Artificial intelligence chemistry最新文献_第8页

Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces 耦合表示的阶数作为高维空间中稀疏数据机器学习的通用框架

Artificial intelligence chemistry Pub Date : 2023-07-17 DOI: 10.1016/j.aichem.2023.100008

Sergei Manzhos , Tucker Carrington , Manabu Ihara

{"title":"Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces","authors":"Sergei Manzhos , Tucker Carrington , Manabu Ihara","doi":"10.1016/j.aichem.2023.100008","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100008","url":null,"abstract":"<div><p>Machine learning (ML) techniques are already widely and increasingly used in diverse applications in science and technology, including computational chemistry. Specifically in computational chemistry, neural networks (NN) and kernel methods such as Gaussian process regressions (GPR) have been increasingly used for the construction of potential functions and functionals for density functional theory. While ML techniques have a number of advantages vs intuition-based models, notably their generality and black-box nature, they are still challenged when faced with high dimensionality of the feature space or low and uneven data density – in part because of their general nature. We review recent works using methods such as NNs and GPR as building blocks of composite methods in the framework of an expansion over orders of coupling. We introduce models using NN or GPR-based components as part of HDMR (high-dimensional model representations)-based structures. HDMR is a formalization of orders-of-coupling representations that include the many-body and N-mode representations well known in computational chemistry and allows, in particular, building all terms from one dataset of arbitrarily distributed data. The resulting HDMR-NN and HDMR-GPR combinations and NN with HDMR-GPR derived neuron activation functions not requiring non-linear optimization enhance machine learning capabilities in high dimensional spaces and or with sparse data.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49764054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

How do centrality measures help to predict similarity patterns in molecular chemical structural graphs? 中心性测量如何帮助预测分子化学结构图中的相似模式?

Artificial intelligence chemistry Pub Date : 2023-07-13 DOI: 10.1016/j.aichem.2023.100007

Nirmala Parisutham

{"title":"How do centrality measures help to predict similarity patterns in molecular chemical structural graphs?","authors":"Nirmala Parisutham","doi":"10.1016/j.aichem.2023.100007","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100007","url":null,"abstract":"<div><p>The proposed work uses centrality measures based heuristic method to improve the efficiency of the solution for the similarity search problem in molecular chemical graphs by effectively identifying central candidate or representative candidate nodes, which simplify the complex processes involved while detecting a large-sized maximal common connected edge subgraph. After analyzing the structure of the two input molecular chemical graphs, a Tensor Product graph is created. This newly built graph is further analyzed to get the similarity pattern of the input graphs. It is an open problem to decide which centrality measure selects the best central candidate node in Tensor Product graphs to get a large maximal common connected edge graph. Since each centrality measure is analyses, the given graph is uniquely based on its own specific aspects. The proposed work offers directions on using various centrality measures to determine a big-sized maximal common connected subgraph for two molecular chemical input graphs. It also analyses seven centrality measures to select the best candidate node in the Tensor Product graph of two input chemical molecular graphs. Based on the obtained results, the betweenness centrality and degree centrality measures exclusively help to get large-sized similarity patterns.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49764053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ML meets MLn: Machine learning in ligand promoted homogeneous catalysis ML与MLn相遇:配体促进均相催化的机器学习

Artificial intelligence chemistry Pub Date : 2023-07-11 DOI: 10.1016/j.aichem.2023.100006

Jonathan D. Hirst , Samuel Boobier , Jennifer Coughlan , Jessica Streets , Philippa L. Jacob , Oska Pugh , Ender Özcan , Simon Woodward

引用次数: 2

Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction 评估神经网络在蛋白质配体结合预测中的点预测不确定性

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100004

Ya Ju Fan , Jonathan E. Allen , Kevin S. McLoughlin , Da Shi , Brian J. Bennion , Xiaohua Zhang , Felice C. Lightstone

{"title":"Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction","authors":"Ya Ju Fan , Jonathan E. Allen , Kevin S. McLoughlin , Da Shi , Brian J. Bennion , Xiaohua Zhang , Felice C. Lightstone","doi":"10.1016/j.aichem.2023.100004","DOIUrl":"10.1016/j.aichem.2023.100004","url":null,"abstract":"<div><p>Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9f/25/nihms-1912151.PMC10426331.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10019861","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

Reproducing the color with reformulated recipe 用重新配方再现颜色

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100003

Jinming Fan , Chao Qian , Shaodong Zhou

引用次数: 0

Machine learning modeling of the absorption properties of azobenzene molecules 偶氮苯分子吸收特性的机器学习建模

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100002

Valentin Stanev , Ryota Maehashi , Yoshimi Ohta , Ichiro Takeuchi

引用次数: 0

Starting the new journal of “Artificial Intelligence Chemistry” 创办新期刊《人工智能化学》

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100001

Jun Jiang , Song Wang , Shaul Mukamel

引用次数: 0

Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study 在钯催化的C-N偶联中，三苯基膦在双芳基骨架上的高效性的来源：DFT和机器学习的结合研究

Artificial intelligence chemistry Pub Date : 2023-06-01 DOI: 10.1016/j.aichem.2023.100005

Qingfu Ye , Yu Zhao , Jun Zhu

{"title":"Probing the origin of higher efficiency of terphenyl phosphine over the biaryl framework in Pd-catalyzed C-N coupling: A combined DFT and machine learning study","authors":"Qingfu Ye , Yu Zhao , Jun Zhu","doi":"10.1016/j.aichem.2023.100005","DOIUrl":"https://doi.org/10.1016/j.aichem.2023.100005","url":null,"abstract":"<div><p>The Pd-catalyzed Buchwald–Hartwig coupling reaction is important in the construction of the C-N bond due to various applications in organic synthesis. Quantum chemical calculations are widely used in understanding reaction mechanisms whereas the machine learning method is extremely popular in recognizing the relationships of data. Here, we combine density functional theory calculations with the support vector regression method to probe the origin of the higher efficiency of terphenyl phosphine ligand over the biaryl counterpart in the Buchwald–Hartwig C-N coupling reaction. By quantum chemical calculations, the turnover frequency-determining transition states are located and ligand features are calculated with high accuracy. By machine learning, the relationship between the reaction barrier and ligand features has been examined. It is found that the interplay of the charge on the metal center, the cone angle of the ligands, and the Sterimol L parameters of the ligand determines the catalytic performance of the palladium catalysts with different phosphine ligands. Our findings could help experimental chemists to design the ligands for Pd-catalyzed C-N coupling reactions with high efficiency.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49817020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0