jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeonghyeon Kim | Juno Nam | Seongok Ryu
Source: arXiv (Cornell University) (preprint, 2024-06-14)
Field: Chemistry — Organic Chemistry

Understanding active learning of molecular docking and its applications With the advancing capabilities of computational methodologies and resources, ultra-large-scale virtual screening via molecular docking has emerged as a prominent strategy for in silico hit discovery. Given the exhaustive nature of ultra-large-scale virtual screening, active learning methodologies have garnered attention as a means to mitigate computational cost through iterative small-scale docking and machine learning model training. While the efficacy of active learning methodologies has been empirically validated in extant literature, a critical investigation remains in how surrogate models can predict docking score without considering three-dimensional structural features, such as receptor conformation and binding poses. In this paper, we thus investigate how active learning methodologies effectively predict docking scores using only 2D structures and under what circumstances they may work particularly well through benchmark studies encompassing six receptor targets. Our findings suggest that surrogate models tend to memorize structural patterns prevalent in high docking scored compounds obtained during acquisition steps. Despite this tendency, surrogate models demonstrate utility in virtual screening, as exemplified in the identification of actives from DUD-E dataset and high docking-scored compounds from EnamineReal library, a significantly larger set than the initial screening pool. Our comprehensive analysis underscores the reliability and potential applicability of active learning methodologies in virtual screening campaigns. Docking (animal) ; Computer science ; Computational biology ; Artificial intelligence ; Chemistry ; Biology ; Medicine Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Hanjing Ge
Source: Current Analytical Chemistry (article, 2024-05-07)
Field: Chemistry — Analytical Chemistry

Deep Learning-enhanced Hyperspectral Imaging for the Rapid Identificationand Classification of Foodborne Pathogens Background: Bacterial cellulose (BC) is a versatile biomaterial with numerous applications, and the identification of bacterial strains that produce it is of great importance. This study explores the effectiveness of a Stacked Autoencoder (SAE)-based deep learning method for the classification of bacterial cellulose-producing bacteria. Objective: The primary objective of this research is to assess the potential of SAE-based classification models in accurately identifying and classifying bacterial cellulose-producing bacteria, with a particular focus on strain GZ-01. Methods: Strain GZ-01 was isolated and subjected to a comprehensive characterization process, including morphological observations, physiological and biochemical analysis, and 16S rDNA sequencing. These methods were employed to determine the identity of strain GZ-01, ultimately recognized as Acetobacter Okinawa. The study compares the performance of SAE-based classification models to traditional methods like Principal Component Analysis (PCA). Results: The SAE-based classifier exhibits outstanding performance, achieving an impressive accuracy of 94.9% in the recognition and classification of bacterial cellulose-producing bacteria. This approach surpasses the efficacy of conventional PCA in handling the complexities of this classification task. Conclusion: The findings from this research highlight the immense potential of utilizing nanotechnology- driven data analysis methods, such as Stacked Autoencoders, in the realm of bacterial cellulose research. These advanced techniques offer a promising avenue for enhancing the efficiency and accuracy of bacterial cellulose-producing bacteria classification, which has significant implications for various applications in biotechnology and materials science. Hyperspectral imaging ; Identification (biology) ; Artificial intelligence ; Computational biology ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Massimiliano Lupo Pasini | Jong Youl Choi | Pei Zhang | Justin C. Baker
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

HydraGNN v3.0 New or improved capabilities included in v3.0 release are as follows: 1. Enhancement in message passing layers through generalization of the class inheritance to enable the inclusion of a broader set of message passing policies Inclusion of equivariant message passing layers from the original implementations of: SchNet (https://pubs.aip.org/aip/jcp/article/148/24/241722/962591/SchNet-A-deep-learning-architecture-for-molecules); DimeNet++ (https://arxiv.org/abs/2011.14115); EGNN models (https://arxiv.org/pdf/2102.09844.pdf) 2. Restructuring of class inheritance for data management 3. Support of DDStore https://github.com/ORNL/DDStore capabilities for improved distributed data parallelism on large volumes of data that cannot fit on intra-node memory capacities 4. Large-scale system support for OLCF-Crusher and OLCF-Frontier Computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Machine Learning in Healthcare
Authors: Arnór I. Sigurdsson | Justus F. Gräf | Zhiyu Yang | Kirstine Ravn | Jonas Meisner | Roman Thielemann | Henry Webel | Roelof A. J. Smit | Lili Niu | Matthias Mann | FinnGen | Bjarni J. Vilhjálmsson | Benjamin M. Neale | Andrea Ganna | Torben Hansen | Ruth J. F. Loos | Simon Rasmussen
Source: medRxiv (preprint, 2024-07-07)
Field: Chemistry — Spectroscopy

