Artificial intelligence chemistry最新文献

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

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

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 : 2024-08-24 DOI: 10.1016/j.aichem.2024.100076

引用次数: 0

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 : 2024-07-27 DOI: 10.1016/j.aichem.2024.100074

{"title":"Conf-GEM: A geometric information-assisted direct conformation generation model","authors":"","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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","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

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 : 2024-07-27 DOI: 10.1016/j.aichem.2024.100075

{"title":"Top 20 influential AI-based technologies in chemistry","authors":"","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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","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}

引用次数: 0

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 : 2024-07-14 DOI: 10.1016/j.aichem.2024.100072

{"title":"User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals","authors":"","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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","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}

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 : 2024-07-10 DOI: 10.1016/j.aichem.2024.100073

{"title":"A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds","authors":"","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":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","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

Artificial intelligence for drug repurposing against infectious diseases 人工智能为防治传染病重新设计药物用途

Artificial intelligence chemistry Pub Date : 2024-06-12 DOI: 10.1016/j.aichem.2024.100071

Anuradha Singh

{"title":"Artificial intelligence for drug repurposing against infectious diseases","authors":"Anuradha Singh","doi":"10.1016/j.aichem.2024.100071","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100071","url":null,"abstract":"<div><p>Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000290/pdfft?md5=061eb4d8766a2284afb22976a6d28b51&pid=1-s2.0-S2949747724000290-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322997","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

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models 人工智能时代的药物发现与开发：从机器学习到大型语言模型

Artificial intelligence chemistry Pub Date : 2024-05-09 DOI: 10.1016/j.aichem.2024.100070

Shenghui Guan , Guanyu Wang

{"title":"Drug discovery and development in the era of artificial intelligence: From machine learning to large language models","authors":"Shenghui Guan , Guanyu Wang","doi":"10.1016/j.aichem.2024.100070","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100070","url":null,"abstract":"<div><p>Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000289/pdfft?md5=3f360afc13a60f15f7dab8b6a1dd740b&pid=1-s2.0-S2949747724000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906077","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

Spatial-temporal self-attention network based on bayesian optimization for light olefins yields prediction in methanol-to-olefins process 基于贝叶斯优化的时空自我关注网络，用于预测甲醇制烯烃过程中的轻质烯烃产量

Artificial intelligence chemistry Pub Date : 2024-04-30 DOI: 10.1016/j.aichem.2024.100067

Jibin Zhou , Duiping Liu , Mao Ye , Zhongmin Liu

{"title":"Spatial-temporal self-attention network based on bayesian optimization for light olefins yields prediction in methanol-to-olefins process","authors":"Jibin Zhou , Duiping Liu , Mao Ye , Zhongmin Liu","doi":"10.1016/j.aichem.2024.100067","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100067","url":null,"abstract":"<div><p>Methanol-to-olefins (MTO), as an alternative pathway for the synthesis of light olefins (ethylene and propylene), has gained extensive attention. Accurate prediction of light olefins yields can effectively facilitate process monitoring and optimization, as they are significant economic indexes and stable operation indicators of the industrial MTO process. However, the nonlinearity and dynamic interactions among process variables pose challenges for the prediction using traditional statistical methods. Additionally, physical-based methods relying on first-principle theory are always limited by an insufficient understanding of reaction mechanisms. In contrast, data-driven methods offer a viable solution for the prediction based solely on process data without requiring extensive process knowledge. Therefore, in this work, a data-driven approach that integrates spatial and temporal self-attention modules is proposed to capture complex interactions. Furthermore, Bayesian optimization is employed to determine the optimum hyperparameters and enhance the accuracy of the model. Studies on an actual MTO process demonstrate the superior prediction performance of the proposed model compared to baseline models. Specifically, 24 process variables are selected as the high-dimensional inputs, and yields of ethylene and propylene, as the low-dimensional outputs, are successfully predicted at various prediction horizons ranging from 2 to 8 h.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000253/pdfft?md5=23aede3f145af7617d071f10a10c1e3f&pid=1-s2.0-S2949747724000253-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880534","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

Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions 通过机器学习确定二元溶液中水的活度和电解质的活度系数

Artificial intelligence chemistry Pub Date : 2024-04-27 DOI: 10.1016/j.aichem.2024.100069

Guillaume Zante

{"title":"Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions","authors":"Guillaume Zante","doi":"10.1016/j.aichem.2024.100069","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100069","url":null,"abstract":"<div><p>Activity of water and electrolytes in aqueous solutions is of utmost importance for multiple industrial applications. However, experimental determination of such values is time-consuming, while calculation of activity coefficients using numerical methods is challenging. By training neural networks models on literature data, one could predict activity of water and electrolytes easily, without requiring any experiment. In this paper, multiple descriptors (or features) are compared to predict activity coefficients of electrolytes and activity of water in electrolyte solutions. A neural network based on the Levenberg-Marquardt algorithm (LM-NN) showed high accuracy to calculate values, despite the small size of the training datasets. Both activity coefficients of electrolytes and activity of water in electrolyte solutions can be predicted accurately even on unseen data, using simple descriptors such as electrolyte concentration, ion sizes and charges. However, some discrepancies were observed due to the lack of representativeness of the training dataset. This could be solved by selecting training data sets that are similar (<em>e.g.</em> same group of the periodic table) to the unknown values, or by including available experimental data for the salt considered. The ability of the LM-NN to solve non-linear least square curve fitting problems makes it a good candidate to fit experimental activity coefficient data, with the advantage of simplicity as compared to e-NRTL or UNIQUAC methods. This method paves the way for accurate and quick determination of thermodynamic data for electrolyte solutions (and beyond) using machine learning, without necessitating large training datasets.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000277/pdfft?md5=e459d6b44dc65480007e2791c28b76ea&pid=1-s2.0-S2949747724000277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825119","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