Computational biology and chemistry最新文献

{"title":"Deploying machine learning and metagenomics for predicting gut microbial drug biotransformation in colorectal cancer.","authors":"Hema Palanisamy, Subramanian Vidyalakshmi","doi":"10.1016/j.compbiolchem.2026.109495","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2026.109495","url":null,"abstract":"<p><p>Colorectal cancer (CRC) is one of the leading causes of death globally. Chemotherapy is considered as one of the standard treatment option for CRC. Although effective in many cases, drugs used in chemotherapy are often associated with adverse toxicity. Inter-individual variations in drug response are often attributed to pharmacogenomic associations, and current evidence suggests involvement of gut microbes as a contributor to drug response variations. While several studies have implemented machine learning to predict drug responses mediated by gut microbial enzymes, these predictions mainly rely on substrate information and bacterial genome data. In this study, we have developed CRCGMMeta Predictor, a tool that predicts gut microbial enzymes and species capable of biotransforming drugs. Unlike existing tools, CRCGMMeta Predictor integrates enzyme prediction with species-level abundance in CRC metagenomes using eight geographically diverse metagenomic studies. The tool was trained on 437,079 species-specific enzyme data, incorporating 3457 substrate and protein-encoding features. Multiple machine learning models were evaluated, with the random forest model achieving 99% accuracy, precision, recall, F1 score and ROC-AUC metrics. Validation of the generated model with a blind set achieved 97% accuracy, while 10-fold cross-validation achieved an accuracy of 0.831 ± 0.068. Based on the above results, the random forest model was selected to build the CRCGMMeta Predictor tool. This was further assessed against fluoropyrimidine drugs and its ability to predict enzymes supported by literature evidence and those predicted by existing computational tools. These findings highlight the potential of CRCGMMeta Predictor in identifying microbiome-mediated drug responses.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"126 Pt 2","pages":"109495"},"PeriodicalIF":0.0,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Uncovering hub genes and key pathways responsive to drought stress in rice via meta-analysis of transcriptomic data\" [Comput. Biol. Chem. 126P1 (2026) 109394].","authors":"Sachin Joshi, Parneeta Chaudhary, Rishi Mrinal, Ankita Chauhan, Niharika Pandey, Sneh Gautam, Pushpa Lohani","doi":"10.1016/j.compbiolchem.2026.109440","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2026.109440","url":null,"abstract":"","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":" ","pages":"109440"},"PeriodicalIF":0.0,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"Computational identification of AKT1-modulating phytochemicals from Moringa oleifera targeting the PI3K/AKT/mTOR pathway in autism spectrum disorder\" [Comput. Biol. Chem. 124 (2026) 109119].","authors":"Khalil Said, Sumreena Mansoor, Asif Hussain, Warda Gul, Shumaila Siraj, Mohamed M Hassan, Muhammad Hamayun","doi":"10.1016/j.compbiolchem.2026.109372","DOIUrl":"https://doi.org/10.1016/j.compbiolchem.2026.109372","url":null,"abstract":"","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":" ","pages":"109372"},"PeriodicalIF":0.0,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI fragment based optimization of saffron and chamomile phytochemicals as aryl hydrocarbon receptor inhibitors for dementia therapy an integrated computational approach.","authors":"Asra Khan, Nouman Ali, Beenish Asrar, Saara Ahmad","doi":"10.1016/j.compbiolchem.2025.108606","DOIUrl":"10.1016/j.compbiolchem.2025.108606","url":null,"abstract":"<p><p>Dementia represents a rapidly rising global health challenge as a progressive neurodegenerative disease with few options for disease-modifyingtreatments. The present studyaimed to explore the leading phytochemicals from Crocus sativus (saffron) and Matricaria chamomilla (chamomile) and apply AI fragmentation on lead phytochemicals to target the aryl hydrocarbon receptor (AHR), an expertized target for dementia therapy. Bioactive compounds were screened from ISO 3632-2-2010 (E) specified for saffron and GC-MS specified for chamomile. Protein Network mapping, Density Functional Theory, Molecular docking, and molecular dynamics simulations were performed to determine thebinding affinity and interactions stability of key phytochemicals with AHR, such as safranal and bisabolone oxide A. In-silico ADMET predictions of pharmacokinetics and toxicity showed good properties for these molecules. In addition, their structuraland pharmacological properties were optimized to enhance drug-like features by using artificial intelligence (AI) generative model. Collectively, our findings highlight these AI-enhanced phytochemicals as promising AHR modulators with potentially therapeutic activities in pathological pathways that lead toneuroinflammation and oxidative stress involved in the pathogenesis of dementia. They offer an avenue for additional experimental validation and encourage further investigation of these leads as sources of new therapeutic modalities to treat neurodegenerativediseases.