Journal of pharmaceutical analysis最新文献_第2页

Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction. 基于多尺度信息融合和解耦表示学习的微生物-疾病交互预测。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2024-10-29 DOI: 10.1016/j.jpha.2024.101134

Wentao Wang, Qiaoying Yan, Qingquan Liao, Xinyuan Jin, Yinyin Gong, Linlin Zhuo, Xiangzheng Fu, Dongsheng Cao

{"title":"Multi-scale information fusion and decoupled representation learning for robust microbe-disease interaction prediction.","authors":"Wentao Wang, Qiaoying Yan, Qingquan Liao, Xinyuan Jin, Yinyin Gong, Linlin Zhuo, Xiangzheng Fu, Dongsheng Cao","doi":"10.1016/j.jpha.2024.101134","DOIUrl":"https://doi.org/10.1016/j.jpha.2024.101134","url":null,"abstract":"Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101134"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234401","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

DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction. DTLCDR：基于靶标的多模态融合深度学习框架，用于癌症药物反应预测。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-04-21 DOI: 10.1016/j.jpha.2025.101315

Jie Yu, Cheng Shi, Yiran Zhou, Ningfeng Liu, Xiaolin Zong, Zhenming Liu, Liangren Zhang

{"title":"DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.","authors":"Jie Yu, Cheng Shi, Yiran Zhou, Ningfeng Liu, Xiaolin Zong, Zhenming Liu, Liangren Zhang","doi":"10.1016/j.jpha.2025.101315","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101315","url":null,"abstract":"Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101315"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234307","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

An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph. 使用多关系药物-疾病-基因图预测药物-基因相互作用的基于归纳学习的方法。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-05-16 DOI: 10.1016/j.jpha.2025.101347

Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

{"title":"An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph.","authors":"Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo","doi":"10.1016/j.jpha.2025.101347","DOIUrl":"10.1016/j.jpha.2025.101347","url":null,"abstract":"Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101347"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115879","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

Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach. 优化KRAS抑制剂的血脑屏障通透性：一种结构约束的分子生成方法。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-05-09 DOI: 10.1016/j.jpha.2025.101337

Xia Sheng, Yike Gui, Jie Yu, Yitian Wang, Zhenghao Li, Xiaoya Zhang, Yuxin Xing, Yuqing Wang, Zhaojun Li, Mingyue Zheng, Liquan Yang, Xutong Li

{"title":"Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach.","authors":"Xia Sheng, Yike Gui, Jie Yu, Yitian Wang, Zhenghao Li, Xiaoya Zhang, Yuxin Xing, Yuqing Wang, Zhaojun Li, Mingyue Zheng, Liquan Yang, Xutong Li","doi":"10.1016/j.jpha.2025.101337","DOIUrl":"10.1016/j.jpha.2025.101337","url":null,"abstract":"Kirsten rat sarcoma viral oncogene homolog (KRAS) protein inhibitors are a promising class of therapeutics, but research on molecules that effectively penetrate the blood-brain barrier (BBB) remains limited, which is crucial for treating central nervous system (CNS) malignancies. Although molecular generation models have recently advanced drug discovery, they often overlook the complexity of biological and chemical factors, leaving room for improvement. In this study, we present a structure-constrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties. Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization. This method specifically aims to enhance BBB permeability (BBBp) while maintaining high-affinity substructures of KRAS inhibitors. To support this, we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models. Additionally, we introduce two novel metrics, the knowledge-integrated reproduction score (KIRS) and the composite diversity score (CDS), to assess structural performance and biological relevance. Retrospective validation with KRAS inhibitors, AMG510 and MRTX849, demonstrates the framework's effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications. This study provides a robust framework for accelerating the structural enhancement of lead compounds, advancing the drug development process across diverse targets.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101337"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984991","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

Artificial intelligence and computational methods in human metabolism research: A comprehensive survey. 人工智能与计算方法在人体代谢研究中的应用综述。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-08-18 DOI: 10.1016/j.jpha.2025.101437

