jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Beatriz Galindo‐Prieto | Ian Mudway | Johan Linderholm | Paul Geladi
Source: Journal of Chemometrics (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Multi‐Block Chemometric Approaches to the Unsupervised Spectral Characterization of Geological Samples ABSTRACT As an example for the potential use of multi‐block chemometric methods to provide improved unsupervised characterization of compositionally complex materials through the integration of multi‐modal spectrometric data sets, we analysed spectral data derived from five field instruments (one XRF, two NIR, and two FT‐Raman), collected on 76 bedrock samples of diverse composition. These data were analysed by single‐ and multi‐ block latent variable models, based on principal component analysis (PCA) and partial least squares (PLS). For the single‐block approach, PCA and PLS models were generated; whilst hierarchical partial least squares (HPLS) regression was applied for the multi‐block modelling. We also tested whether dimensionality reduction resulted in a more computationally efficient muti‐block HPLS model with enhanced model interpretability and geological characterization power using the variable influence on projection (VIP) feature selection method. The results showed differences in the characterization power of the five spectrometer data sets for the bedrock samples based on their mineral composition and geological properties; moreover, some spectroscopic techniques under‐performed for distinguishing samples by composition. The multi‐block HPLS and its VIP‐strengthened model yielded a more complete unsupervised geological aggrupation of the samples in a single parsimonious model. We conclude that multi‐block HPLS models are effective at combining multi‐modal spectrometric data to provide a more comprehensive characterization of compositionally complex samples, and VIP can reduce HPLS model complexity, while increasing its data interpretability. These approaches have been applied here to a geological data set, but are amenable to a broad range of applications across chemical and biomedical disciplines. Chemometrics ; Characterization (materials science) ; Block (permutation group theory) ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Mathematics ; Machine learning ; Materials science ; Nanotechnology Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Metabolomics and Mass Spectrometry Studies
Authors: Wei Liu | Ziqin Zhang | Yang Liu | Longguang Jiang | Pao Li | Wei Fan
Source: Molecules (article, 2025-08-30)
Field: Chemistry — Analytical Chemistry

A Novel Pattern Recognition Method for Non-Destructive and Accurate Origin Identification of Food and Medicine Homologous Substances with Portable Near-Infrared Spectroscopy In this study, a novel pattern recognition method named boosting-partial least squares-discriminant analysis (Boosting-PLS-DA) was developed for the non-destructive and accurate origin identification of food and medicine homologous substances (FMHSs). Taking <i>Gastrodia elata</i>, <i>Aurantii Fructus Immaturus</i>, and <i>Angelica dahurica</i> as examples, spectra of FMHSs from different origins were obtained by portable near-infrared (NIR) spectroscopy without destroying the samples. The identification models were developed with Boosting-PLS-DA, compared with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) models. The model performances were evaluated using the validation set and an external validation set obtained one month later. The results showed that the Boosting-PLS-DA method can obtain the best results. For the analysis of <i>Aurantii Fructus Immaturus</i> and <i>Angelica dahurica</i>, 100% accuracies of the validation sets and external validation sets were obtained using Boosting-PLS-DA models. For the analysis of <i>Gastrodia elata</i>, Boosting-PLS-DA models showed significant improvements in external validation set accuracies compared to PLS-DA, reducing the risk of overfitting. Boosting-PLS-DA method combines the high robustness of ensemble learning with the strong discriminative capability of discriminant analysis. The generalizability will be further validated with a sufficiently large external validation set and more types of FMHSs. Identification (biology) ; Infrared spectroscopy ; Homologous chromosome ; Computational biology ; Spectroscopy ; Chemistry ; Materials science ; Biology ; Organic chemistry ; Biochemistry ; Physics ; Botany ; Gene Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Mena Abdelsayed | Yassir Boulaamane
Source: Biomedicines (article, 2025-10-24)
Field: Chemistry — Organic Chemistry