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108606"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative artificial intelligence and large language models in smart healthcare applications: Current status and future perspectives.","authors":"Md Asraful Haque, Hifzur R Siddique","doi":"10.1016/j.compbiolchem.2025.108611","DOIUrl":"10.1016/j.compbiolchem.2025.108611","url":null,"abstract":"<p><p>With climate change, habitat destruction, and increased population ages, the incidence of both communicable and non-communicable diseases is rising, and managing these has become a growing concern. In recent years, generative artificial intelligence (AI) and large language models (LLMs) have ushered in a transformative era for smart healthcare applications. These models, built on advanced ML architectures like Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT), have demonstrated significant capabilities in various medical tasks. This review aims to provide an overview of the potential benefits of generative AI and LLMs in smart healthcare applications, as well as challenges and ethical considerations. A systematic literature review was conducted to identify relevant research papers published in peer-reviewed journals. Databases such as PubMed, PMC, Cochrane Library, Google Scholar, and Web of Science were searched using keywords related to generative AI, LLMs, and healthcare applications. The relevant papers were analyzed to extract key findings and contributions. Generative AI and LLMs are powerful tools that can process and analyze massive amounts of data. Researchers are actively exploring their potential to transform healthcare-powering intelligent virtual health assistants, crafting personalized patient care plans, and facilitating early detection and intervention for medical conditions. With ongoing research and development, the future of generative AI and LLMs in healthcare is promising; however, issues such as bias in AI models, lack of explainability, ethical concerns, and integration difficulties must be addressed.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108611"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting antigenic peptides using a multi-level pooling-based transformer model with enhanced Kolaskar & Tongaonkar's algorithm for feature selection.","authors":"Ashwini S, Minu R I, Jeevan Kumar M","doi":"10.1016/j.compbiolchem.2025.108615","DOIUrl":"10.1016/j.compbiolchem.2025.108615","url":null,"abstract":"<p><p>Antigenic peptide (AP) prediction is one of the most important roles in improve vaccine design and interpreting immune responses. This paper develops a Multi-Level Pooling-based Transformer (MLPT) model, which improves the accuracy and efficiency of predicting T-cell epitopes (TCEs). The model has utilized peptide sequences from the Immune Epitope Database (IEDB) and utilized a refined Kolaskar & Tongaonkar algorithm for feature extraction as well as a Self-Improved Black-winged Kite optimization algorithm to optimize the scoring matrix. The MLPT architecture takes the input features from the Adaptive Depthwise Multi-Kernel Atrous Module (ADMAM) as inputs to the Swin Transformer, and the output of Swin block 1 is concatenated with the features extracted from the Kolaskar-Tongaonkar algorithm with the SA-BWK model. This hierarchical integration enhances feature representation and predictive capability. Advanced feature extraction, coupled with optimized feature selection for the MLPT model improves its performance over the conventional approach in the identification of reduced-complexity antigenic determinants.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108615"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144805434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-step joint model based on deep learning realizes intelligent recognition of exfoliated cells in serous effusion.","authors":"Yige Yin, Xiaotao Li, Dongsheng Li, Yue Hu, Qiang Wu, Jiarong Zhao, Qiuyan Sun, Hong-Qiang Wang, Wulin Yang","doi":"10.1016/j.compbiolchem.2025.108616","DOIUrl":"10.1016/j.compbiolchem.2025.108616","url":null,"abstract":"<p><p>Cytological examination of serous effusion is critical for diagnosing malignancies, yet it heavily relies on subjective interpretation by pathologists, leading to inconsistent accuracy and misdiagnosis, especially in regions with limited medical resources. To address this challenge, we propose a two-step deep learning framework to standardize and enhance the diagnostic process. First, we improved the YOLOv8 model by integrating the Online Convolutional Reparameterization (OREPA) module, achieving a 93.09 % sensitivity for detecting abnormal cells. Second, we employed the Dual Attention Vision Transformer (DaViT) to classify normal cells (lymphocytes, mesothelial cells, histiocytes, neutrophils) with 98.74 % accuracy. By jointly deploying these models, our approach reduces missed diagnoses and provides granular insights into cell composition, offering a robust tool for rapid and objective cytopathological diagnosis. This work bridges the gap between AI-driven automation and clinical needs, particularly in resource-constrained settings.