Journal of Chemical Information and Modeling 最新文献_第9页

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28

Yago Ferreira e Silva, Harold Hilarion Fokoue and Paulo Ricardo Batista*,

引用次数: 0

MutPNI: A Unified Model Architecture for Accurately Predicting the Effects of Mutations on Protein-Nucleic Acid Interactions. MutPNI：用于准确预测突变对蛋白质-核酸相互作用影响的统一模型架构。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28 DOI: 10.1021/acs.jcim.5c00756

Zihao Yan,Fang Ge,Ying Zhang,He Yan,Yan Liu,Yi-Heng Zhu,Jiangning Song,Dong-Jun Yu

{"title":"MutPNI: A Unified Model Architecture for Accurately Predicting the Effects of Mutations on Protein-Nucleic Acid Interactions.","authors":"Zihao Yan,Fang Ge,Ying Zhang,He Yan,Yan Liu,Yi-Heng Zhu,Jiangning Song,Dong-Jun Yu","doi":"10.1021/acs.jcim.5c00756","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00756","url":null,"abstract":"Missense mutations in DNA- and RNA-binding proteins can disrupt vital interaction networks and frequently lead to disease. However, current methods for predicting changes in binding affinity between protein-DNA and protein-RNA remain fragmented and inefficient. In this study, we introduce MutPNI, a novel general regression model that predicts the effects of missense mutations in DNA-binding and RNA-binding proteins. To achieve this, we integrate feature embeddings from pretrained ESM-2 and ProtT5 protein language models and incorporate energy terms derived from mutant protein structures. We then train the model through a Transformer-based encoder, enabling it to attain PCC values of 0.661 and 0.701 on DNA and RNA benchmark test sets, respectively. Furthermore, our final strategy relies on the same model architecture, rather than identical parameters, to predict mutations in both protein-DNA and protein-RNA complexes, thereby highlighting shared features as well as key distinctions in the two data sets. Finally, the method's high computational efficiency allows for scaling to large biological data sets, offering a robust platform for future research. The web server and data sets for MutPNI are publicly available at https://csbioinformatics.njust.edu.cn/mutpni/ for academic use.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"57 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28

引用次数: 0

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28

Dinesh Kumar Jagannathareddy, and , Durba Roy*,

引用次数: 0

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28

Hassan Ayaz, Asia Nawaz, Sajjad Ahmad, Faisal Ahmad*, Anisa Tariq, Hanbal Ahmad Khan, Iftikhar Ahmed, Sidra Rahman, Muhammad Suleman, Dilber Uzun Ozsahin, Ilker Ozsahin and Yasir Waheed*,

引用次数: 0

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28

Patryk Tajs, Mateusz Skarupski and Jakub Rydzewski*,

引用次数: 0

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28

Clauber Henrique Souza da Costa, Camila Auad Beltrão de Freitas, Alberto Monteiro dos Santos, Carlos Gabriel da Silva de Souza, José Rogério A. Silva, Jerônimo Lameira, Vicent Moliner and Munir S. Skaf*,

引用次数: 0

Machine Learning-Based Retention Time Prediction Tool for Routine LC-MS Data Analysis. 基于机器学习的LC-MS数据分析保留时间预测工具。

IF 5.3 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28 Epub Date: 2025-07-16 DOI: 10.1021/acs.jcim.5c00514

Sofiia A Dymura, Oleksandr O Viniichuk, Kostiantyn P Melnykov, Dmytro S Radchenko, Oleksandr O Grygorenko

{"title":"Machine Learning-Based Retention Time Prediction Tool for Routine LC-MS Data Analysis.","authors":"Sofiia A Dymura, Oleksandr O Viniichuk, Kostiantyn P Melnykov, Dmytro S Radchenko, Oleksandr O Grygorenko","doi":"10.1021/acs.jcim.5c00514","DOIUrl":"10.1021/acs.jcim.5c00514","url":null,"abstract":"Accurate retention time (RT) prediction models can significantly improve liquid chromatography-mass spectrometry (LC-MS) data analysis widely used in chemical synthesis. As hundreds of thousands of syntheses are performed annually at Enamine, a large amount of experimental data has been generated internally. In this paper, we present the development of an RT prediction model based on the GATv2Conv + DL graph neural network (NN) architecture, trained on the internal data and further evaluated using the METLIN SMRT data set. The final model achieved a mean absolute error (MAE) of 2.48 s for the 120 s LC-MS method. We also conducted a detailed analysis of RT prediction errors and determined that the interval between RT - 7.12 s and RT + 9.58 s contained over 95% of the data. The developed model has been successfully integrated into the existing in-house LC-MS analysis toolkit, enhancing its predictive and analytical capabilities. Additionally, we have published a curated subset of 20,000 data points from our internal data set to support community benchmarking and further research.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7415-7425"},"PeriodicalIF":5.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey. 新药物设计的生成式深度学习──化学太空漫游。

IF 5.3 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-28 Epub Date: 2025-07-09 DOI: 10.1021/acs.jcim.5c00641

Rıza Özçelik, Helena Brinkmann, Emanuele Criscuolo, Francesca Grisoni

引用次数: 0

QComp: A QSAR-Based Imputation Framework for Drug Discovery. QComp：一个基于qsar的药物发现归算框架。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-27 DOI: 10.1021/acs.jcim.5c00059

Bingjia Yang,Yunsie Chung,Archer Y Yang,Bo Yuan,Tianchi Chen,Xiang Yu

引用次数: 0