Drug Repurposing for AML: Structure-Based Virtual Screening and Molecular Simulations of FDA-Approved Compounds with Polypharmacological Potential <b>Background:</b> Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by impaired differentiation, apoptosis resistance, and metabolic reprogramming, which collectively contribute to therapeutic resistance and poor clinical outcomes. While targeted agents-such as LSD1 inhibitors, the BCL-2 inhibitor venetoclax, and IDH1 inhibitors-have provided clinical benefit, their efficacy is often limited by compensatory signaling and clonal evolution. This study aimed to identify FDA-approved compounds with multitarget potential to simultaneously modulate key epigenetic, apoptotic, and metabolic pathways in AML. <b>Methods:</b> Structure-based virtual screening of 3957 FDA-approved molecules was performed against three AML-relevant targets: lysine-specific demethylase 1 (LSD1), BCL-2, and mutant IDH1 (R132H). Top-ranked hits were evaluated using ADMET prediction and molecular dynamics (MD) simulations to assess pharmacokinetic properties, toxicity, and ligand-protein complex stability over 100 ns trajectories. <b>Results:</b> Three compounds-DB16703, DB08512, and DB16047-exhibited high binding affinities across all three targets with favorable pharmacokinetic and safety profiles. MD simulations confirmed the structural stability of the ligand-protein complexes, revealing persistent hydrogen bonding and minimal conformational deviation. These findings suggest that these repurposed drugs possess a promising multitarget profile capable of addressing AML's multifactorial pathophysiology. <b>Conclusions:</b> This computational study supports the feasibility of a polypharmacology-based strategy for AML therapy by integrating epigenetic modulation, apoptotic reactivation, and metabolic correction within single molecular scaffolds. However, the identified compounds (Belumosudil, DB08512, and Elraglusib) have not yet demonstrated efficacy in AML models; further preclinical validation is warranted to substantiate these predictions and advance translational development. Virtual screening ; Drug repositioning ; Myeloid leukemia ; Drug discovery ; Druggability ; IDH1 ; Drug ; Epigenetics ; Demethylase Click Chemistry and Applications ; Computational Drug Discovery Methods ; HIV/AIDS drug development and treatment
Authors: Verónica Montes‐García | Victor F. Martín | Manuel Obelleiro‐Liz | Ignacio Pérez‐Juste | Artur Ciesielski | Paolo Samorı́
Source: Advanced Sensor Research (article, 2025-01-21)
Field: Materials Science — Materials Chemistry

Ultrasensitive Isomer Discrimination: A Joint Surface‐Enhanced Raman Scattering (SERS) Spectroscopy and Machine Learning Strategy Abstract Isomer discrimination is of paramount importance across various sectors, including pharmaceuticals, agriculture, and the food industry, owing to their unique physicochemical characteristics. Because of their extremely similar characteristics, traditional analytical methods fail or encounter severe limitations in isomer discrimination. To overcome this grand challenge, a novel sensing strategy is proposed based on surface‐enhanced Raman scattering (SERS) substrates (i.e., plasmonic platforms) combined with machine learning algorithms. These plasmonic platforms exhibit exceptional signal uniformity across wide regions and sensitivity, enabling the discrimination of structural isomers (hydroquinone, resorcinol, pyrocatechol), geometric isomers ((Z/E)‐stilbene, (Z/E)‐resveratrol), and optical isomers (R/S‐ibuprofen). Notably, for the analysis of optical isomers, 1‐naphthalenethiol is employed as a probe to facilitate specific isomer orientation on the surface of the plasmonic platform through, for the first time, π–π interactions. The integration of machine learning methodologies, such as Partial Least Squares Regression and Artificial Neural Networks, significantly enhances both quantitative analysis and classification accuracy, achieving detection limits as low as 2 × 10⁻⁸ m . Validation with commercially available ibuprofen samples shows excellent agreement with traditional circular dichroism results, highlighting the method's robustness and precision. The strategy provides a versatile, ultrasensitive, and reliable solution for isomer discrimination, with broad applications in pharmaceuticals, environmental monitoring, and clinical diagnostics. Raman scattering ; Spectroscopy ; Raman spectroscopy ; Materials science ; Joint (building) ; Surface (topology) ; Chemistry ; Optics ; Physics ; Engineering ; Mathematics Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Acela López-Benítez | Alfredo Guevara | D. Palma‐Ramírez | Karen A. Neri-Espinoza | R. Silva–Rodrigo | J.A. Andraca-Adame
Source: Foods (article, 2025-07-21)
Field: Chemistry — Analytical Chemistry

Detection of Organophosphorus, Pyrethroid, and Carbamate Pesticides in Tomato Peels: A Spectroscopic Study Tomatoes are among the most widely consumed and economically significant fruits in the world. However, the extensive use of pesticides in their cultivation has led to the contamination of the peels, posing potential health risks to consumers. As one of the top global producers, consumers, and exporters of tomatoes, Mexico requires rapid, non-destructive, and real-time methods for pesticide monitoring. In this study, a detailed characterization of six pesticides using Raman and Fourier Transform Infrared (FT-IR) spectroscopies was carried out to identify their characteristic vibrational modes. The pesticides examined included different chemical classes commonly used in tomato cultivation: organophosphorus (dichlorvos and methamidophos), pyrethroids (lambda-cyhalothrin and cypermethrin), and carbamates (methomyl and benomyl). Tomato peel samples were examined both before and after pesticide application. Prior to treatment, the peel exhibited a well-organized polygonal structure and showed the presence of carotenoid compounds. After pesticide application, no visible structural damage was observed; however, distinct vibrational bands enabled the detection of each pesticide. Organophosphorus pesticides could be identified through vibrational bands associated with P-O and C-S bonds. Pyrethroid detection was facilitated by benzene ring breathing modes and C=C stretching vibrations, while carbamates were identified through C-N stretching contributions. Phytotoxicity testing in the presence of pesticides indicates no significant damage during the germination of tomatoes. Methomyl ; Pesticide ; Methamidophos ; Cypermethrin ; Chemistry ; Carbamate ; Pesticide residue ; Pyrethroid ; Environmental chemistry ; Phytotoxicity ; Cherry tomato ; Cyhalothrin ; Toxicology ; Horticulture ; Agronomy ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Insect and Pesticide Research
Authors: Fan Liu | Fang Wang | Zaiqi Zhang | Liang Cao | Jinran Wu | You‐Gan Wang
Source: Frontiers in Genetics (article, 2025-01-15)
Field: Chemistry — Analytical Chemistry

