Journal of pharmaceutical analysis最新文献

{"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":"<p><p>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.</p>","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}

{"title":"TCMKD: From ancient wisdom to modern insights-A comprehensive platform for traditional Chinese medicine knowledge discovery.","authors":"Wenke Xiao, Mengqing Zhang, Danni Zhao, Fanbo Meng, Qiang Tang, Lianjiang Hu, Hongguo Chen, Yixi Xu, Qianqian Tian, Mingrui Li, Guiyang Zhang, Liang Leng, Shilin Chen, Chi Song, Wei Chen","doi":"10.1016/j.jpha.2025.101297","DOIUrl":"10.1016/j.jpha.2025.101297","url":null,"abstract":"<p><p>Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101297"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602750","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}

{"title":"Breaking barriers: MS-BDF tools in the quality control of insect-derived traditional Chinese medicine.","authors":"Caixia Yuan, Dandan Zhang, Hairong Zhang, Jiyang Dong, Caisheng Wu","doi":"10.1016/j.jpha.2025.101193","DOIUrl":"10.1016/j.jpha.2025.101193","url":null,"abstract":"<p><p>Image 1.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101193"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661531","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}

{"title":"Diffusion-based generative drug-like molecular editing with chemical natural language.","authors":"Jianmin Wang, Peng Zhou, Zixu Wang, Wei Long, Yangyang Chen, Kyoung Tai No, Dongsheng Ouyang, Jiashun Mao, Xiangxiang Zeng","doi":"10.1016/j.jpha.2024.101137","DOIUrl":"10.1016/j.jpha.2024.101137","url":null,"abstract":"<p><p>Recently, diffusion models have emerged as a promising paradigm for molecular design and optimization. However, most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geometries, with limited research on molecular sequence diffusion models. The International Union of Pure and Applied Chemistry (IUPAC) names are more akin to chemical natural language than the Simplified Molecular Input Line Entry System (SMILES) for organic compounds. In this work, we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language (SMILES) and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language. We propose DiffIUPAC, a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings. Evaluation results demonstrate that our model outperforms existing methods and successfully captures the semantic rules of both chemical languages. Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints. Additionally, to illustrate the model's applicability in drug design, we conducted case studies in functional group editing, analogue design and linker design.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101137"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661532","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}

{"title":"Combining transformer and 3DCNN models to achieve co-design of structures and sequences of antibodies in a diffusional manner.","authors":"Yue Hu, Feng Tao, Jiajie Xu, Wen-Jun Lan, Jing Zhang, Wei Lan","doi":"10.1016/j.jpha.2025.101267","DOIUrl":"10.1016/j.jpha.2025.101267","url":null,"abstract":"<p><p>Image 1.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101267"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12246596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144628412","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}

{"title":"Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models.","authors":"Yudong Yan, Yinqi Yang, Zhuohao Tong, Yu Wang, Fan Yang, Zupeng Pan, Chuan Liu, Mingze Bai, Yongfang Xie, Yuefei Li, Kunxian Shu, Yinghong Li","doi":"10.1016/j.jpha.2025.101275","DOIUrl":"10.1016/j.jpha.2025.101275","url":null,"abstract":"<p><p>Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101275"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661528","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}

{"title":"EvoNB: A protein language model-based workflow for nanobody mutation prediction and optimization.","authors":"Danyang Xiong, Yongfan Ming, Yuting Li, Shuhan Li, Kexin Chen, Jinfeng Liu, Lili Duan, Honglin Li, Min Li, Xiao He","doi":"10.1016/j.jpha.2025.101260","DOIUrl":"10.1016/j.jpha.2025.101260","url":null,"abstract":"<p><p>The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101260"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661534","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}

{"title":"3D-EDiffMG: 3D equivariant diffusion-driven molecular generation to accelerate drug discovery.","authors":"Chao Xu, Runduo Liu, Yufen Yao, Wanyi Huang, Zhe Li, Hai-Bin Luo","doi":"10.1016/j.jpha.2025.101257","DOIUrl":"10.1016/j.jpha.2025.101257","url":null,"abstract":"<p><p>Structural optimization of lead compounds is a crucial step in drug discovery. One optimization strategy is to modify the molecular structure of a scaffold to improve both its biological activities and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. One of the deep molecular generative model approaches preserves the scaffold while generating drug-like molecules, thereby accelerating the molecular optimization process. Deep molecular diffusion generative models simulate a gradual process that creates novel, chemically feasible molecules from noise. However, the existing models lack direct interatomic constraint features and struggle with capturing long-range dependencies in macromolecules, leading to challenges in modifying the scaffold-based molecular structures, and creates limitations in the stability and diversity of the generated molecules. To address these challenges, we propose a deep molecular diffusion generative model, the three-dimensional (3D) equivariant diffusion-driven molecular generation (3D-EDiffMG) model. The dual strong and weak atomic interaction force-based long-range dependency capturing equivariant encoder (dual-SWLEE) is introduced to encode both the bonding and non-bonding information based on strong and weak atomic interactions. Additionally, a gate multilayer perceptron (gMLP) block with tiny attention is incorporated to explicitly model complex long-sequence feature interactions and long-range dependencies. The experimental results show that 3D-EDiffMG effectively generates unique, novel, stable, and diverse drug-like molecules, highlighting its potential for lead optimization and accelerating drug discovery.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101257"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661526","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}

{"title":"Machine learning-assisted microfluidic approach for broad-spectrum liposome size control.","authors":"Yujie Jia, Xiao Liang, Li Zhang, Jun Zhang, Hajra Zafar, Shan Huang, Yi Shi, Jian Chen, Qi Shen","doi":"10.1016/j.jpha.2025.101221","DOIUrl":"10.1016/j.jpha.2025.101221","url":null,"abstract":"<p><p>Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101221"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577471","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}

{"title":"A disentangled generative model for improved drug response prediction in patients via sample synthesis.","authors":"Kunshi Li, Bihan Shen, Fangyoumin Feng, Xueliang Li, Yue Wang, Na Feng, Zhixuan Tang, Liangxiao Ma, Hong Li","doi":"10.1016/j.jpha.2024.101128","DOIUrl":"10.1016/j.jpha.2024.101128","url":null,"abstract":"<p><p>Personalized drug response prediction from molecular data is an important challenge in precision medicine for treating cancer. Computational methods have been widely explored and have become increasingly accurate in recent years. However, the clinical application of prediction methods is still in its infancy due to large discrepancies between preclinial models and patients. We present a novel disentangled synthesis transfer network (DiSyn) for drug response prediction specifically designed for transfer learning from preclinical models to clinical patients. DiSyn uses a domain separation network (DSN) to disentangle drug response related features, employs data synthesis technology to increase the sample size and iteratively trains for better feature disentanglement. DiSyn is pretrained on large-scale unlabeled cancer samples and validated by three datasets, The Cancer Genome Atlas (TCGA), Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And moLecular Analysis 2 (I-SPY2) and Novartis Institutes for Biomedical Research Patient-Derived Xenograft Encyclopedia (NIBR PDXE), achieving competitive performance with the state-of-the-art methods on cancer patients and mice. Furthermore, the application of DiSyn to thousands of breast cancer patients show the heterogeneity in drug responses and demonstrate its potential value in biomarker discovery and drug combination prediction.</p>","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 6","pages":"101128"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12268049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144661527","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}