Artificial intelligence chemistry最新文献_第4页

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments 评估基于局部原子环境加速预测密度泛函理论计算的 19F 化学位移的机器学习模型

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-10-17 DOI: 10.1016/j.aichem.2024.100078

Sophia Li , Emma Wang , Leia Pei , Sourodeep Deb , Prashanth Prabhala , Sai Hruday Reddy Nara , Raina Panda , Shiven Eltepu , Marx Akl , Larry McMahan , Edward Njoo

{"title":"Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments","authors":"Sophia Li , Emma Wang , Leia Pei , Sourodeep Deb , Prashanth Prabhala , Sai Hruday Reddy Nara , Raina Panda , Shiven Eltepu , Marx Akl , Larry McMahan , Edward Njoo","doi":"10.1016/j.aichem.2024.100078","DOIUrl":"10.1016/j.aichem.2024.100078","url":null,"abstract":"<div><div>The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. <sup>19</sup>F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT). However, the screening of compounds and discovery of feasible drug candidates is limited due to its computational cost. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated <sup>19</sup>F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional environments, representing their neighboring atoms within a radius of <em>n</em> Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. Among the models, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 3.31 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while reducing computational time from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments.</div></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535587","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

Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives 利用图神经网络预测苯甲酸衍生物的哈米特常数

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-10-16 DOI: 10.1016/j.aichem.2024.100079

Vaneet Saini , Ranjeet Kumar

引用次数: 0

Machine learning approaches for modelling of molecular polarizability in gold nanoclusters 金纳米团簇分子极化性建模的机器学习方法

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-11-07 DOI: 10.1016/j.aichem.2024.100080

Abhishek Ojha , Satya S. Bulusu , Arup Banerjee

引用次数: 0

A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds 利用量子机器学习研究喹喔啉化合物缓蚀作用的综合方法

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-07-10 DOI: 10.1016/j.aichem.2024.100073

Muhamad Akrom , Supriadi Rustad , Hermawan Kresno Dipojono , Ryo Maezono

{"title":"A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds","authors":"Muhamad Akrom , Supriadi Rustad , Hermawan Kresno Dipojono , Ryo Maezono","doi":"10.1016/j.aichem.2024.100073","DOIUrl":"10.1016/j.aichem.2024.100073","url":null,"abstract":"<div><p>In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R<sup>2</sup>) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid <strong>(Q1)</strong>, methyl quinoxaline-6-carboxylate <strong>(Q2)</strong>, (2<em>E</em>,3<em>E</em>)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline <strong>(Q3)</strong>, (2<em>E</em>,3<em>E</em>) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline <strong>(Q4)</strong>, (<em>E</em>)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one <strong>(Q5)</strong>, and 2-(4-methoxyphenyl)-7-methylthieno[3,2-<em>b</em>] quinoxaline <strong>(Q6)</strong>, show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000319/pdfft?md5=a7bac287a83d3748d5afc1de5b5f817c&pid=1-s2.0-S2949747724000319-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630521","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

Large-language models: The game-changers for materials science research 大型语言模型：改变材料科学研究的游戏规则

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-08-24 DOI: 10.1016/j.aichem.2024.100076

Songlin Yu , Nian Ran , Jianjun Liu

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Conf-GEM: A geometric information-assisted direct conformation generation model Conf-GEM：几何信息辅助直接构象生成模型

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-07-27 DOI: 10.1016/j.aichem.2024.100074

Zhijiang Yang , Youjun Xu , Li Pan , Tengxin Huang , Yunfan Wang , Junjie Ding , Liangliang Wang , Junhua Xiao

{"title":"Conf-GEM: A geometric information-assisted direct conformation generation model","authors":"Zhijiang Yang , Youjun Xu , Li Pan , Tengxin Huang , Yunfan Wang , Junjie Ding , Liangliang Wang , Junhua Xiao","doi":"10.1016/j.aichem.2024.100074","DOIUrl":"10.1016/j.aichem.2024.100074","url":null,"abstract":"<div><p>Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks such as molecular property prediction, molecular generation, and molecular docking. Existing MCG methods mostly rely on indirect distance-based strategies, which which can result in geometrically unrealistic conformations, or direct coordinate-based methods, which have larger search spaces and are prone to overfitting. Therefore, this study introduces Conf-GEM, a novel geometric information-assisted direct conformation generation model based on E-GeoGNN, a geometrically augmented 3D graph neural network with multiple scales. Pre-training and divide-and-conquer strategies, are integrated into the proposed model. Conf-GEM outperforms RDKit and nine deep-learning-based MCG models on the GEOM-QM9 and GEOM-Drugs datasets, achieving conformational coverage of 96.69% and 96.07%, respectively, without force field optimization. It also excels on the X-ray diffraction crystal structure dataset with up to 97.04% conformational coverage. In conclusion, Conf-GEM provides a novel solution for stabilizing 3D conformations generation. We provide an online prediction service (<span><span>https://confgem.cmdrg.com</span><svg><path></path></svg></span>) with a user-friendly interface for researchers.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000320/pdfft?md5=48affbdd2252ef50c6eb12dedcdeacc7&pid=1-s2.0-S2949747724000320-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845096","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

Molecular similarity: Theory, applications, and perspectives 分子相似性：理论、应用与展望

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-08-31 DOI: 10.1016/j.aichem.2024.100077

Kenneth López-Pérez , Juan F. Avellaneda-Tamayo , Lexin Chen , Edgar López-López , K. Eurídice Juárez-Mercado , José L. Medina-Franco , Ramón Alain Miranda-Quintana