Manzhan Zhang, Yuxin Wan, Jing Wang, Shiliang Li, Honglin Li

引用次数: 0

Discovery of selective HDAC6 inhibitors driven by artificial intelligence and molecular dynamics simulation approaches. 利用人工智能和分子动力学模拟方法发现选择性HDAC6抑制剂。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-05-12 DOI: 10.1016/j.jpha.2025.101338

Xingang Liu, Hao Yang, Xinyu Liu, Minjie Mou, Jie Liu, Wenying Yan, Tianle Niu, Ziyang Zhang, He Shi, Xiangdong Su, Xuedong Li, Yang Zhang, Qingzhong Jia

{"title":"Discovery of selective HDAC6 inhibitors driven by artificial intelligence and molecular dynamics simulation approaches.","authors":"Xingang Liu, Hao Yang, Xinyu Liu, Minjie Mou, Jie Liu, Wenying Yan, Tianle Niu, Ziyang Zhang, He Shi, Xiangdong Su, Xuedong Li, Yang Zhang, Qingzhong Jia","doi":"10.1016/j.jpha.2025.101338","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101338","url":null,"abstract":"Increasing evidence showed that histone deacetylase 6 (HDAC6) dysfunction is directly associated with the onset and progression of various diseases, especially cancers, making the development of HDAC6-targeted anti-tumor agents a research hotspot. In this study, artificial intelligence (AI) technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline, which combined Voting strategy based on compound-protein interaction (CPI) prediction models, cascade molecular docking, and molecular dynamic (MD) simulations. The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays. Among the identified compounds, Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity (IC50 = 5.41 nM) than that of tubastatin A (TubA) (IC50 = 15.11 nM), along with a favorable subtype selectivity profile (selectivity index ≈ 117.23 for HDAC1), which was further verified by the Western blot analysis. Additionally, Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells, exerting desirable antiproliferative activity (IC50 = 2.59 μM). Furthermore, based on long-term MD simulation trajectory, the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis, thereby elucidating its binding mechanism. Moreover, the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation, thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101338"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234810","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 multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors. 预测p糖蛋白底物和抑制剂的多模态对比学习框架。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-04-16 DOI: 10.1016/j.jpha.2025.101313

Yixue Zhang, Jialu Wu, Yu Kang, Tingjun Hou

{"title":"A multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.","authors":"Yixue Zhang, Jialu Wu, Yu Kang, Tingjun Hou","doi":"10.1016/j.jpha.2025.101313","DOIUrl":"10.1016/j.jpha.2025.101313","url":null,"abstract":"P-glycoprotein (P-gp) is a transmembrane protein widely involved in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs within the human body. Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment. However, existing models rely on limited molecular information, leading to suboptimal model performance for predicting P-gp inhibitors and substrates. To overcome this challenge, we compiled an extensive dataset from public databases and literature, consisting of 5,943 P-gp inhibitors and 4,018 substrates, notable for their high quantity, quality, and structural uniqueness. In addition, we curated two external test sets to validate the model's generalization capability. Subsequently, we developed a multimodal graph contrastive learning (GCL) model for the prediction of P-gp inhibitors and substrates (MC-PGP). This framework integrates three types of features from Simplified Molecular Input Line Entry System (SMILES) sequences, molecular fingerprints, and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation. Furthermore, we employed a GCL approach to enhance structural representations by aligning local and global structures. Extensive experimental results highlight the superior performance of MC-PGP, which achieves improvements in the area under the curve of receiver operating characteristic (AUC-ROC) of 9.82% and 10.62% on the external P-gp inhibitor and external P-gp substrate datasets, respectively, compared with 12 state-of-the-art methods. Furthermore, the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights, demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions. These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101313"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145017051","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

ToxBERT: an explainable AI framework for enhancing prediction of adverse drug reactions and structural insights. ToxBERT：一个可解释的AI框架，用于增强药物不良反应的预测和结构洞察。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-07-03 DOI: 10.1016/j.jpha.2025.101387

