jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siham Aggoun | Salah Belaıdı | Meriam Ghamri | Aicha Kerassa | Mehmet Çınar | Imane Yamari | Oussama Abchır | Samir Chtita
Source: The Philippine journal of science (article, 2024-02-25)
Field: Chemistry — Organic Chemistry

Artificial Neural Network and Multiple Regression Analysis Applied to 2D-QSAR Studies: the Case of Imidazolidine-2,4-dione as PTP1B Inhibitors In this investigation, we undertook a comprehensive quantitative structure-activity relationship (QSAR) analysis using both modern artificial neural network (ANN) methods and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine- 2,4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the ANN architecture (6-4-1) over the MLR method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study’s significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics. Quantitative structure–activity relationship ; Artificial neural network ; Linear regression ; Artificial intelligence ; Computer science ; Regression ; Anticancer drug ; Partial least squares regression ; Machine learning ; Mathematics ; Drug ; Biology ; Pharmacology ; Statistics Synthesis and biological activity ; Protein Tyrosine Phosphatases ; Bioactive Compounds and Antitumor Agents
Authors: Yi-Rong Liu | Yan Jiang
Source: The Journal of Physical Chemistry A (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Predicting Composition Evolution for a Sulfuric Acid-Dimethylamine System from Monomer to Nanoparticle Using Machine Learning Experimental and theoretical studies on the compositional changes of new particle formation in the nucleation and initial growth stages of acid-base systems (2 and 5 nm) are extremely challenging. This study proposes a machine learning method for predicting the composition change of the sulfuric acid-dimethylamine system in the transformation from monomer to nanoparticle by learning the structure and composition information on small-sized sulfuric acid (SA)-dimethylamine (DMA) molecular clusters. Based on this method and changes in components, we found that the sulfuric acid-dimethylamine growth was mainly through the alternate adsorption of (SA)<sub>1</sub>(DMA)<sub>1</sub>, (SA)<sub>1</sub>(DMA)<sub>2</sub>, and (SA)<sub>1</sub> clusters at the early stage of nucleation, which accounted for about 70, 20, and 10%, respectively. This can explain the nature of possible changes in cluster acidity during the initial nucleation stage for the sulfuric acid-dimethylamine system. This method can also predict the base-stabilization mechanism of the sulfuric acid-dimethylamine system without relying on any experimental data, thereby yielding results that are consistent with those of previous experimental measurement. Dimethylamine ; Sulfuric acid ; Nucleation ; Monomer ; Chemistry ; Adsorption ; Nanoparticle ; Cluster (spacecraft) ; Chemical engineering ; Inorganic chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Computer science ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Hydrodesulfurization Studies
Authors: Mengzhen Lu | Qunxi Zhang | Yiming Li | Jing Ji | Lvzheng Zhang | Jun Wang
Source:  (article, 2024-05-17)
Field: Chemistry — Analytical Chemistry

Research on Labeling Method for Fungal Diseases in Murals Based on Hyperspectral Images Murals are an important part of human cultural heritage, reflecting the cultural characteristics of different periods and containing extremely high research value. However, for a long time, due to environmental factors such as humidity and temperature, as well as internal factors such as mural production materials and processes, mold has grown on murals, seriously affecting their aesthetics and preservation status. Mold disease labeling can detect mold growth and provide technical support for mold prevention and control. Traditional manual annotation methods are not only time-consuming and laborious, but also suffer from subjective errors and inconsistent annotation. Therefore, this article proposes a method for labeling fungal diseases in murals based on hyperspectral images, which can achieve automatic recognition and labeling of fungi on the surface of murals. This method first generates mold training samples based on hyperspectral image unmixing, and then proposes a mural mold labeling network framework based on a multi grouping strategy long short-term memory network and a multi-scale convolutional neural network (MGL-MC), effectively combining spatial and spectral information of mural hyperspectral images. The experiment shows that the method proposed in this article has a good effect on the recognition and labeling of mold on the surface of murals, and can improve the accuracy of mold detection. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Computer vision ; Remote sensing ; Computer graphics (images) ; Geology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Alaa Maatouk | Thibaud Rossel | Bruno Therrien
Source: Inorganics (article, 2024-12-24)
Field: Chemistry — Spectroscopy

Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes A water-soluble arene ruthenium metalla-rectangle (MR1) functionalized with boronic acid groups was used to host various fluorescent dyes (fluorescein, eosin Y, and erythrosin B). These simple host–guest systems partially quench the natural fluorescence of the dyes, which can be regained in the presence of saccharides, phosphorylated molecules, and other analytes. The intensity of the regained fluorescence is directly linked to the nature of the analyte, and it shows some dose–response relationships with saccharides and phosphorylated molecules that are not compatible with a displacement assay, thus suggesting an allosteric mechanism. Interestingly, when fluorescein is trapped by the metalla-rectangle in the presence of D-fructose, half of the maximum fluorescence intensity is recovered at a fructose concentration of 17.2 ± 4.7 μM, while, for D-glucose, a concentration of 50.6 ± 2.5 μM is required for the same effect. Indeed, all combinations of analyte–host–dye (seven analytes, one host, three dyes) show a unique dose–response relationship in water at pH 8.0. However, in the presence of naphthalene and pyrene, fluorescein⸦MR1 shows a different behavior, acting as an indicator displacement assay with the full recovery of fluorescence. All data were analyzed by unsupervised machine learning technologies (PCA and cluster analysis), suggesting that such systems with multiple analyte–response behaviors are offering new perspectives for the development of highly sensitive, easily tunable, water-soluble, fluorescent-based sensing arrays for biomolecules and other analytes. Fluorescence ; Analyte ; Chemistry ; Ruthenium ; Biomolecule ; Boronic acid ; Fluorescein ; Molecule ; Combinatorial chemistry ; Photochemistry ; Chromatography ; Organic chemistry ; Biochemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes
Authors: Devid Kumar Sahu | Y. M. Sharma | G. S. Tagore | P. S. Kulhare | R. K. Nema | R. K. Sahu
Source: Asian Journal of Soil Science and Plant Nutrition (article, 2024-12-17)
Field: Chemistry — Analytical Chemistry

Prediction of Soil Colour using Vis-NIR Spectroscopy and Machine Learning Models Soil colour is a critical indicator of soil properties and conditions, influencing various agronomic and environmental factors. A total of 2216 surface soil samples (0-15 cm) were collected from the Kymore Plateau and Satpura Hill zone of Madhya Pradesh, using Global Positioning System (GPS) for precise location. Soil colour parameters were measured in the field using the Munsell soil colour chart, while chemical analysis was conducted in the laboratory following standard procedures. Additionally, spectra of the soil samples were recorded using a spectroradiometer under laboratory conditions. The results showed that the soil colour hues ranged from 10R, 10YR, 2.5Y, 2.5YR, 5Y, 5R, 5YR, to 7.5YR, Values and Chroma varied from 2 to 7 and 1 to 8, respectively. Correlation analysis revealed negative correlations between the RGB components and organic carbon, with r values of -0.114**, -0.071**, and -0.101* for R, G, and B, respectively. Polynomial models showed the best fit for the relationship between the value and chroma of the colour parameters and soil organic carbon (SOC), with equations Y = 0.086x² - 0.860x + 7.528 (R² = 0.982) and Y = 0.018x² - 0.249x + 6.126 (R² = 0.948), respectively. A linear relationship was observed between chroma and available phosphorus (P), with the equation Y = -0.873 + 13.92 (R² = 0.922). In addition, machine learning models, including PLSR, SVM, Random Forest, ANN, XGBoost, LightGBM, CatBoost, and ELM algorithms, were used to predict soil colour parameters. Among these, the Random Forest and XGBoost models demonstrated the best performance in predicting soil colour parameters (L*, a*, b*, R, G, and B), with model accuracies of 83.6%, 80.9%, 83.0%, 84.3%, 83.7%, and 83.4%, respectively. soil colour variation depicted in the maps generated using GIS can also serve as covariates for mapping, offering comprehensive insights into the soil's properties. Spectroscopy ; Remote sensing ; Near-infrared spectroscopy ; Computer science ; Artificial intelligence ; Environmental science ; Machine learning ; Geology ; Optics ; Physics Spectroscopy and Chemometric Analyses
Authors: Paul Fuchs | Stephan Thaler | Sebastien Röcken | Julija Zavadlav
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Chemtrain: Learning Deep Potential Models Via Automatic Differentiation and Statistical Physics Automatic differentiation ; Deep learning ; Artificial intelligence ; Computer science ; Machine learning ; Statistical physics ; Physics ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Qiushuang Mo | Lili Xu | Fangxiu Meng | Shaoyong Hong | Xuemei Lin
Source: Open Journal of Statistics (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Prediction of Wastewater Index Using CNN Architecture and PLS Series Statistical Methods Chemical oxygen demand (COD) is an important index to measure the degree of water pollution. In this paper, near-infrared technology is used to obtain 148 wastewater spectra to predict the COD value in wastewater. First, the partial least squares regression (PLS) model was used as the basic model. Monte Carlo cross-validation (MCCV) was used to select 25 samples out of 148 samples that did not conform to conventional statistics. Then, the interval partial least squares (iPLS) regression modeling was carried out on 123 samples, and the spectral bands were divided into 40 subintervals. The optimal subintervals are 20 and 26, and the optimal correlation coefficient of the test set (RT) is 0.58. Further, the waveband is divided into five intervals: 17, 19, 20, 22 and 26. When the number of joint intervals under each interval is three, the optimal RT is 0.71. When the number of joint subintervals is four, the optimal RT is 0.79. Finally, convolutional neural network (CNN) was used for quantitative prediction, and RT was 0.9. The results show that CNN can automatically screen the features inside the data, and the quantitative prediction effect is better than that of iPLS and synergy interval partial least squares model (SiPLS) with joint subinterval three and four, indicating that CNN can be used for quantitative analysis of water pollution degree. Series (stratigraphy) ; Index (typography) ; Computer science ; Statistics ; Time series ; Data mining ; Artificial intelligence ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Sergei V. Tatarin | L.V. Krasnov | E. V. Nykhrikova | M. G. Minin | Daniil Smirnov | Andrei V. Churakov | Stanislav I. Bezzubov
Source: ChemRxiv (preprint, 2024-11-01)
Field: Materials Science — Materials Chemistry

