand design. By mapping the local frustration landscape, we identify critical, minimally frustrated contact networks at the protein interface, which are essential for stabilizing the PPI. We demonstrate that the ability of a ligand to dampen distance fluctuations within these key networks, a metric rooted in thermodynamic rigor, correlates strongly with experimental potency. This method outperforms conventional static docking, AI-driven dynamic docking approaches, and standard molecular simulations, providing a more accurate and reproducible basis for cross-ligand comparison. This work not only resolves the intractability of the TREM2-DAP12 complex but also establishes a physics-driven paradigm for targeting dynamic transmembrane interfaces via frustration-optimized PPI stabilizers.
13 May 2026
Read appraisal →Journal of chemical information and modeling
A Transformer for Reaction-Aware Compound Explorations with GFlowNet in QSAR-Guided Molecular Design
In drug discovery tasks, achieving a balance between high biological activity toward therapeutic targets and synthetic chemical feasibility is critically important. While the recently proposed deep learning-based molecular generation models have enabled explorations of vast chemical spaces, most existing approaches do not consider synthetic routes for generated compounds. To address this issue, TRACE-GFN is proposed for molecular optimization; this method incorporates chemical reaction pathways into a quantitative structure-activity relationship (QSAR)-guided molecular design procedure. The method integrates a transformer model to explicitly learn chemical reactions with a generative flow network (GFlowNet) that efficiently samples diverse candidates. In benchmark experiments involving dopamine receptor D2 (DRD2), AKT serine/threonine kinase 1 (AKT1), and C-X-C motif chemokine receptor 4 (CXCR4), TRACE-GFN demonstrated the ability to identify compounds with high QSAR values while maintaining strong diversity, outperforming the existing molecular generation models. These results demonstrate that the proposed model can efficiently explore promising compounds while accounting for real-world chemical reactions. The source code is publicly available under an MIT license at https://github.com/sekijima-lab/TRACE-GFN.
13 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 →Molecular pharmaceutics
mRNASyner: An Integrative Framework for Full-Length mRNA Sequence Optimization via Multimodule Synergistic Design
Messenger RNA (mRNA) technology holds great promise in biomedicine; however, its therapeutic efficacy relies heavily on translation efficiency and the in vivo stability of mRNA sequences, both of which are governed by dynamic interactions between the coding and noncoding regions. Existing optimization approaches typically focus on individual sequence components and lack the capacity for global, synergistic optimization. Moreover, their performance is often limited when processing long sequences or supporting iterative sequence refinement. To overcome these challenges, we propose a unified and interpretable in silico framework for full-length mRNA sequence design, integrating CDS optimization, UTR generation, and degradation modeling. In silico case studies based on respiratory syncytial virus (RSV) vaccine design demonstrated that mRNASyner achieved a favorable balance between translational accessibility and structural stability. mRNASyner thus enables the design of full-length mRNA sequences, supports long-sequence optimization, and offers a novel solution for the development of personalized mRNA therapeutics.
11 May 2026
Read appraisal →Molecular informatics
ADME-DTI: Augmented Deep Meta Ensemble for Drug-Target Interaction Prediction
Drug-target interaction represents a critical focus area in computational drug discovery and pharmaceutical research. However, the process of identifying and analyzing these interactions is often resource-intensive, requiring extensive experimentation to evaluate the binding relationships between numerous drugs and their respective targets. This complexity is further compounded by the fact that a drug can inhibit multiple targets, and a target may also bind to various drugs. To address these issues, advanced deep learning models have been introduced as promising tools, offering the ability to accurately predict binding affinity and other bioactivity values to distinguish between potential drug-target interactions. The proposed model, named the Augmented Deep Meta Ensemble for Drug-Target Interaction (ADME-DTI), leverages multiple descriptors and fingerprint representations to extract meaningful insights from drug and protein data. These submodels from each representation are then combined with a deep learning architecture along with the metadata of the drug and target entries. The proposed approach has proved competitive performance against state-of-the-art models across diverse datasets and evaluation metrics, as evidenced by key metrics, namely r m 2 $r_m^2$ , concordance index, and mean squared error. Specifically, the model achieved mean squared error values of 0.186 (Davis), 0.118 (Kiba), 0.134 (DTC), 0.262 (Metz), 0.300 (ToxCast), and 0.791 (STITCH), all of which are publicly available benchmark datasets commonly used in drug-target interaction prediction tasks. The results highlight the model's effectiveness in reducing prediction errors and improving accuracy. Previous drug-target interaction research often relied on limited, non-diverse datasets, reducing generalizability. Few studies addressed drug-target interaction prediction as a regression task. Our novel deep learning metamodel integrates multiple models and representations, surpassing benchmarks and delivering greater prediction accuracy across varied datasets and evaluation metrics.