Yujie He, Xiang Lv, Wulin Long, Shengqiu Zhai, Menglong Li, Zhining Wen

{"title":"ToxBERT: an explainable AI framework for enhancing prediction of adverse drug reactions and structural insights.","authors":"Yujie He, Xiang Lv, Wulin Long, Shengqiu Zhai, Menglong Li, Zhining Wen","doi":"10.1016/j.jpha.2025.101387","DOIUrl":"10.1016/j.jpha.2025.101387","url":null,"abstract":"Accurate prediction of drug-induced adverse drug reactions (ADRs) is crucial for drug safety evaluation, as it directly impacts public health and safety. While various models have shown promising results in predicting ADRs, their accuracy still needs improvement. Additionally, many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints, which can introduce bias. To address these challenges, we propose ToxBERT, an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system (SMILES) representations. Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve (AUROC) scores of 0.839, 0.759, and 0.664 for predicting drug-induced QT prolongation (DIQT), rhabdomyolysis, and liver injury, respectively, outperforming previous studies. Furthermore, ToxBERT can identify drug substructures that are closely associated with specific ADRs. These findings indicate that ToxBERT is not only a valuable tool for understanding the mechanisms underlying specific drug-induced ADRs but also for mitigating potential ADRs in the drug discovery pipeline.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101387"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115862","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

Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process. 颗粒分层过程中人工智能辅助内窥镜在线粒度分析。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-02-12 DOI: 10.1016/j.jpha.2025.101227

Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata

{"title":"Artificial intelligence-aided endoscopic in-line particle size analysis during the pellet layering process.","authors":"Orsolya Péterfi, Nikolett Kállai-Szabó, Kincső Renáta Demeter, Ádám Tibor Barna, István Antal, Edina Szabó, Emese Sipos, Zsombor Kristóf Nagy, Dorián László Galata","doi":"10.1016/j.jpha.2025.101227","DOIUrl":"https://doi.org/10.1016/j.jpha.2025.101227","url":null,"abstract":"In this study, an artificial intelligence-based machine vision system was developed for in-line particle size analysis during the pellet layering process. Drug-layered pellets were produced by coating microcrystalline cellulose cores with an ibuprofen-containing layering liquid until the target drug content was achieved. Drug content increases with pellet size; therefore, particle size monitoring can ensure product safety and quality. The direct imaging system, consisting of a rigid endoscope, a light source, and a high-speed camera, provides real-time information about pellet size and layer uniformity, enabling timely intervention in the case of out-of-spec products. A convolutional neural network-based instance segmentation algorithm was employed to detect particles in focus, ensuring that pellet size could be accurately determined despite the dense flow of the particles. After training the model, the performance of the developed system was assessed by analysing the particle size distribution of pellet cores with variable sizes within the 250-850 μm size range. The endoscopic system was tested in-line at a larger scale during the drug layering of inert pellet cores. The particle size data acquired in real time with the endoscopic imaging system corresponded with the reference methods, demonstrating the feasibility of the proposed machine vision-based method as a process analytical technology tool for in-line process monitoring.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101227"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234881","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

ACtriplet: An improved deep learning model for activity cliffs prediction by in tegrating triplet loss and pre-training. ACtriplet：一种改进的深度学习模型，通过整合三元组损失和预训练来预测活动悬崖。

IF 8.9

Journal of pharmaceutical analysis Pub Date : 2025-08-01 Epub Date: 2025-04-21 DOI: 10.1016/j.jpha.2025.101317

Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li, Yun Tang, Guixia Liu

{"title":"ACtriplet: An improved deep learning model for activity cliffs prediction by in tegrating triplet loss and pre-training.","authors":"Xinxin Yu, Yimeng Wang, Long Chen, Weihua Li, Yun Tang, Guixia Liu","doi":"10.1016/j.jpha.2025.101317","DOIUrl":"10.1016/j.jpha.2025.101317","url":null,"abstract":"Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization.","PeriodicalId":94338,"journal":{"name":"Journal of pharmaceutical analysis","volume":"15 8","pages":"101317"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12398830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144984850","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