Non-linear genetic regulation of the blood plasma proteome Abstract Although thousands of genetic variants are linked to human traits and diseases, the underlying mechanisms influencing these traits remain largely unexplored. One important aspect is to understand how proteins are regulated by the genome by identifying protein quantitative trait loci (pQTLs). Beyond this, there is a need to understand the role of complex genetics effects such as dominance and epistasis that regulate plasma proteins and protein biomarkers. Therefore, we developed EIR-auto-GP, a deep learning-based approach, to identify such effects. Our results complement the additive genetic regulation identified in previous pQTLs screens by adding a nuanced view of the complex genetic regulation of plasma proteins. Applying this method to the UK Biobank proteomics cohort of 48,594 individuals, we identified 138 proteins that were regulated by non-linear effects, including non-linear covariates (123) as well as genetic dominance and epistasis (15). We uncovered a novel epistatic interaction between the ABO and FUT3 loci, and demonstrated dominance effects of the ABO locus on plasma levels of pathogen recognition receptors CD209 and CLEC4M. Furthermore, we replicated these findings and the methodology across Olink and mass spectrometry-based cohorts and concluded that large sample sizes are needed to discover more complex genetic effects. Our approach presents a systematic, large-scale attempt to identify complex effects of plasma protein levels and can be applied to study other tissues or molecular QTLs. Proteome ; Computational biology ; Biology ; Genetics Advanced Proteomics Techniques and Applications
Authors: H. Q. Zhang | Jinyang Liu | Dongshan Jiang | Ziqiu Luo | Wenxiang Liang | Shicong Li | Xin Chen | Xingxing Yuan | Shangbing Gao
Source: IEEE Access (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

LMS-Net: A Light-Weight Network for Mungbean Salt Stress Identification Salt stress is one of the main factors affecting mungbean production, and the assessment of salt tolerance in mungbean typically requires long-term morphological observation or time-consuming physiological and biochemical experiments, which are time-consuming and labor-intensive in field conditions. This study introduced a salt stress level identification model for mungbeans, named LMS-Net, which used early-stage chlorophyll fluorescence imaging and a lightweight convolutional neural network (CNN) model to identify the salt tolerance of seedlings. To facilitate deployment on mobile and portable devices, we incorporated depth-wise separable convolutions and reparameterization techniques into LMS-Net to reduce latency during inference. Moreover, to better capture the key features and regions of stress, we proposed a Multi-scale Collaborative Attention Mechanism (MSCA) into LMS-Net. Using the FluorCam Open Chlorophyll Fluorescence Imaging System, we collected 1,879 chlorophyll fluorescence images of mungbean natural populations under salt stress to create a mungbean salt stress chlorophyll fluorescence dataset. Through comprehensive comparisons with mainstream lightweight models based on CNN and Transformer architectures, the LMS-Net model achieved an average identification accuracy of 97.64%, an average precision of 97.78%, an average recall of 97.74%, and an average F1-score of 97.75% on the test set, which benefit from model architecture and MSCA. These classification performance metrics surpass those of the comparative models. Using this method, we identified two highly salt-tolerant mungbean materials, ‘Suhuang 1’ and ‘Wei 9002-341’, from a natural population of 230 mungbean accessions. In summary, this method is promising and valuable for early-stage salt tolerance assessment of mungbean seedlings, offering new insights for screening salt-tolerant germplasm resources in mungbeans and other crops. Stress (linguistics) ; Identification (biology) ; Net (polyhedron) ; Computer science ; Salt (chemistry) ; Mathematics ; Chemistry ; Botany ; Biology Spectroscopy and Chemometric Analyses
Authors: Jan Jakub Kęsik | Wiesław Paja | Paweł Jakubczyk | Maryna Khalavka | Piotr Terlecki | Marek Iłżecki | Wioletta Rzad | Joanna Depciuch
Source: Nanomedicine Nanotechnology Biology and Medicine (article, 2024-09-26)
Field: Chemistry — Analytical Chemistry

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses Chemometrics ; Fourier transform infrared spectroscopy ; Spectroscopy ; Stenosis ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Chemistry ; Internal medicine ; Medicine ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Optical Imaging and Spectroscopy Techniques ; Infrared Thermography in Medicine
Authors: Veera Mohana Rao Kakita | D. Flemming Hansen
Source: ChemRxiv (preprint, 2024-11-07)
Field: Chemistry — Spectroscopy

