Journal of Chemometrics最新文献

This computational study aimed to demonstrate distinct characteristics of alpha-D-mannopyranoside structure, leveraging D-mannose and its analogs due to their known roles in host–pathogen interactions and potential to be used as nutraceuticals. Targeting bacterial adhesion is a critical strategy to combat urinary tract infections (UTIs), especially given rising antibiotic resistance. The FimH lectin on Escherichia coli is a key mediator of this adhesion, making it a compelling target for novel anti-adhesive therapies. We employed a multi-stage virtual screening pipeline to efficiently explore a vast chemical space around the ligands and their binding interactions. Ligand-based virtual screening, utilizing self-organizing maps (SOMs), clustered 5256 D-mannose-similar structures, identifying a promising subset of 141 molecules with 39 known bioassay actives. This was followed by structure-based ligand docking to precisely evaluate their inhibitory impact on FimH lectin. To understand the structural features driving activity, principal component analysis (PCA) was then applied to analyze the molecular structures and their physicochemical descriptors. Our analysis revealed that 15 compounds exhibited the highest binding energy and docking scores. Crucially, the alpha-D-mannopyranoside conformation demonstrated the most effective inhibitory profile. This superior activity, despite structural similarities, was differentiated by two 3D-matrix descriptors: HRG and Wi G, highlighting their significance in predicting subtle yet impactful conformational preferences.

To address the issue of insufficient fault detection performance of global–local preserving projections (GLPP) in the detection of minor faults within nonlinear dynamic processes, a novel fault detection method based on GLPP and Kantorovich distance combined with a sliding window (GLPP-KD) is proposed. Firstly, the GLPP algorithm is used to construct a weight matrix to retain the key information of the data, and the objective function containing local and global information is transformed into a generalized eigenvector problem to obtain a projection matrix. Additionally, the sliding window technique integrated with the Kantorovich distance is employed to quantify the discrepancies between probability distributions, thereby capturing the local dynamic characteristics of the data. Eventually, the fault detection task is achieved by identifying the minor distinctions between normal and faulty states. Experimental results show that compared with traditional methods, GLPP-KD improves the fault detection accuracy and effectively reduces the false alarm rate. The proposed method provides a strong guarantee for the safe and stable operation of the industry and has high application value.

Immersive analytics is a developing field growing as technology improves. This paper presents some important points, but by no means is the discussion complete. The cited papers and books should be read to fully grasp the potential of the general field of immersive analytics. The direction of this paper is to highlight those components useful for chemometric data analyses in virtual reality.

This study demonstrates that integrating infrared spectroscopy with machine learning enables highly accurate, nondestructive classification of red-stamp ink manufacturers. We evaluated five classifiers—partial least squares discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and a feed-forward neural network (FNN)—across multiple spectral regions. The FNN trained on second-derivative spectra in the 1700–900 cm⁻¹ region achieved perfect test metrics (F1 = 1.000; AUC = 1.000), while PLS-DA and RF also performed robustly (F1 ≥ 0.933). Variable importance in projection (VIP) analysis identified the 1650–1100 cm⁻¹ subrange as most informative, streamlining feature selection and model training. Applied to three unknown samples, the optimized FNN produced high-confidence manufacturer predictions consistent with expected origins. These results confirm that targeted spectral selection combined with derivative preprocessing markedly enhances nondestructive ink classification for forensic applications.

The present study aims to find azole-containing acetylcholinesterase (AChE) inhibitors for the treatment of Alzheimer's disease (AD) through a mixed in silico approach. The first step involved the collection of azole derivatives and predictive quantitative structure–activity relationship (QSAR) model development for their AChE inhibition activity, using multiple linear regressions (MLRs) with the genetic algorithm (GA) for feature selection. The developed GA-MLR models were statistically robust enough internally (R²_adj = 0.643–0.640, Q²_LOO = 0.616–0.621) as well as externally (R²_pred = 0.626–0.658, R²M = 0.562–0.601). The prediction reliability of the models was assured through the leverage approach of the applicability domain. The most significant models were applied to azole-containing PubChem database compounds, which were classified as active and inactive based on theoretical predictions. The toxicity analysis was also performed for the active compounds by the online web server Protox-II. The less or nontoxic compounds were subjected to molecular docking, along with donepezil as a standard. Docking analysis revealed that the four compounds have better binding affinity (binding energy = −11.6 to −11.2 kcal/mol) as compared to donepezil (binding energy = −11 kcal/mol). Apart from binding energy, donepezil was observed to be toxic by the prediction from the Protox-II. Finally, molecular dynamics (MD) analysis of two compounds (Compound 5, having the lowest IC₅₀, and Compound 25, having the highest IC₅₀ among the top 4 docked compounds) not only differentiated them based on final interactions but also exhibited that the toxicity of donepezil might be due to hydrogen bonding with the active site.

A method is introduced to perform simultaneous sparse dimension reduction on two blocks of variables. Beyond dimension reduction, it also yields an estimator for multivariate regression with the capability to intrinsically deselect uninformative variables in both independent and dependent blocks. An algorithm is provided that leads to a straightforward implementation of the method. The benefits of simultaneous sparse dimension reduction are shown to carry through to enhanced capability to predict a set of multivariate dependent variables jointly. Both in a simulation study and in two chemometric applications, the new method outperforms its dense counterpart, as well as multivariate partial least squares.