Journal of pharmaceutical analysis最新文献_第5页

Biological activity analysis of baicalin nanodrugs: Nanosizing enhances antiviral and anti-inflammatory effects in the treatment of viral pneumonia. 黄芩苷纳米药物的生物活性分析：纳米增强抗病毒和抗炎作用治疗病毒性肺炎。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2025-01-21 DOI: 10.1016/j.jpha.2025.101201

Chenqi Chang, Chang Lu, Yu Zheng, Lili Lin, XiuZhen Chen, Linwei Chen, Zhipeng Chen, Rui Chen

{"title":"Biological activity analysis of baicalin nanodrugs: Nanosizing enhances antiviral and anti-inflammatory effects in the treatment of viral pneumonia.","authors":"Chenqi Chang, Chang Lu, Yu Zheng, Lili Lin, XiuZhen Chen, Linwei Chen, Zhipeng Chen, Rui Chen","doi":"10.1016/j.jpha.2025.101201","DOIUrl":"10.1016/j.jpha.2025.101201","url":null,"abstract":"Respiratory syncytial virus (RSV) is a ubiquitous respiratory virus that affects individuals of all ages; however, there is a notable lack of targeted treatments. RSV infection is associated with a range of respiratory symptoms, including bronchiolitis and pneumonia. Baicalin (BA) exhibits significant therapeutic effects against RSV infection through mechanisms of viral inhibition and anti-inflammatory action. Nonetheless, the clinical application of BA is constrained by its low solubility and bioavailability. In this study, we prepared BA nanodrugs (BA NDs) with enhanced water solubility utilizing the supramolecular self-assembled strategy, and we further conducted a comparative analysis of this pharmacological activity between free drugs and NDs of BA. Both in vitro and in vivo results demonstrated that BA NDs significantly enhanced the dual effects of viral inhibition and inflammation relief compared to free BA, attributed to prolonged lung retention, improved cellular uptake, and increased targeting affinity. Our study confirms that the nanosizing strategy, a straightforward approach to enhance drug solubility, can also increase biological activity compared to free drugs with the same content, thereby providing a potential ND for RSV treatment. This correlation analysis between the existing forms of drugs and their biological activity offers a novel perspective for research on the active ingredients of traditional Chinese medicine.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101201"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12305573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144746712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

E3 ubiquitin ligase FBXW11-mediated downregulation of S100A11 promotes sensitivity to PARP inhibitor in ovarian cancer. E3泛素连接酶fbxw11介导的S100A11下调促进卵巢癌对PARP抑制剂的敏感性。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2025-02-27 DOI: 10.1016/j.jpha.2025.101246

Ligang Chen, Mingyi Wang, Yunge Gao, Yanhong Lv, Lianghao Zhai, Jian Dong, Yan Chen, Xia Li, Xin Guo, Biliang Chen, Yi Ru, Xiaohui Lv

{"title":"E3 ubiquitin ligase FBXW11-mediated downregulation of S100A11 promotes sensitivity to PARP inhibitor in ovarian cancer.","authors":"Ligang Chen, Mingyi Wang, Yunge Gao, Yanhong Lv, Lianghao Zhai, Jian Dong, Yan Chen, Xia Li, Xin Guo, Biliang Chen, Yi Ru, Xiaohui Lv","doi":"10.1016/j.jpha.2025.101246","DOIUrl":"10.1016/j.jpha.2025.101246","url":null,"abstract":"Resistance to poly adenosine diphosphate (ADP)-ribose polymerase inhibitor (PARPi) presents a considerable obstacle in the treatment of ovarian cancer. F-box and tryptophan-aspartic (WD) repeat domain containing 11 (FBXW11) modulates the ubiquitination of growth-and invasion-related factors in lung cancer, colorectal cancer, and osteosarcoma. The function of FBXW11 in PARPi therapy is still ambiguous. In this study, RNA sequencing (RNA-seq) showed that FBXW11 expression was raised in ovarian cancer cells that had been treated with PARPi. FBXW11 was abnormally expressed at low levels in high-grade serous ovarian cancer (HGSOC) tissues, and low levels of FBXW11 were associated with shorter overall survival (OS) and progression-free survival (PFS) in HGSOC patients. Overexpressing FBXW11 made ovarian cancer more sensitive to PARPi, while knocking down FBXW11 made it less sensitive. The four-dimensional (4D) label-free quantitative proteomic analysis revealed that FBXW11 targeted S100 calcium binding protein A11 (S100A11) and promoted its degradation through ubiquitination. The increased degradation of S100A11 led to less efficient DNA damage repair, which in turn contributed to increased PARPi-induced DNA damage. The role of FBXW11 in promoting PARPi sensitivity was also confirmed in xenograft mouse models. In summary, our study confirms that FBXW11 promotes the susceptibility of ovarian cancer cells to PARPi via affecting S100A11-mediated DNA damage repair.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101246"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12311512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New insights into the dule roles CDK12 in human cancers: Mechanisms and interventions for cancer therapy. CDK12在人类癌症中作用的新见解：癌症治疗的机制和干预措施。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2024-12-28 DOI: 10.1016/j.jpha.2024.101173