Classical and machine learning tools for identifying yellow-seeded Brassica napus by fusion of hyperspectral features Our findings indicate significant recognition accuracies of 96.55% and 98% for the PLSR and Logit-R models, respectively, aligning closely with the accuracy of previous methods. This approach represents a meaningful advancement in identifying yellow-seeded rapeseed, with high recognition accuracy demonstrating the practical applicability of these models. Hyperspectral imaging ; Brassica ; Artificial intelligence ; Seeding ; Computer science ; Fusion ; Machine learning ; Biology ; Computational biology ; Pattern recognition (psychology) ; Botany ; Agronomy ; Linguistics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: N. Santhosh | B A Praveena | Ashwin C. Gowda | Dhanunjaya Munthala | Soodkhet Pojprapai | M. Rudra Naik
Source: Materials Research Express (article, 2025-05-01)
Field: Materials Science — Polymers and Plastics

Synthesis and mechanical behaviour of corn husk and sisal fibre reinforced hybrid polymer composites Abstract This paper presents the synthesis and mechanical characterization of hybrid polymer composites reinforced with sisal and corn husk fibres using vacuum bag moulding. The selection of these natural fibres was guided by their availability, low cost, and biodegradability. Composite specimens were fabricated with fibre weight fractions of 5%, 10%, 15%, 20%, and 25%, employing epoxy resin as the matrix material. Mechanical tests including tensile, flexural, impact, and wear evaluations were conducted. The tensile strength increased from 32 MPa to 45 MPa, while the tensile modulus rose from 2.5 GPa to 3.5 GPa with increasing fibre content. Similarly, flexural strength improved from 60 MPa to 85 MPa, and the flexural modulus from 3.0 GPa to 4.0 GPa. Wear rate decreased from 0.8 mm 3 /Nm to 0.3 mm 3 /Nm, and both impact resistance and energy absorption capacity increased from 15 J to 35 J. SEM analysis revealed uniform fibre dispersion and strong interfacial bonding between the fibres and the matrix. These enhancements in mechanical performance indicate that the developed composites are environmentally friendly alternatives to conventional synthetic composites, with potential applications in construction, automotive panels, and sports equipment. Husk ; SISAL ; Composite material ; Materials science ; Polymer ; Botany Natural Fiber Reinforced Composites
Authors: Zheng Liu | Keqian Gong | Zifeng Song | Chao Zhou
Source: Journal of the American Ceramic Society (article, 2025-07-02)
Field: Materials Science — Ceramics and Composites

Atomic origin and dynamics of structural relaxation in borosilicate glass below glass transition temperature Abstract Owing to their inherent non‐crystalline nature, elucidating the structural relaxation of glass at the atomic scale presents significant challenges. In this investigation, 3 years of room temperature relaxation and in situ accelerated relaxation experiments were performed using a borosilicate glass. Additionally, the β relaxation activation energy of this borosilicate glass was determined by linear contraction monitoring, and the evolution of the network structure was characterized by a variety of spectroscopic techniques and simulated by molecular dynamics. The findings reveal that the atomic origin of the β relaxation below glass transition temperature can be ascribed primarily to the migration of the alkali ions. The network‐forming units are disturbed by the alkali ions and make responses consequently. Specifically, [BO 3 ] triangles are inclined to accept a nonbridging oxygen from the [SiO 4 ] tetrahedra, transforming into [BO 4 ] tetrahedra with coordination changes, which ultimately leads to the formation of borosilicate rings. Such mechanism manifests the structural relaxation in borosilicate glass from the atomic scale and is expected to provide new insights into the microscopic nature of structural relaxation in oxide glasses. Borosilicate glass ; Glass transition ; Relaxation (psychology) ; Materials science ; Porous glass ; Vitrification ; Dynamics (music) ; Chemical physics ; Mineralogy ; Thermodynamics ; Condensed matter physics ; Chemistry ; Composite material ; Polymer ; Physics ; Nuclear physics Glass properties and applications ; Material Dynamics and Properties ; High-pressure geophysics and materials
Authors: M. YAMAUCHI | Yukino Kitamura | Chihiro Tada | Ryuji Kato | Hiroaki Gotoh
Source: Journal of the Science of Food and Agriculture (article, 2025-07-24)
Field: Chemistry — Organic Chemistry