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108616"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144818599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of biselyngbyaside B a novel lead inhibitor of drug-resistant bacteria targeting DNA gyrase B.","authors":"Kiran Mahapatra, Swagat Ranjan Maharana, Showkat Ahmad Mir, Munmun Bordhan, Binata Nayak","doi":"10.1016/j.compbiolchem.2025.108628","DOIUrl":"10.1016/j.compbiolchem.2025.108628","url":null,"abstract":"<p><p>Antimicrobial resistance (AMR) poses a growing global threat, with antibiotic-resistant infections becoming a leading cause of death worldwide. The present study explores natural cyanobacterial compounds as possible inhibitors of Escherichia coli DNA gyrase B (GyrB) which is a verified antibacterial target that is not present in higher eukaryotes. Because of the urgent need for novel antibacterial drugs, we identified nine drug-like candidates using lipinski's rule of five and ADMET profiling. Molecular docking revealed that Biselyngbyaside B and Smenamide A exhibited greater binding affinities in comparison to the co-crystallized inhibitor EOF, with a binding energy of -9.03 kcal/mol. Further molecular dynamics simulations revealed that the Biselyngbyaside B-DNA gyrase B complex surpassed both EOF and Smenamide A in terms of structural stability, compactness, and strong hydrogen bonding. Umbrella sampling was employed to estimate the binding free energy from thirty sampling simulations, and Biselyngbyaside B exhibited a significantly favourable ΔG bind of -91.66 kJ/mol, outperforming EOF (-68.93 kJ/mol) and Smenamide A (-36.4 kJ/mol). These findings clearly indicate a stronger and more stable interaction between Biselyngbyaside B and GyrB. Biselyngbyaside B continuously showed better pharmacokinetic characteristics, non-hepatotoxicity, and a greater binding affinity than previously documented DNA gyrase B inhibitors. This study emphasizes the integration of molecular dockings, molecular dynamics simulation, umbrella sampling, and ADMET analysis provided crucial quantitative insights into the identification of potent drug-like candidates for further validation. Overall, the Biselyngbyaside B was found to be the most promising lead compound for novel antibacterial drug development targeting DNA gyrase B.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108628"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trichomonas vaginalis acid sphingomyelinases' theoretical structural analysis shows substrate binding diversity related to protein flexibility and mobility.","authors":"Ana Laura Medina-Nieto, Sairy Yarely Andrade-Guillen, Fátima Berenice Ramírez-Montiel, Fátima Tornero-Gutiérrez, José A Martínez-Álvarez, Ángeles Rangel-Serrano, Itzel Páramo-Pérez, Naurú Idalia Vargas-Maya, Javier de la Mora, Claudia Leticia Mendoza-Macías, Patricia Cuéllar-Mata, Nayeli Alva-Murillo, Bernardo Franco, Felipe Padilla-Vaca","doi":"10.1016/j.compbiolchem.2025.108601","DOIUrl":"10.1016/j.compbiolchem.2025.108601","url":null,"abstract":"<p><p>Acid sphingomyelinases (aSMases) are enzymes involved in the repair of the plasma membrane in eukaryotic cells. However, neutral sphingomyelinases (nSMases) have also been shown to possess other roles in bacteria and eukaryotic microorganisms, especially as virulence factors. These enzymes exhibit structural conservation but are characterized by elusive homology and the lack of sequence signatures or motifs. In a previous study, we reported the structural features of the complete set of sphingomyelinases (SMases) in Entamoeba histolytica and Trichomonas vaginalis, showing structural homology and functional differences in two aSMases from E. histolytica (EhSMase). However, the approach was limited due to the AlphaFold3 source code not being publicly available at the time. In this report, the structural transitions in the aSMases from T. vaginalis (TvSMase) were measured using open-source AlphaFold3 and collective motions of proteins via Normal Mode Analysis in internal coordinates. They compared them with the models from aSMase4 (EHI_100080) and aSMase6 (EHI_125660) from E. histolytica, containing different combinations of ligands. Using full-length sphingomyelin and the Mg²⁺ and Co²⁺ ions, where Co²⁺ was shown to inhibit the enzymes of both organisms, we demonstrate that the enzymes exhibit limited flexibility and deformability, except for the T. vaginalis TVAG_271580 enzyme, which displays high structural deformability. This contrasts with the inhibitory mechanism elicited by Co²⁺ as shown previously. TVSMase3 (TVAG_222460) could not be modelled with the sphingomyelin in the active site pocket, suggesting a regulatory role rather than a functional active enzyme. Additional physicochemical parameters calculated for T. vaginalis enzymes suggest unstable structures and high internal mobility (estimated using the Internal Coordinate method), which may be associated with the functional role of these enzymes. The results presented here open an avenue for searching for novel inhibitors of aSMases that target their physical properties, which could potentially complement treatment to control the parasite burden. These inhibitors could be valuable for further studying the role of these enzymes in parasite pathobiology and, potentially, as therapeutic targets.</p>","PeriodicalId":93952,"journal":{"name":"Computational biology and chemistry","volume":"120 Pt 1","pages":"108601"},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}