Chemometrics and Intelligent Laboratory Systems最新文献_第8页

A review of quantitative structure-activity relationship: The development and current status of data sets, molecular descriptors and mathematical models 定量结构-活性关系综述：数据集、分子描述符和数学模型的发展与现状

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-21 DOI: 10.1016/j.chemolab.2024.105278

Jianmin Li , Tian Zhao , Qin Yang , Shijie Du , Lu Xu

{"title":"A review of quantitative structure-activity relationship: The development and current status of data sets, molecular descriptors and mathematical models","authors":"Jianmin Li , Tian Zhao , Qin Yang , Shijie Du , Lu Xu","doi":"10.1016/j.chemolab.2024.105278","DOIUrl":"10.1016/j.chemolab.2024.105278","url":null,"abstract":"<div><div>Developing Quantitative Structure-Activity Relationship (QSAR) models applicable to general molecules is of great significance for molecular design in many disciplines. This paper reviews the development and current status of molecular QSAR research, including datasets, molecular descriptors, and mathematical models. A representative bibliometric analysis reveals the evolutionary trends in this field in the past decade. Based on the discussion of the advantages and shortcomings of existing methods, the requirements and possible approaches for developing a widely applicable QSAR model were put forward. This goal poses a series of challenges to QSAR, including: (1) Having a sufficient number of structure-activity relationship instances as training data to cope with the complexity and diversity of molecular structures and action mechanisms; (2) Developing and using precise molecular descriptors to avoid the situation of ‘garbage in, garbage out’, while balancing descriptor dimensions and computational costs; and (3) Using powerful and flexible mathematical models, such as deep learning models, to learn complex functional relationships between descriptors and activity. With the emergence of larger and higher-quality data sets, more accurate molecular descriptors and deep learning methods, predictive ability, interpretability and application domain of QSAR models will continue to improve, and it will play a more important role in various fields of molecular design.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105278"},"PeriodicalIF":3.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719740","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

VAE-SIMCA — Data-driven method for building one class classifiers with variational autoencoders VAE-SIMCA - 利用变异自动编码器构建一类分类器的数据驱动方法

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-19 DOI: 10.1016/j.chemolab.2024.105276

Akam Petersen, Sergey Kucheryavskiy

{"title":"VAE-SIMCA — Data-driven method for building one class classifiers with variational autoencoders","authors":"Akam Petersen, Sergey Kucheryavskiy","doi":"10.1016/j.chemolab.2024.105276","DOIUrl":"10.1016/j.chemolab.2024.105276","url":null,"abstract":"<div><div>The paper proposes a new method for building one class classifiers based on variational autoencoders (VAE). The classification decision is built on a linear combination of two squared distances: computed for the original and the reconstructed image as well as for the representation of the original image inside the latent space formed by VAE. Because both distances are well approximated by scaled chi-square distribution, the decision boundary is computed using the theoretical quantile function for this distribution and the predefined probability for Type I error, ⍺. Thereby the boundary does not require any specific optimization and is solely based on the model outcomes computed for the training set.</div><div>The original idea of the proposed method is inherited from another OCC approach, Data Driven Soft Independent Method for Class Analogies, where singular value decomposition is employed for building the latent space. In this paper we show how this idea can be adopted to be used with VAE for detection of anomalies on images. The paper describes the theoretical background, introduces the main outcomes as well as tools for visual exploration of the classification results, and shows how the method works on several simulated and real datasets.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105276"},"PeriodicalIF":3.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Studies on QSAR models for the anti-virus effect of oseltamivir derivatives targeting H5N1 based on Mix-Kernel support vector machine

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-13 DOI: 10.1016/j.chemolab.2024.105273

Xianquan Wang , Fengjing Nie , Zhan Gao , Guoliang Li , Dengshuai Zhang , Jinfeng Zhang , Peijian Zhang