Deep Learning Assisted Proton Pure Shift NMR Spectroscopy Nuclear magnetic resonance spectroscopy (NMR) plays a key role for the analysis of a plethora of molecules, including natural products and drug-like organic molecules. For such cases 1H NMR spectra have proven imperative because of their high sensitivity. However, these spectra are complicated by complex multiplet patterns that, although important for the analysis, lead to substantial overlap. Here we show a deep-learning approach, which transforms spin-echo modulated 1H NMR spectra into highly sensitive and high-resolution singlet NMR spectra, that is, virtual homonuclear decoupled pure shift spectra. The approach was evaluated on experimental NMR spectra of complex organic compounds, where it outperforms current methods. The method also predicts uncertainties of the transformation and therefore allows for quantifications. We believe that our approach will provide significant advantages when characterizing low sensitivity samples, where no signals are observed in traditional pure-shift spectra and strong overlaps are hampering analysis from conventional spectra. Proton ; Spectroscopy ; Nuclear magnetic resonance spectroscopy ; Materials science ; Chemistry ; Nuclear magnetic resonance ; Physics ; Nuclear physics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Atomic and Subatomic Physics Research
Authors: Jinhong Yu | Rhiann Jakubowski | Terry Bates | Yu Jiang | Chang Chen
Source:  (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Integrating Hyperspectral Imaging and Machine Learning for Non-Destructive Damage Detection of Grapes <b><sc>Abstract.</sc></b> Fungal diseases can adversely affect the flavor profile and nutritional value of grapes, thereby impacting the marketability and consumer acceptance of final products. Quality defects in grapes are conventionally examined by visual inspection, which is somewhat subjective and qualitative, or chemical analysis that are laborious, slow, and destructive. These methods are facing the challenges in the current practices that feature mechanical harvesting and large throughputs. More accurate and rapid Novel onsite assessment techniques of fruit quality in the fruit processing facilities that are more accurate and rapid will address this challenge and are critical to effective food quality management. The aim of this study was to develop a novel non-destructive sensing technique for grape defect detection using hyperspectral imaging integrated with machine learning methods. Two hundred and seventy-five freshly harvested grapes of Niagara variety with different levels of Downy Mildew infection (clean, low, moderate, and extensive) were classified by conventional visual inspection methods. Berries were imaged by a line-scan hyperspectral imaging system that covered the visible to near-infrared (Vis-NIR) spectral range (400–1000 nm) at reflectance mode. The raw hyperspectral images were preprocessed by flat field correction (FFC) to remove effects due to nonuniform illumination. The resultant images were segmented by thresholding to extract the mean spectra of individual grape berries in the images. Principal Component Analysis (PCA) was performed to extract the most important wave bands that differentiate different defect levels. A random forest model was assessed over 30 iterations, each time randomly reassigning data into training and testing sets from the preprocessed spectra with reduced dimensionality. The model demonstrated a promising efficacy in categorizing the degrees of downy mildew damage for Niagara grapes, with an average accuracy rate of 83%. These findings suggest that hyperspectral reflectance imaging is an efficient tool for assessing the quality and safety of processing grapes rapidly and non-destructively in processing facilities. This novel sensing technique can potentially be expanded to table grapes and other berry fruits. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Machine learning ; Computer vision ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses
Authors: Wenke Liang | Mingshan Yao
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

Suppression of Interference Fringes in Wavelength Modulation Based on Convolutional Neural Networks Interference (communication) ; Modulation (music) ; Convolutional neural network ; Wavelength ; Computer science ; Physics ; Optics ; Telecommunications ; Artificial intelligence ; Acoustics ; Channel (broadcasting) Spectroscopy and Laser Applications ; Advanced Optical Sensing Technologies ; Optical Systems and Laser Technology
Authors: Alaa Maatouk | Thibaud Rossel | Bruno Therrien
Source: ChemRxiv (preprint, 2024-07-17)
Field: Chemistry — Spectroscopy