Predicting Emission Wavelengths and Quantum Yields of Diverse Bis-cyclometalated Iridium(III) Complexes Using Machine Learning Cyclometalated iridium(III) complexes are excellent emitters for phosphorescent organic light-emitting diodes, but the design of such complexes require substantial cost and experimental efforts. In turn, the predictive power of density functional theory calculations is seldom enough for reliable prediction of the excited state properties of iridium(III) complexes. In this work, we aimed at data-driven prediction of the emission energies and photoluminescence quantum yields of such complexes. To this end, we created a database (IrLumDB) that contains experimentally measured luminescence properties for over 1200 literature bis-cyclometalated iridium(III) complexes. Based on this database, we developed machine learning models that are capable of predicting the energy of emission maxima and photoluminescence quantum yields for the iridium phosphors with mean absolute errors of 18.26 nm and 0.129, respectively, requiring only SMILES of ligands. Furthermore, we validated the model for emission wavelength prediction on the set of 33 experimentally obtained luminescence spectra for newly synthesized and characterized iridium(III) complexes. Our data-driven methodology will complement quantum chemical calculations as an efficient alternative approach for the prediction of the excited-state properties of large sets of bis-cyclometalated iridium(III) complexes, facilitating computational discovery of efficient emitters. The emission properties prediction and the dataset exploration are available at https://irlumdb.streamlit.app/. Iridium ; Wavelength ; Quantum ; Computer science ; Optoelectronics ; Materials science ; Physics ; Chemistry ; Quantum mechanics ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Lanthanide and Transition Metal Complexes ; Organic Light-Emitting Diodes Research
Authors: Yongnian Qi | Xiaoguang Guo | Zhuang Song | Wanxue Zhang | Xing Gao | Ping Zhou
Source: Journal of Non-Crystalline Solids (article, 2024-11-27)
Field: Materials Science — Ceramics and Composites

Transmittance enhancement of amorphous SiO2 via ultraviolet irradiation to eliminate atomic defects Transmittance ; Irradiation ; Amorphous solid ; Ultraviolet ; Materials science ; Optoelectronics ; Photochemistry ; Chemistry ; Crystallography ; Physics ; Nuclear physics Glass properties and applications ; Thin-Film Transistor Technologies ; Semiconductor materials and devices
Authors: Peng‐Hu Du | Yoshiyuki Kawazoe | Qiang Sun
Source: Nano Letters (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Unconventional Thermal Rectification in Assemblies of Supertetrahedral T<sub>3</sub>-Type Sn<sub>4</sub>In<sub>6</sub>Se<sub>20</sub> Clusters Going beyond the conventional design paradigm with atoms as building blocks, we propose the concept of cluster-assembled thermal rectifiers comprising metal chalcogenide supertetrahedral clusters. Different from the experimentally reported T<sub>4,∞</sub> and T<sub>5,∞</sub>, for the first time we assemble T<sub>3</sub>-Sn<sub>4</sub>In<sub>6</sub>Se<sub>20</sub> clusters into a stable T<sub>3,∞</sub> framework without needing extra ions, based on which the thermal rectification (TR) effect is explored using machine-learning molecular dynamics and the mode-resolved phonon Boltzmann transport equation. The tetrahedron-shaped cluster assembly serves as a novel TR switch, where the open state shows an outstanding TR efficiency (∼40%) arising from the asymmetric lateral confinement due to not only the phonon particle behavior but also the phonon wave nature. The prism-shaped assembly has a symmetric skeleton but with asymmetric surface roughness induced by clusters, thus exhibiting an unusual TR effect, distinguished from atom-based symmetric systems with atomically flat surfaces. Our findings demonstrate the unique potential of cluster assemblies for unconventional thermal rectification. Rectification ; Materials science ; Crystallography ; Thermal ; Chemistry ; Physics ; Thermodynamics Phase-change materials and chalcogenides ; Thermal properties of materials ; Thermal Expansion and Ionic Conductivity
Authors: Jiale Liu | Zheng Guo | Hantian You | Changsheng Zhang | Luhua Lai
Source: Angewandte Chemie (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