{"title":"Studies on QSAR models for the anti-virus effect of oseltamivir derivatives targeting H5N1 based on Mix-Kernel support vector machine","authors":"Xianquan Wang , Fengjing Nie , Zhan Gao , Guoliang Li , Dengshuai Zhang , Jinfeng Zhang , Peijian Zhang","doi":"10.1016/j.chemolab.2024.105273","DOIUrl":"10.1016/j.chemolab.2024.105273","url":null,"abstract":"<div><div>To assist in design and synthesis of novel oseltamivir derivatives as to help yield more inhibitors with lower time and cost, four quantitative structure-activity relationship (QSAR) models on 83 ODs with methods of random forest (RF), extreme gradient boosting (XGBoost), radial basis kernel function support vector machine (RBF-SVM) and mix kernel function support vector machine (MIX-SVM) were established. In the study, five descriptors of the highest importance were identified by RF. To further examine the robustness and stability of the four models, leave-one-out cross validation was adapted to the models and R, Rwere used to measure the performance. The model built by MIX-SVM performed best on the dataset: R<sup>2</sup> on training set and test set were 0.963 and 0.961, mean squared error (MSE) on training set and test set were 0.055 and 0.054, and R, R were 0.918 and 0.889, respectively. Furthermore, five important descriptors were selected for analysis, and the modelling method with the application of MIX-SVM achieved great prediction performance, which could facilitate the design and synthesis of oseltamivir derivatives.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"261 ","pages":"Article 105273"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761165","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

Sound uncertainty-based strategy for oil spill source identification 基于不确定性的健全溢油源识别战略

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-13 DOI: 10.1016/j.chemolab.2024.105275

Ana Catarina Rocha , Carla Palma , Ricardo J.N. Bettencourt da Silva

{"title":"Sound uncertainty-based strategy for oil spill source identification","authors":"Ana Catarina Rocha , Carla Palma , Ricardo J.N. Bettencourt da Silva","doi":"10.1016/j.chemolab.2024.105275","DOIUrl":"10.1016/j.chemolab.2024.105275","url":null,"abstract":"<div><div>Oil spills are frequent and a major environmental threat, whether they are massive or small. Therefore, authorities and experts have developed analytical chemistry tools to identify spill sources and address these illegal acts by comparing oil patterns obtained by Gas Chromatography-Mass Spectrometry analysis of the spill (Sp) and suspected sources (SS) samples. Several methodologies have proposed different balances between data processing complexity and reliability. Supported by the accessibility and validity of Microsoft Excel spreadsheets, an alternative, accurate, and user-friendly tool was developed for spill source identification based on Monte Carlo Method (MCM) simulation of correlated oil components expressed by abundance ratio (<em>DR</em>). However, the statistical control of various <em>DR</em> and the degree of similarity of samples' compositions, at defined confidence levels, impact the probability of true and false composition equivalence claim of Sp and SS becoming a challenge to recognise the offender. This work not only compares the MCM and the conventional approaches allowing to highlight the limitations that result in evidence with greater uncertainty, but also offers a statistically sound strategy that manages the probabilities of a compositional equivalence claim assessing the ability to distinguish competing spill sources and reporting the most likely polluting source with reduced uncertainty. A decision chart proposed, based on objective and statistically sound criteria, indicates the performance of consecutive <em>DR</em> comparison trials if necessary. The target values established for the probability of compositional equivalence claim of the Sp and the first and second most likely SS (≥95.0 % and ≤0.50 %, respectively) provide to forensic experts’ sound evidence to be presented in court (likelihood ratio ≥190). This work represents a significant breakthrough in comparing complex chemical oil patterns.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105275"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719851","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

A flame image soft sensor for oxygen content prediction based on denoising diffusion probabilistic model 基于去噪扩散概率模型的氧气含量预测火焰图像软传感器

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-12 DOI: 10.1016/j.chemolab.2024.105269

Yi Liu , Angpeng Liu , Shuang Gao

引用次数: 0

Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking 灵芝中潜在抗肿瘤成分的预测：利用机器学习和分子对接的综合方法

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-08 DOI: 10.1016/j.chemolab.2024.105271

Qi Yang , Lihao Yao , Fang Jia , Guiyuan Pang , Meiyu Huang , Chengxiang Liu , Hua Luo , Lili Fan