4 May 2026
Read appraisal →Journal of chemical information and modeling
A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations
Achieving optimal target activity while maintaining synthetic accessibility and drug-likeness represents a major challenge in computational drug discovery. Existing de novo generative models often yield chemically invalid or synthetically intractable structures and struggle to optimize multiple objectives simultaneously. Here, we introduce ALCHIMIA, an interpretable hybrid framework combining reinforcement learning (RL) and a genetic algorithm (GA), built based on a vocabulary of 33 medicinal chemistry-inspired molecular transformations. The RL component trains a policy network to prioritize transformation sequences that improve synthetic accessibility (SA) and the quantitative estimate of drug-likeness (QED) scores, embedding these constraints directly into molecular generation. The GA component applies the learned policy as a mutational operator within population-based optimization guided by molecular docking, enabling the exploration of diverse chemical lineages while converging toward high-affinity ligands. ALCHIMIA was applied to two different pharmacologically relevant targets: human Cannabinoid Receptor 2 (CB2R) and human Sigma nonopioid intracellular Receptor 1 (S1R). We considered three different scenarios: (i) unconstrained hit identification; (ii) scaffold-constrained lead optimization; and (iii) design of dual modulators. The framework generated chemically valid molecules with QED and SA scores comparable to or better than those obtained with random baselines and selected de novo design methods. By codifying typical medicinal chemistry actions as learnable transformations and coupling multiobjective optimization with GA-based diversity maintenance, ALCHIMIA, freely available as a GitHub repository (https://github.com/alberdom88/ALCHIMIA), provides a practical, interpretable, and scalable framework for molecular de novo design.
2 May 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 →Journal of chemical information and modeling
AI-MedCraft: A Strategy-Driven AI Platform for Multi-Objective Molecular Design
Small-molecule drug discovery requires balancing potency, selectivity, solubility, safety, and synthetic feasibility, among other interdependent properties. Yet most computational workflows address these properties sequentially or collapse them into fixed weighted scores. Although artificial intelligence has accelerated molecular design, many platforms struggle to handle explicit multiobjective trade-offs. Here, we present AI-MedCraft, a strategy-driven molecular design framework that applies adaptive, Pareto-guided reinforcement learning to optimize multiple objectives concurrently within a unified workflow. When structural information is available, physics-aware scoring is incorporated to support binding-competent designs. In a structure-based benchmark on the solubility rescue of the BTK inhibitor GDC-0834, AI-MedCraft achieves broader Pareto-front coverage than the scalar-reward reinforcement learning framework REINVENT 4 under matched objectives and computational cost. In a second case study, AI-MedCraft redesigns Efavirenz to retain HIV-1 reverse transcriptase engagement while reducing predicted 5-HT2A off-target binding, demonstrating its use in multiconstraint molecular optimization.
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 →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 →PDA journal of pharmaceutical science and technology
The European Qualified Person: Legal Accountability, Quality Leadership, and AI Integration in Modern Pharmaceutical Manufacturing: Navigating Complex Therapies and AI-Supported Oversight to Safeguard Patient Safety
The European Qualified Person (QP) holds unique legal responsibility for certifying batch release, ensuring patient safety, and maintaining regulatory compliance. Modern pharmaceutical manufacturing-especially in biologics, advanced therapy medicinal products (ATMPs), and personalized medicines-generates complex, multi-site, data-rich environments that challenge traditional oversight. Artificial Intelligence (AI) offers predictive analytics, anomaly detection, and trend recognition to support decision-making but cannot currently replace the QP's scientific judgment or legal accountability. The forthcoming Annex 22 introduces Human-in-the-Loop (HITL) frameworks that embed human oversight within AI-supported processes, aligning technological advancement with regulatory control. This review explores evolving QP responsibilities, core and emerging competencies, AI integration within Quality Risk Management (QRM) and Good Manufacturing Practice (GMP) frameworks, and ethical considerations, illustrated with practical case studies. By developing AI literacy and applying HITL oversight, QPs can more effectively translate technological potential into transparent, science-based, and patient-centered batch-release decisions.
