Journal of Chemical Information and Modeling 最新文献_第8页

IF 5.6 2区化学

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

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IF 5.6 2区化学

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

Dinesh Kumar Jagannathareddy, and , Durba Roy*,

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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*,

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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*,

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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*,

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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

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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

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Integrating Chemo- and Bioinformatics with In Vitro Biological Assays to Discover Potential ACE2 and Mpro Inhibitors against SARS-CoV-2. 将化学和生物信息学与体外生物测定相结合，发现抗SARS-CoV-2的潜在ACE2和Mpro抑制剂

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-25 DOI: 10.1021/acs.jcim.5c01056

Ryan S Ramos,João S N de Souza,Mariana H Chaves,Joaquín M Campos,Willyenne M Dantas,Lindomar J Pena,Maracy L D S Andrade,Cleydson B R Santos

{"title":"Integrating Chemo- and Bioinformatics with In Vitro Biological Assays to Discover Potential ACE2 and Mpro Inhibitors against SARS-CoV-2.","authors":"Ryan S Ramos,João S N de Souza,Mariana H Chaves,Joaquín M Campos,Willyenne M Dantas,Lindomar J Pena,Maracy L D S Andrade,Cleydson B R Santos","doi":"10.1021/acs.jcim.5c01056","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01056","url":null,"abstract":"The study aims to identify potential SARS-CoV-2 inhibitors and investigate the mechanism of action on the viral ACE2 receptor and main protease (Mpro), using chemo- and bioinformatics approaches. Ligand-based virtual screening was performed in the Molport database (∼4.79 million compounds), and after applying physicochemical filters, 313 molecules with characteristics such as hydroxychloroquine were obtained. After obtaining bioactive conformations, the molecular structures were subjected to the study of pharmacokinetic predictions, in which 106 molecules presented properties for oral bioavailability, penetration of the BBB, PPB, and solubility (average). The toxicological property predictions proved plausible for the molecules, as they did not present warnings of hepatotoxicity, mutagenicity, potential risk of carcinogenicity, and LC50 and LD50 values higher than the controls. Subsequently, 81 structures were subjected to a molecular docking study of ACE2 receptor/Spike and Mpro. In the ACE2 receptor, four (4) ligands showed high binding affinity value, in which the molecule MolPort-010-778-422 had the best ΔG value of -9.414 kcal/mol, followed by MolPort-009-093-282 with ΔG = -8.978 kcal/mol. In the Mpro receptor, four (4) ligands showed high binding affinity values compared to control 11b, with emphasis on molecule MolPort-005-766-143 with ΔG = -8.829 kcal/mol, followed by molecule MolPort-046-186-743. To study the antiviral effects of the molecules in vitro, TopHits8 molecules were tested against the SARS-CoV-2 virus. MolPort-010-778-422 had the best result on the screening and presented an IC50 of 8.9 nM.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701378","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}

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Machine Learning-Driven Prediction of Electrochemical Promotion in the Reverse Water Gas Shift Reaction. 机器学习驱动的水气倒转反应电化学促进预测。

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2025-07-25 DOI: 10.1021/acs.jcim.5c00927

Ju Wang,Hongying Zhou,Mustapha Ezzeddine,Karim Harb,Sayed Ahmed Ebrahim,Elena A Baranova

{"title":"Machine Learning-Driven Prediction of Electrochemical Promotion in the Reverse Water Gas Shift Reaction.","authors":"Ju Wang,Hongying Zhou,Mustapha Ezzeddine,Karim Harb,Sayed Ahmed Ebrahim,Elena A Baranova","doi":"10.1021/acs.jcim.5c00927","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00927","url":null,"abstract":"Electrochemical promotion of catalysis (EPOC) provides an effective and versatile strategy to enhance catalytic activity, selectivity, and stability in the reverse water-gas shift (RWGS) reaction, facilitating efficient CO2 hydrogenation to syngas under milder conditions. However, predicting EPOC results using novel catalytic materials under diverse conditions remains challenging. This study introduces a machine learning framework to predict electrochemical promotion behavior and the rate enhancement ratios (ρ), i.e., ratio between promoted and unpromoted reaction rate, based on the selected catalyst, reaction, and electrochemical condition descriptors. Several classification and regression models were trained and tested using a data set compiled from previous studies. The best-performing random forest (RF) and extreme gradient boosting (XGB) models were validated with new experimental data collected from systems employing lithium lanthanum titanate (LLTO) solid electrolyte and Pt-ZnO catalysts, achieving an R2 of 0.97 and a mean squared error (MSE) of 0.01. This data-driven approach is interpretable, generalizable to other catalytic systems, and provides a powerful tool for advancing the development of catalytic materials for EPOC in RWGS reactions.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"25 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701376","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