All‐Atom Protein Sequence Design Based on Geometric Deep Learning Abstract Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to produce the complete all‐atom structures for designed sequences. GeoSeqBuilder uses spatial geometric features from protein backbones and explicitly includes three‐body interactions of neighboring residues. GeoSeqBuilder achieves native residue type recovery rate of 51.6 %, comparable to ProteinMPNN and other leading methods, while accurately predicting side chain conformations. We first used GeoSeqBuilder to design sequences for thioredoxin and a hallucinated three‐helical bundle protein. All the 15 tested sequences expressed as soluble monomeric proteins with high thermal stability, and the 2 high‐resolution crystal structures solved closely match the designed models. The generated protein sequences exhibit low similarity (minimum 23 %) to the original sequences, with significantly altered hydrophobic cores. We further redesigned the hydrophobic core of glutathione peroxidase 4, and 3 of the 5 designs showed improved enzyme activity. Although further testing is needed, the high experimental success rate in our testing demonstrates that GeoSeqBuilder is a powerful tool for designing novel sequences for predefined protein structures with atomic details. GeoSeqBuilder is available at https://github.com/PKUliujl/GeoSeqBuilder . Protein design ; Protein engineering ; Sequence (biology) ; Protein structure ; Computational biology ; Computer science ; Protein structure prediction ; Chemistry ; Protein sequencing ; Bioinformatics ; Biological system ; Peptide sequence ; Biochemistry ; Enzyme ; Biology ; Gene Enzyme Structure and Function ; Microbial Natural Products and Biosynthesis ; Computational Drug Discovery Methods
Authors: Qinghai Jiang | Bin Chen | Jia Chen | Zhiyu Song
Source: Processes (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Non-Destructive Analysis for Machine-Picked Tea Leaf Composition Using Near-Infrared Spectroscopy Combined Chemometric Methods This paper aimed to predict the mechanical composition of machine-picked fresh tea leaves (MPFTLs) using near-infrared spectroscopy (NIRS) rapidly and non-destructively. Samples of MPFTL with different mechanical composition ratios were collected and subjected to NIRS analysis. Subsequently, various preprocessing methods were employed to eliminate extraneous noise information. Next, characteristic spectral information was extracted using the backward interval partial least squares (biPLS) method, which was subsequently subjected to principal component analysis (PCA). Finally, a predictive model was constructed by applying the back propagation artificial neural network (BP-ANN) method, which was tested by external samples to assess its predictive efficacy, and the results were expressed as root mean square error and determination coefficient of prediction (Rp2). The optimal spectral pretreatment method was the following: (standard normal variate (SNV) + second derivative (SD)). Four characteristic spectral subintervals of ([2, 3, 7, 10]) were screened out, and the cumulative contribution rate of 95.20%, attributable to the first three principal components, was determined. When the tanh transfer function was applied to construct the BP-ANN-NIRS model, the results demonstrated optimal performance, exhibiting a root mean square error and a determination coefficient of prediction (Rp2) of 0.976 and 0.027, respectively. The absolute values of prediction deviation for all prediction set samples were found to be less than 0.04. The results of the best BP-ANN model for external samples were found to be in close agreement with those of the prediction set model. NIRS technology has successfully achieved the forecasting of the mechanical composition of machine-picked fresh tea leaves rapidly and accurately, providing a fair and convenient new method for purchasing fresh tea raw materials by machines, according to their quality, and promoting the sustainable high-quality and healthy development of the tea industry. Principal component analysis ; Partial least squares regression ; Mean squared error ; Standard deviation ; Mathematics ; Coefficient of determination ; Artificial neural network ; Biological system ; Second derivative ; Standard error ; Preprocessor ; Statistics ; Test set ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Water Quality Monitoring and Analysis