Wei Dai, Dong Xie, Hao Huang, Jingxuan Li, Caiyao Guo, Fuqiang Cao, Luo Yang, Chengyong Zhong, Shenglan Liu

{"title":"New insights into the dule roles CDK12 in human cancers: Mechanisms and interventions for cancer therapy.","authors":"Wei Dai, Dong Xie, Hao Huang, Jingxuan Li, Caiyao Guo, Fuqiang Cao, Luo Yang, Chengyong Zhong, Shenglan Liu","doi":"10.1016/j.jpha.2024.101173","DOIUrl":"10.1016/j.jpha.2024.101173","url":null,"abstract":"The dysregulation of cyclin-dependent kinase 12 (CDK12), which may result from genomic alterations or modulation by upstream effectors, is implicated in cancer oncogenesis and progression. CDK12 overexpression or activation is sufficient to induce tumor initiation, recurrence, and therapeutic resistance. However, CDK12 may also exert tumor-suppressive functions in a context-dependent manner. Therefore, caution is warranted when targeting CDK12 in future clinical trials. A comprehensive elucidation of the dual roles and underlying mechanisms of CDK12 in carcinogenesis is urgently needed to advance precision oncology. This review provides an overview of the current understanding of the dysregulation and biological roles of CDK12 in cancer. Subsequently, we systematically summarize the functions and mechanisms of the oncogenic and tumor-suppressive roles of CDK12 in different contexts. Finally, we discuss the potential of CDK12 as a novel therapeutic target and its implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101173"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12311446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reproducibility of the NMR-based quantitative metabolomics and HBV-caused changes in human serum lipoprotein subclasses and small metabolites. 基于核磁共振的定量代谢组学和hbv引起的人血清脂蛋白亚类和小代谢物变化的可重复性。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2024-12-31 DOI: 10.1016/j.jpha.2024.101180

Qingxia Huang, Qinsheng Chen, Xiaoxuan Yi, Huan Wang, Qi Wang, Haijuan Zhi, Junfang Wu, Dao Wen Wang, Huiru Tang

引用次数: 0

The anti-hyperuricemia potential of bioactive natural products and extracts derived from traditional Chinese medicines: A review and perspective. 中药生物活性天然产物和提取物的抗高尿酸血症潜力：综述与展望。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-07-01 Epub Date: 2025-01-03 DOI: 10.1016/j.jpha.2024.101183