{"title":"Prediction of potential antitumor components in Ganoderma lucidum: A combined approach using machine learning and molecular docking","authors":"Qi Yang , Lihao Yao , Fang Jia , Guiyuan Pang , Meiyu Huang , Chengxiang Liu , Hua Luo , Lili Fan","doi":"10.1016/j.chemolab.2024.105271","DOIUrl":"10.1016/j.chemolab.2024.105271","url":null,"abstract":"<div><div>The objective of this study is to develop a reliable predictive model for antitumour activity and to identify potential antitumour components in <em>Ganoderma lucidum</em>. Four machine learning models, including Random Forest, were employed to train predictive models for antitumour activity, utilising Morgan fingerprints as molecular descriptors. The most effective model was then employed to predict the chemical composition of <em>Ganoderma lucidum</em>, identifying the four most probable compounds for molecular docking with known TNF-α-related targets. The findings of the study indicate that a Support Vector Machine (SVM) model exhibits an accuracy, F1 score, AUC, and sensitivity of 0.7638, 0.7638, 0.8332, and 0.7621, respectively. The model demonstrated an 80 % accuracy rate in predicting the antitumour activity of 10 FDA-approved drugs. Besides, the model identified 11 components in <em>Ganoderma lucidum</em>, including 3-nitroanisole, with a probability of antitumour activity exceeding 0.5, indicating their potential as antitumour agents. The results of the molecular docking procedure indicated that the four most promising antitumour compounds derived from <em>Ganoderma lucidum</em> exhibited a favourable binding affinity with the TNF-α target. In conclusion, this study incorporated a machine learning prediction step prior to molecular docking, thereby enhancing the reliability of the latter. Furthermore, it identified previously unreported compounds in <em>Ganoderma lucidum</em> with potential antitumour activity, such as 3-nitroanisole.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105271"},"PeriodicalIF":3.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652927","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

Spectra data calibration based on deep residual modeling of independent component regression 基于独立分量回归深度残差建模的光谱数据校准

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-05 DOI: 10.1016/j.chemolab.2024.105270

Junhua Zheng , Zeyu Yang , Zhiqiang Ge

引用次数: 0

Enhanced CO2 leak detection in soil: High-fidelity digital colorimetry with machine learning and ACES AP0 增强型土壤二氧化碳泄漏检测：高保真数字比色法与机器学习和 ACES AP0

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-02 DOI: 10.1016/j.chemolab.2024.105268

Chairul Ichsan , Navinda Ramadhan , Komang Gede Yudi Arsana , M. Mahfudz Fauzi Syamsuri , Rohmatullaili

{"title":"Enhanced CO2 leak detection in soil: High-fidelity digital colorimetry with machine learning and ACES AP0","authors":"Chairul Ichsan , Navinda Ramadhan , Komang Gede Yudi Arsana , M. Mahfudz Fauzi Syamsuri , Rohmatullaili","doi":"10.1016/j.chemolab.2024.105268","DOIUrl":"10.1016/j.chemolab.2024.105268","url":null,"abstract":"<div><div>The importance of effective carbon capture and storage (CCS) in addressing climate change issues highlights the need for robust CO<sub>2</sub> leak monitoring systems. Limitations of conventional methods have prompted interest in alternative approaches, such as optical CO<sub>2</sub> sensors, which offer non-invasive and continuous monitoring. Here, we present a novel methodology for high-fidelity digital colorimetry to enhance CO<sub>2</sub> leak detection in soil, integrating machine learning algorithms with the ACES AP0 color space. Optical CO<sub>2</sub> sensors, utilizing a cresol red-based detection solution, were calibrated and validated in a controlled environment chamber designed to simulate CO<sub>2</sub> leakage. Digital images of the sensor's colorimetric response to varying CO<sub>2</sub> levels were analyzed in five color spaces. The ACES AP0 color space, renowned for its expansive color gamut and perceptual uniformity, exhibited optimal performance in discerning subtle color variations induced by changes in CO<sub>2</sub> concentration. Ten machine learning regression models were evaluated, and Multivariate Polynomial Regression (MPR) emerged as the most effective in converting ACES AP0 color data into precise CO<sub>2</sub> concentration estimates, achieving a Mean Absolute Percentage Error (MAPE) of 2.9 % and a Root Mean Square Error (RMSE) of 0.0731. Field validation at a carbon capture and storage (CCS) facility corroborated the robustness and accuracy of this method, showcasing its potential for real-world applications in CCS and environmental monitoring.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105268"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571294","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