Allosteric Fluorescent Detection of Saccharides and Biomole-cules in Water from a Boronic Acid Functionalized Arene Ru-thenium Assembly Hosting Fluorescent Dyes. A water-soluble arene-ruthenium metalla-rectangle (MR1) functionalized with boronic acid groups was used to host vari-ous fluorescent dyes (fluorescein, eosin Y, 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, as well as 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 ana-lyte-host-dye (7 analytes, 1 host, 3 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 full recovery of fluorescence. All data were analyzed by unsupervised machine learning technologies (PCA, 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 ; Boronic acid ; Chemistry ; Allosteric regulation ; Fluorescent protein ; Combinatorial chemistry ; Biochemistry ; Green fluorescent protein ; Enzyme Molecular Sensors and Ion Detection ; Organoboron and organosilicon chemistry ; Luminescence and Fluorescent Materials
Authors: A. Gençer | Özge Sürücü | Murat Şahin | Faruk Özel | Gökhan Sürücü
Source: Physica Scripta (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Determining the hydrogen production potential of Co<sub>n</sub>Mo<sub>6</sub>Se<sub>8</sub> chevrel phases Abstract In this study, the Co n Mo 6 Se 8 (n = 1, 2, 3, and 4) Chevrel phases are investigated by using Density Functional Theory (DFT) to reveal their potential for photocatalytic hydrogen production. The stability conditions of these phases reveal that CoMo 6 Se 8 , Co 2 Mo 6 Se 8 , and Co 3 Mo 6 Se 8 satisfy the thermodynamic and mechanic stability properties, while Co 4 Mo 6 Se 8 does not satisfy any of these properties. Furthermore, the formation enthalpy of these phases shows that CoMo 6 Se 8 , Co 2 Mo 6 Se 8 , and Co 3 Mo 6 Se 8 can be synthesized experimentally due to having negative formation enthalpy values. Furthermore, the thermal stabilities of the machine-learning (ML) force fields are investigated by ab-initio molecular dynamics (AIMD) calculations. The electronic properties of these phases are also investigated in detail, and it is found that Co 3 Mo 6 Se 8 has a suitable band gap for photocatalytic water splitting. Concerning the investigation of the valence band and conduction band levels, it is shown that Co 3 Mo 6 Se 8 has a conduction band minimum level suitable for producing hydrogen. This study is the first attempt to reveal the hydrogen production performance of the Co n Mo 6 Se 8 (n = 1, 2, 3, and 4) Chevrel phases as far as the literature is concerned, paving the ground for future investigations in this field. Materials science ; Hydrogen ; Production (economics) ; Hydrogen production ; Physics Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Inorganic Chemistry and Materials
Authors: Eric Sivonxay | Lucas Attia | Evan Walter Clark Spotte‐Smith | Benjamin Lengeling | Xiaojing Xia | Daniel Barter | Emory M. Chan | Samuel M. Blau
Source: ChemRxiv (preprint, 2024-12-26)
Field: Materials Science — Materials Chemistry

Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core-shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in e.g., biosensing, super-resolution microscopy, and 3D printing, can emit visible and ultraviolet light from near-infrared excitations. We report the first large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use this data to train a heterogeneous graph neural network (GNN) using a novel representation of UCNP nanostructure. Applying gradient-based optimization on the trained GNN, we identify structures with 6.5x higher predicted emission under 800nm illumination than any UCNP in our training set. Our work reveals new design principles for UCNPs and presents a roadmap for DL-based inverse design of nanomaterials. Inverse ; Deep learning ; Nanoparticle ; Heterojunction ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology ; Mathematics ; Optoelectronics ; Geometry Machine Learning in Materials Science
Authors: Akshat Chaudhari | Chakradhar Guntuboina | Hongshuo Huang | Amir Barati Farimani
Source: arXiv (Cornell University) (preprint, 2024-03-28)
Field: Materials Science — Materials Chemistry

AlloyBERT: Alloy Property Prediction with Large Language Models The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based model designed to predict properties such as elastic modulus and yield strength of alloys using textual inputs. Leveraging the pre-trained RoBERTa encoder model as its foundation, AlloyBERT employs self-attention mechanisms to establish meaningful relationships between words, enabling it to interpret human-readable input and predict target alloy properties. By combining a tokenizer trained on our textual data and a RoBERTa encoder pre-trained and fine-tuned for this specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi Principal Elemental Alloys (MPEA) data set and 0.00611 on the Refractory Alloy Yield Strength (RAYS) dataset. This surpasses the performance of shallow models, which achieved a best-case MSE of 0.00025 and 0.0076 on the MPEA and RAYS datasets respectively. Our results highlight the potential of language models in material science and establish a foundational framework for text-based prediction of alloy properties that does not rely on complex underlying representations, calculations, or simulations. Property (philosophy) ; Alloy ; Natural language processing ; Computer science ; Materials science ; Metallurgy ; Philosophy ; Epistemology Machine Learning in Materials Science