Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Archives of toxicology
A review of machine learning in toxicology: current practices and reporting gaps
In recent years, machine learning and artificial intelligence approaches have been increasingly applied in the context of toxicological risk assessment. Many published overview, review, and comment papers discuss advantages, disadvantages, success stories, and open challenges for the application of machine learning models in toxicology. Machine learning methods using information from in vitro experiments can help to avoid animal experiments, thus allowing for larger numbers of experiments to be conducted. Drawbacks of machine learning models are the lack of mechanistic interpretability and the need for large amounts of high-quality data. In this work, we present a literature review of papers indexed in PubMed or published in the journal Computational Toxicology in the years 2022 to 2024, to assess the usage of machine learning methods in toxicology as well as the practices in reporting of methods and corresponding results. We do not address the suitability or the performance of methods, which is impossible to assess objectively without reanalysis on raw data, but focus on common practices and gaps in reporting. Major results are that many different machine learning methods are used in toxicology, often with appropriate internal validation. However, in only half of the cases, interpretation methods are used to address the problem that these models often make predictions as a black box. Moreover, there are very frequent gaps in reporting, in particular related to handling of missing values, and availability of data and code. Thus, this review can serve as a starting point for further tailored methodological research and guidance.
2 Aug 2026
Read appraisal →Journal of chemical information and modeling
Characterizing the MSMP-CCR2 Interaction through Molecular Dynamics Simulations and Machine Learning Approaches
The MSMP (MicroSeminoProtein, Prostate-associated) protein is overexpressed in several cancers, including prostate, ovarian, and breast cancers. Its overexpression is particularly prevalent in tumors resistant to hormonal therapy, as well as to antiangiogenic treatments (e.g., anti-VEGF therapies). In hypoxic tumor microenvironments, characteristic of solid tumors, MSMP expression increases and facilitates tumor growth. MSMP binds to the transmembrane receptor CCR2 (C-C chemokine receptor type 2), a GPCR present on monocytes and lymphocytes. This interaction stabilizes an active conformation of CCR2, enabling downstream MAP kinase signaling pathways to reactivate androgen synthesis and promote tumor progression. In this study, we employed molecular modeling, molecular dynamics simulations, and machine learning techniques to elucidate the structural basis of the MSMP-CCR2 interaction. High-resolution models of the MSMP-CCR2-G protein complex were generated using AlphaFold2 and refined with MD simulations. Comparative analyses with the CCL2-CCR2-G protein complex and the unbound CCR2-G protein complex revealed key conformational rearrangements that modulate and stabilize the active state of the receptor. Binding free energy calculations revealed similar energetic profiles for MSMP and CCL2, despite distinct binding modes. Residue-level energy decomposition identified critical residues at the MSMP-CCR2 interface, offering insights into the receptor's activation mechanism. Additionally, machine learning models classified molecular dynamics trajectories, highlighting key structural features associated with stable MSMP-CCR2-G protein states. This integrative approach identified residues essential for maintaining a stable conformation of the MSMP-CCR2-G protein complex, providing a basis for targeting MSMP-CCR2 interactions in therapeutic development.
14 July 2026
Read appraisal →Journal of computer-aided molecular design
Two faces of drug repurposing data in machine learning-driven structure-based virtual screening supported by interaction fingerprints
Ensemble docking is a prominent source of protein-ligand complex structures, which could be conveniently analyzed with interaction fingerprints approach in a machine learning framework. Nevertheless, the chemical nature and provenance of the source data libraries used for model development may influence the predictive ability. We assessed the performance of SARS-CoV-2 main protease (Mpro) inhibitors classification by random forest models trained on different fingerprint implementations using three inhibitor libraries, comprising drug repurposing data or compounds rationally designed as Mpro inhibitors. Models trained on drug repurposing data showed a low prediction performance on their own, while infusion of these data into libraries with specific inhibitors greatly improved applicability domains of the models. Our results highlight the importance of balanced dataset preparation for machine learning applications in medicinal chemistry, especially when relied on drug repurposing experiments.
8 July 2026
Read appraisal →Journal of integrative medicine
Deep learning in traditional Chinese medicine
As an alternative medicine approach, traditional Chinese medicine (TCM) is mainly built on an ancient understanding of the world. In order to translate the promises of TCM into modern therapies, rigorous scientific techniques are applied, which generates big data from experiments, clinical practices and medical literature. Recently, deep learning (DL) has attracted the attention of TCM researchers due to its application in data mining. In this review, we summarized recent examples of DL application in the study of TCM, including medical image processing, medicinal substances investigation, data fusion and natural language processing. We find that DL methods have considerable potential to extract valuable information from TCM data, but high-quality TCM data are still needed for further development of DL techniques. Please cite this article as: Liu LP, Yang C, Cen SX, Suo TC. Deep learning in traditional Chinese medicine. J Integr Med. 2026; 24(4):471-480.
1 July 2026
Read appraisal →PloS one
A multi-modal co-attention model for accurate drug-target interaction prediction
Accurate prediction of drug-target interactions (DTIs) plays a crucial role in modern drug discovery and repositioning. Despite recent advances in deep learning, existing methods often fail to effectively integrate heterogeneous data, such as molecular structures and protein sequences, into a unified representation. To address this limitation, we propose MMCA (Multi-Modal Co-Attention), a novel deep learning framework that introduces a multi-modal co-attention mechanism to dynamically align and fuse graph-based drug features with sequence-based protein embeddings. Our model leverages parallel encoding pathways to capture both structural and semantic information, followed by a context-aware fusion module that adaptively weighs cross-modal dependencies. Evaluation on three benchmark datasets-BioSNAP, BindingDB, and Human STRING-demonstrates that MMCA outperforms state-of-the-art methods in terms of AUC, AUPR, and F1-score, achieving up to 98.4% AUC. Ablation studies confirm the significance of our co-attention fusion mechanism in enhancing both accuracy and robustness. Case studies of high-confidence predictions reveal biologically plausible drug-protein interactions, supporting MMCA's potential for prioritizing candidates for experimental validation. By enabling end-to-end multi-modal reasoning, MMCA provides a powerful framework for advancing DTI prediction systems and offers broad applicability for various bioinformatics tasks. The source code of MMCA is publicly available at https://github.com/Join-xiaobai/MMCA.
25 June 2026
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