Yaolei Li, Zhijian Lin, Hongyu Jin, Feng Wei, Shuangcheng Ma, Bing Zhang

{"title":"The anti-hyperuricemia potential of bioactive natural products and extracts derived from traditional Chinese medicines: A review and perspective.","authors":"Yaolei Li, Zhijian Lin, Hongyu Jin, Feng Wei, Shuangcheng Ma, Bing Zhang","doi":"10.1016/j.jpha.2024.101183","DOIUrl":"10.1016/j.jpha.2024.101183","url":null,"abstract":"Hyperuricemia (HUA) and gout became typical metabolic disorders characterized by multiple pathogenic factors. Their incidence increased annually, affecting younger populations. Given that uric acid (UA) and inflammation were the primary disease mechanisms, the search for effective and low-side-effect UA-lowering and anti-inflammatory drugs became a pressing scientific priority. Traditional Chinese medicine (TCM) encompassed a rich array of theoretical and practical experience, along with a diverse range of chemical substances, making herbs or their components potential sources for therapeutic drugs. Despite the significant role that modern herbal medicines played in treating HUA and gout, the existing research literature remained fragmented, lacking comprehensive and systematic reviews. In this review, we focused on the regulation of UA and summarized the discovery of UA-lowering pharmacodynamic components or ingredients derived from herbs and formulas, as well as their multi-targeted mechanisms of action. Emphasizing this focus, we proposed that, compared to acute inflammation, low-grade inflammation may play a relatively \"unnoticed\" role in the disease process. In contrast to Western medicine, we discussed the risks and benefits of herbal medicines and their ingredients for treatment, drawing from theoretical insights and clinical practice. This review offered comprehensive perspectives on the research into anti-HUA and gout treatments using herbal medicines and their natural products. Additionally, it provided a forward-looking view on natural product discovery, the exploration of therapeutic strategies, and new drug research in this field.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 7","pages":"101183"},"PeriodicalIF":8.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In silico prediction of pK _a values using explainable deep learning methods. 使用可解释的深度学习方法进行pK值的计算机预测。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2024-12-28 DOI: 10.1016/j.jpha.2024.101174

Chen Yang, Changda Gong, Zhixing Zhang, Jiaojiao Fang, Weihua Li, Guixia Liu, Yun Tang

{"title":"In silico prediction of pK a values using explainable deep learning methods.","authors":"Chen Yang, Changda Gong, Zhixing Zhang, Jiaojiao Fang, Weihua Li, Guixia Liu, Yun Tang","doi":"10.1016/j.jpha.2024.101174","DOIUrl":"10.1016/j.jpha.2024.101174","url":null,"abstract":"Negative logarithm of the acid dissociation constant (pK a) significantly influences the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of molecules and is a crucial indicator in drug research. Given the rapid and accurate characteristics of computational methods, their role in predicting drug properties is increasingly important. Although many pK a prediction models currently exist, they often focus on enhancing model precision while neglecting interpretability. In this study, we present GraFpK a, a pK a prediction model using graph neural networks (GNNs) and molecular fingerprints. The results show that our acidic and basic models achieved mean absolute errors (MAEs) of 0.621 and 0.402, respectively, on the test set, demonstrating good predictive performance. Notably, to improve interpretability, GraFpK a also incorporates Integrated Gradients (IGs), providing a clearer visual description of the atoms significantly affecting the pK a values. The high reliability and interpretability of GraFpK a ensure accurate pK a predictions while also facilitating a deeper understanding of the relationship between molecular structure and pK a values, making it a valuable tool in the field of pK a prediction.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101174"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update. 基于人工智能的2型糖尿病治疗策略的进展：一个关键的更新。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2025-04-10 DOI: 10.1016/j.jpha.2025.101305

Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari

{"title":"Advancement of artificial intelligence based treatment strategy in type 2 diabetes: A critical update.","authors":"Aniruddha Sen, Palani Selvam Mohanraj, Vijaya Laxmi, Sumel Ashique, Rajalakshimi Vasudevan, Afaf Aldahish, Anupriya Velu, Arani Das, Iman Ehsan, Anas Islam, Sabina Yasmin, Mohammad Yousuf Ansari","doi":"10.1016/j.jpha.2025.101305","DOIUrl":"10.1016/j.jpha.2025.101305","url":null,"abstract":"In the unrelenting race to strive to dominate type 2 diabetes mellitus (T2DM) care better, this review paper sets out on a significant discovery trip across recent advancements in treatment and the blooming era of artificial intelligence (AI) utilities. Given the considerable global burden of T2DM, innovative therapeutic approaches to improve patient outcomes remain a public health priority. This review first provides an in-depth analysis of the current state of therapy, from novel pharmacotherapy to lifestyle interventions and new treatment methods. At the same time, the rapidly increasing role of AI in diabetes care is woven into the story, mainly targeting how insulin therapy can be modified and personalized through algorithms and predictive modelling. It leaves a deep review of their pre-existing synergies, which helps understand how collaborative opportunities will unlock the future of T2DM care. This critical role is shown by integrating recent therapeutic advances and AI with overall showcasing better screening, diagnosis, and therapeutics decision-making to outcome prediction in T2DM. The review emphasizes how AI applications in insulin therapy have transformative potential in diabetes care. These person-centred approaches to T2DM management, which are more effective and personalized than some traditional strategies, only work because of the often-hidden synergies between AI algorithms in areas such as diagnostic criteria, predictive methods, and familiar classification tools for subgroups with relevant aspects/predictors on prognosis or treatment responsiveness.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101305"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances and challenges in drug design against dental caries: Application of in silico approaches. 抗龋药物设计的进展与挑战：计算机方法的应用。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2024-12-09 DOI: 10.1016/j.jpha.2024.101161