Kinetic and thermodynamic evaluation of antioxidant reactions: factors influencing the radical scavenging properties of phenolic compounds in foods The radical scavenging ability of antioxidants can be better assessed by conducting evaluations from both thermodynamic and kinetic aspects. This study provides a better understanding of the properties of antioxidants during conventional evaluations using 2,2-diphenyl-1-picrylhydrazyl measurements. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. Hydroquinone ; Catechol ; Chemistry ; Trolox equivalent antioxidant capacity ; Antioxidant ; Phenol ; Scavenging ; Antioxidant capacity ; Trolox ; Organic chemistry ; Polyphenol ; Kinetics ; Food science Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Qiqi Dai | Yinpeng Wang | Cheng Xu | Dongxiao Li | Prakash Pitchappa | Thomas Caiwei Tan | Ranjan K. Singh | Chengkuo Lee
Source: Advanced Science (article, 2025-09-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning‐Augmented Diffusion Model Terahertz (THz) metamaterials with high-figure-of-merit (high-FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low-asymmetry geometric structures, leaving high-asymmetry designs largely unexplored due to the inefficiency of trial-and-error-based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data-driven nature. Here, a novel prior knowledge-guided generative model augmented by a physics-constrained active learning mechanism to design high-asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high-FoM THz resonance and generates new high-asymmetry structures. To mitigate the limited number of classical structures, the generated high-asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high-asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning-based metamaterial design. The proposed scheme for generating high-asymmetry structures provides a new effective and efficient solution for high-FoM resonance, expanding applications in high-sensitivity THz metadevices. Metamaterial ; Terahertz radiation ; Resonance (particle physics) ; Focus (optics) ; Key (lock) ; Acceleration ; Automation ; Deep learning Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Millimeter-Wave Propagation and Modeling
Authors: Lixin Zhang | Zhensheng Huang | Xiao Zhang
Source: Journal of Chemometrics (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Stacking Ensemble Learning Method for Quantitative Analysis of Soluble Solid Content in Apples ABSTRACT The soluble solids content (SSC) in apples directly affects their quality. This study aimed to detect SSC nondestructively using hyperspectral technology combined with chemometrics. However, data generation may not follow a specific pattern, and even small perturbations in the data can have a significant impact on the constructed model. To improve the anti‐interference capability of individual models, this study proposed a stacking ensemble learning method that adopted partial least squares (PLS), support vector machine (SVM), extreme gradient boosting (Xgboost), random forest (RF) as basic‐learners, and RF serving as a meta‐learner. Experimental results showed that the performance of the established model on the test set were as follows: the root mean square error (RMSE) was 0.4325, mean absolute error (MAE) was 0.3245, mean absolute percentage error (MAPE) was 0.0271, coefficient of determination () was 0.9250. These results indicate that the stacking ensemble learning approach could appropriately fuse the predictive results of each basic‐learner and improve the prediction accuracy of individual models. To verify the superiority of the proposed stacking ensemble learning method, the selection of its basic‐learners, meta‐learner, and combination strategy were compared and analyzed. This study not only provides a theoretical reference for the further development of related nondestructive detection equipment but also offers guidance for fusion algorithms as well. Stacking ; Content (measure theory) ; Ensemble learning ; Computer science ; Chemistry ; Mathematics ; Artificial intelligence ; Organic chemistry Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Smart Agriculture and AI
Authors: Constantin M. Nuber | Max Schwab | David B. Konrad
Source: Organic & Biomolecular Chemistry (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

A perspective on cysteine-reactive activity-based probes Activity-based protein profiling (ABPP) is a powerful method to investigate protein function, identify protein targets and determine the mechanisms-of-action of chemical probes as well as small molecule drugs. Our perspective provides an overview of the design of covalent cysteine-targeted activity-based probes (ABPs) that rely on reactive elements ('chemotypes' or 'warheads') as well as functional handles and describes their use in mass spectrometry-based chemoproteomics analysis. These segments are followed by strategies for the development of advanced ABPs that are employed in live cells for chemoproteomics analyses and to install post-translational modifications. Our discussion extends to their potential, limitations and opportunities, followed by perspectives on advancing the reach of ABPP and its implications for the pharmaceutical industry. Perspective (graphical) ; Profiling (computer programming) Click Chemistry and Applications ; Biotin and Related Studies ; Protein Degradation and Inhibitors