Quantitative structure properties relationship (QSPR) analysis for physicochemical properties of nonsteroidal anti-inflammatory drugs (NSAIDs) usingVe degree-based reducible topological indices 利用基于Ve度的可还原拓扑指数对非甾体抗炎药（NSAIDs）的理化性质进行定量结构特性关系（QSPR）分析

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-11-02 DOI: 10.1016/j.chemolab.2024.105266

Rong Fan , Abdul Rauf , Manal Elzain Mohamed Abdalla , Arif Nazir , Muhammad Faisal , Adnan Aslam

{"title":"Quantitative structure properties relationship (QSPR) analysis for physicochemical properties of nonsteroidal anti-inflammatory drugs (NSAIDs) usingVe degree-based reducible topological indices","authors":"Rong Fan , Abdul Rauf , Manal Elzain Mohamed Abdalla , Arif Nazir , Muhammad Faisal , Adnan Aslam","doi":"10.1016/j.chemolab.2024.105266","DOIUrl":"10.1016/j.chemolab.2024.105266","url":null,"abstract":"<div><div>Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are a class of medications that are used for different therapeutic uses. They effectively alleviate pain, reduce inflammation, and manage fever. These drugs are available in various forms. NSAIDs are prescribed by healthcare professionals to address a wide range of symptoms, from headaches and dental pain to conditions like arthritis and muscle stiffness. In this work, we use ve-degree-based reducible topological descriptors in quantitative structure-property relationship (QSPR) analysis to estimate the physicochemical properties of NSAIDs. In the first step, we have developed a MAPLE-based code to compute the reducible ve-degree-based topological descriptors of NSAIDs. Then, a linear regression model was used to estimate four physicochemical properties of seventy NSAIDs. It has been observed that two physicochemical properties, namely Molecular Weight and Complexity show a very strong correlation with the reducible ve-degree-based topological descriptors. For both cases, the value of correlation coefficient is greater than 0.9. Finally, quadratic and cubic regression models were constructed, and a comparative analysis with these models is presented. These results may help enhance the understanding of NSAIDs medication structures and aid in predicting their pharmacological activity.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105266"},"PeriodicalIF":3.7,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578433","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

On-the-fly spectral unmixing based on Kalman filtering 基于卡尔曼滤波技术的即时光谱非混频技术

IF 3.7 2区化学

Chemometrics and Intelligent Laboratory Systems Pub Date : 2024-10-30 DOI: 10.1016/j.chemolab.2024.105252

Hugues Kouakou , José Henrique de Morais Goulart , Raffaele Vitale , Thomas Oberlin , David Rousseau , Cyril Ruckebusch , Nicolas Dobigeon

{"title":"On-the-fly spectral unmixing based on Kalman filtering","authors":"Hugues Kouakou , José Henrique de Morais Goulart , Raffaele Vitale , Thomas Oberlin , David Rousseau , Cyril Ruckebusch , Nicolas Dobigeon","doi":"10.1016/j.chemolab.2024.105252","DOIUrl":"10.1016/j.chemolab.2024.105252","url":null,"abstract":"<div><div>This work introduces an on-the-fly (i.e., online) linear spectral unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace to lighten the computational burden of the overall unmixing procedure. Experimental results obtained on synthetic and real Raman data sets show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. The proposed method constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples. The code associated with the numerical illustrations reported in this paper is freely available online at <span><span>https://github.com/HKouakou/KF-OSU</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"255 ","pages":"Article 105252"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0