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User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals 面向药物化学家和制药业的用户友好型工业集成人工智能

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-07-14 DOI: 10.1016/j.aichem.2024.100072

Olga Kapustina , Polina Burmakina , Nina Gubina , Nikita Serov , Vladimir Vinogradov

{"title":"User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals","authors":"Olga Kapustina , Polina Burmakina , Nina Gubina , Nikita Serov , Vladimir Vinogradov","doi":"10.1016/j.aichem.2024.100072","DOIUrl":"10.1016/j.aichem.2024.100072","url":null,"abstract":"<div><p>Artificial intelligence has brought crucial changes to the whole field of natural sciences. Myriads of machine learning algorithms have been developed to facilitate the work of experimental scientists. Molecular property prediction and drug synthesis planning become routine tasks. Moreover, inverse design of compounds with tunable properties as well as on-the-fly autonomous process optimization and chemical space exploration became possible <em>in silico</em>. Affordable robotic platforms exist able to perform thousands of experiments every day, analyzing the results and tuning the protocols. Despite this, most of these developments get trapped at the stage of code or overlooked, limiting their use by experimental scientists. Meanwhile, visibility and the number of user-friendly tools and technologies available to date is too low to compensate for this fact, rendering the development of novel therapeutic compounds inefficient. In this Review, we set the goal to bridge the gap between modern technologies and experimental scientists to improve drug development efficacy. Here we survey advanced and easy-to-use technologies able to help medical chemists at every stage of their research, including those integrated in technological processes during COVID-19 pandemic motivated by the need for fast yet precise solutions. Moreover, we review how these technologies are integrated by industry and clinics to streamline drug development and production. These technologies already transform the current paradigm of scientific thinking and revolutionize not only medicinal chemistry, but the whole field of natural sciences.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000307/pdfft?md5=6e0dd6833ec368337f7792d55171a0b8&pid=1-s2.0-S2949747724000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691742","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}

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Top 20 influential AI-based technologies in chemistry 化学领域最具影响力的 20 项人工智能技术

Artificial intelligence chemistry Pub Date : 2024-12-01 Epub Date: 2024-07-27 DOI: 10.1016/j.aichem.2024.100075

Valentine P. Ananikov

{"title":"Top 20 influential AI-based technologies in chemistry","authors":"Valentine P. Ananikov","doi":"10.1016/j.aichem.2024.100075","DOIUrl":"10.1016/j.aichem.2024.100075","url":null,"abstract":"<div><p>The beginning and ripening of digital chemistry is analyzed focusing on the role of artificial intelligence (AI) in an expected leap in chemical sciences to bring this area to the next evolutionary level. The analytic description selects and highlights the top 20 AI-based technologies and 7 broader themes that are reshaping the field. It underscores the integration of digital tools such as machine learning, big data, digital twins, the Internet of Things (IoT), robotic platforms, smart control of chemical processes, virtual reality and blockchain, among many others, in enhancing research methods, educational approaches, and industrial practices in chemistry. The significance of this study lies in its focused overview of how these digital innovations foster a more efficient, sustainable, and innovative future in chemical sciences. This article not only illustrates the transformative impact of these technologies but also draws new pathways in chemistry, offering a broad appeal to researchers, educators, and industry professionals to embrace these advancements for addressing contemporary challenges in the field.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000332/pdfft?md5=a101cdd9b75aa2e13939289fee50e2d5&pid=1-s2.0-S2949747724000332-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849804","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}

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Advances in Artificial Intelligence (AI)-assisted approaches in drug screening 人工智能（AI）辅助药物筛选方法的进展

Artificial intelligence chemistry Pub Date : 2024-06-01 Epub Date: 2023-12-19 DOI: 10.1016/j.aichem.2023.100039

Samvedna Singh , Himanshi Gupta , Priyanshu Sharma, Shakti Sahi

{"title":"Advances in Artificial Intelligence (AI)-assisted approaches in drug screening","authors":"Samvedna Singh , Himanshi Gupta , Priyanshu Sharma, Shakti Sahi","doi":"10.1016/j.aichem.2023.100039","DOIUrl":"10.1016/j.aichem.2023.100039","url":null,"abstract":"<div><p>Artificial intelligence (AI) is revolutionizing the current process of drug design and development, addressing the challenges encountered in its various stages. By utilizing AI, the efficiency of the process is significantly improved through enhanced precision, reduced time and cost, high-performance algorithms and AI-enabled computer-aided drug design (CADD). Effective drug screening techniques are crucial for identifying potential hit compounds from large volumes of data in compound repositories. The inclusion of AI in drug discovery, including the screening of hit compounds and lead molecules, has proven to be more effective than traditional in vitro screening assays. This article reviews the advancements in drug screening methods achieved through AI-enhanced applications, machine learning (ML), and deep learning (DL) algorithms. It specifically focuses on AI applications in the drug discovery phase, exploring screening strategies and lead optimization techniques such as Quantitative structure-activity relationship (QSAR) modeling, pharmacophore modeling, de novo drug designing, and high-throughput virtual screening. Valuable insights into different aspects of the drug screening process are discussed, highlighting the role of AI-based tools, pipelines, and case studies in simplifying the complexities associated with drug discovery.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100039"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747723000398/pdfft?md5=559cf38dcee28e753d9d412a799e3406&pid=1-s2.0-S2949747723000398-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139017387","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