Zhongxin Chen, Xinyao Zhao, Hanyu Zheng, Yufei Wang, Linglin Zhang

{"title":"Advances and challenges in drug design against dental caries: Application of in silico approaches.","authors":"Zhongxin Chen, Xinyao Zhao, Hanyu Zheng, Yufei Wang, Linglin Zhang","doi":"10.1016/j.jpha.2024.101161","DOIUrl":"10.1016/j.jpha.2024.101161","url":null,"abstract":"Dental caries, a chronic disease characterized by tooth decay, occupies the second position in terms of disease burden and is primarily caused by cariogenic bacteria, especially Streptococcus mutans, because of its acidogenic, aciduric, and biofilm-forming capabilities. Developing novel targeted anti-virulence agents is always a focal point in caries control to overcome the limitations of conventional anti-virulence agents. The current study represents an up-to-date review of in silico approaches of drug design against dental caries, which have emerged more and more powerful complementary to biochemical attempts. Firstly, we categorize the in silico approaches into computer-aided drug design (CADD) and AI-assisted drug design (AIDD) and highlight the specific methods and models they contain respectively. Subsequently, we detail the design of anti-virulence drugs targeting single or multiple cariogenic virulence targets of S. mutans, such as glucosyltransferases (Gtfs), antigen I/II (AgI/II), sortase A (SrtA), the VicRK signal transduction system and superoxide dismutases (SODs). Finally, we outline the current opportunities and challenges encountered in this field to aid future endeavors and applications of CADD and AIDD in anti-virulence drug design.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101161"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction. 基于指纹增强层次分子图神经网络的物业预测。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2025-02-20 DOI: 10.1016/j.jpha.2025.101242

Shuo Liu, Mengyun Chen, Xiaojun Yao, Huanxiang Liu

{"title":"Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.","authors":"Shuo Liu, Mengyun Chen, Xiaojun Yao, Huanxiang Liu","doi":"10.1016/j.jpha.2025.101242","DOIUrl":"10.1016/j.jpha.2025.101242","url":null,"abstract":"Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials. Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction. However, traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules. Similarly, graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information. To address these limitations, we propose a novel fingerprint-enhanced hierarchical graph neural network (FH-GNN) for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints. The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks (D-MPNN) on a hierarchical molecular graph that integrates atomic-level, motif-level, and graph-level information along with their relationships. Additionally, we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features, creating a comprehensive molecular embedding that integrated hierarchical molecular structures with domain knowledge. Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction, validating its capability to comprehensively capture molecular information. By integrating molecular structure and chemical knowledge, FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101242"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144628413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of transformer models in drug discovery and beyond. 药物发现及其他领域变压器模型综述。

Journal of pharmaceutical analysis Pub Date : 2025-06-01 Epub Date: 2024-08-30 DOI: 10.1016/j.jpha.2024.101081

Jian Jiang, Long Chen, Lu Ke, Bozheng Dou, Chunhuan Zhang, Hongsong Feng, Yueying Zhu, Huahai Qiu, Bengong Zhang, Guo-Wei Wei

{"title":"A review of transformer models in drug discovery and beyond.","authors":"Jian Jiang, Long Chen, Lu Ke, Bozheng Dou, Chunhuan Zhang, Hongsong Feng, Yueying Zhu, Huahai Qiu, Bengong Zhang, Guo-Wei Wei","doi":"10.1016/j.jpha.2024.101081","DOIUrl":"10.1016/j.jpha.2024.101081","url":null,"abstract":"Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101081"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0