12 Apr 2026
Read appraisal →PDA journal of pharmaceutical science and technology
Integrating Real-Time Viable Biofluorescent Particle Counters within Robotic Gloveless Isolators
Advanced technologies in both aseptic filling and environmental monitoring are coming together to improve the resilience and sterility assurance of aseptic processing. Continuous, real-time environmental monitoring using biofluorescent particle counters (BFPCs) to detect viable and nonviable particles during the aseptic filling process for injectable drugs is gaining traction as an accepted alternative to conventional methods such as active air sampling instruments. Evolving regulatory guidance, including EU Annex 1 guidelines, are increasingly recommending the adoption of isolator technology as well as continuous environmental monitoring during GMP processes, including technologies that offer real-time feedback during drug product manufacturing. Computational flow dynamics and airflow visualization studies are additional tools that support the design of equipment and determination of locations for environmental monitoring. The current nutrient culture media growth-based environmental monitoring, rooted in science from 150 years ago, is unable to keep pace with recent technological advances and the ability to immediately react to an out-of-control state. Here we examine the design of an isolator that eliminates human intervention and indirect product contact parts during aseptic fill finish operations and present computational fluid dynamics (CFD) studies verified by airflow visualization, along with the incorporation of BFPCs at critical areas. Also, we report the results of the interference study characterizing the baseline results for detection of total particles (nonviable plus viable) using a BFPC within a robotic gloveless isolator during dynamic and static operating conditions. The results of our study using BFPCs demonstrate that airflow in the robotic gloveless isolator provides protection of critical areas from contamination during the tub peeling process, and that the stoppering process in this environment does not generate detectable particles. During dynamic conditions and material transfer, the study demonstrates the design of the robotic gloveless isolator prevents false positives from interfering with materials during normal operations.
12 Apr 2026
Read appraisal →PDA journal of pharmaceutical science and technology
Bridging Guidance and Regulation: Interpreting the Draft Annex 22 on Artificial Intelligence in GMP Manufacturing
This paper builds upon Stassen et al. (2025), "Recommendations for Artificial Intelligence Application in Continued Process Verification," to provide a detailed interpretation of the European Medicines Agency (EMA) Draft Annex 22-Artificial Intelligence (July 2025 draft) within the context of GMP-regulated pharmaceutical manufacturing. The draft annex introduces a structured, risk-based framework for artificial intelligence (AI) adoption, defining expectations for intended use, validation, life cycle management, explainability, and human-in-the-loop (HITL) oversight. This manuscript examines the alignment between Annex 22 provisions and continued process verification (CPV) recommendations, identifies convergence, operational challenges, and areas for future development, and proposes a roadmap for compliant AI implementation. Conceptual frameworks and workflow illustrations depict the harmonization of CPV principles, life cycle management, and emerging regulatory expectations. This review supports harmonized AI governance and readiness for Annex 22 compliance across the pharmaceutical sector.
12 Apr 2026
Read appraisal →International journal of pharmaceutics
Beyond spectroscopy: Machine vision as the future of non-destructive testing in 3D-printed pharmaceuticals
3D Printing (3DP) has revolutionized personalized medicine by enabling the production of patient-specific dosage forms and adaptable drug delivery systems. However, despite these advances, 3DP presents significant challenges in quality control (QC), particularly as traditional methods may not be suitable for small-scale or on-demand manufacturing. To address these challenges, it is essential to develop flexible, precise, and efficient QC strategies that ensure product integrity. This review focuses on non-destructive spectroscopic- and imaging-based testing techniques that can bridge this gap. Techniques such as Near-Infrared Spectroscopy, Raman Spectroscopy, Terahertz Spectroscopy, Optical Coherence Tomography, and Hyperspectral Imaging provide robust alternatives to conventional QC approaches. Moreover, Machine Vision (MV) is emerging as a promising tool for real-time defect detection and visual inspection. When integrated with advanced algorithms and artificial intelligence, MV enables automated decision-making and defect identification in 3DP pharmaceuticals. Recent advancements in deep learning, especially through Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based models, have further enhanced the potential of MV in this domain, offering improved accuracy, speed, and adaptability under varying conditions. This review also includes a comparative evaluation of these techniques, highlighting the increasing importance of non-destructive tools, particularly MV, in supporting innovation and ensuring quality within an increasingly personalized pharmaceutical landscape. Furthermore, it also discusses existing gaps, regulatory challenges, and the need for standardization to ensure the safe and reliable integration of these technologies into pharmaceutical manufacturing workflows.
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
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