jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matteo Guidetti | Rolf Hilfiker | Martin Kuentz | Annette Bauer‐Brandl | Fritz Blatter
Source: Crystal Growth & Design (article, 2022-12-23)
Field: Chemistry — Physical and Theoretical Chemistry

Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties of poorly water-soluble drug compounds. However, the successful discovery of new pharmaceutical cocrystals remains a time- and resource-consuming process. This study proposes the use of a combined computational-experimental high-throughput approach as a tool to accelerate and improve the efficiency of cocrystal screening exemplified by posaconazole. First, we employed the COSMOquick software to preselect and rank cocrystal candidates (coformers). Second, high-throughput crystallization experiments (HTCS) were conducted on the selected coformers. The HTCS results were successfully reproduced by liquid-assisted grinding and reaction crystallization, ultimately leading to the synthesis of thirteen new posaconazole cocrystals (7 anhydrous, 5 hydrates, and 1 solvate). The posaconazole cocrystals were characterized by PXRD, <sup>1</sup>H NMR, Fourier transform-Raman, thermogravimetry-Fourier transform infrared spectroscopy, and differential scanning calorimetry. In addition, the prediction performance of COSMOquick was compared to that of two alternative knowledge-based methods: molecular complementarity (MC) and hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC and COSMOquick show good discriminatory ability, suggesting their use as a potential virtual tool to improve cocrystal screening. Cocrystal ; Crystallization ; Posaconazole ; Chemistry ; Fourier transform infrared spectroscopy ; Combinatorial chemistry ; Hydrogen bond ; Organic chemistry ; Molecule ; Chemical engineering Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Enzyme Structure and Function
Authors: Silvia Pezzola | Samuele Tarallo | Alessandro Iannini | Mariano Venanzi | Pierluca Galloni | Valeria Conte | Federica Sabuzi
Source: Molecules (article, 2022-12-06)
Field: Chemistry — Organic Chemistry

An Accurate Approach for Computational pKa Determination of Phenolic Compounds Computational chemistry is a valuable tool, as it allows for in silico prediction of key parameters of novel compounds, such as pKa. In the framework of computational pKa determination, the literature offers several approaches based on different level of theories, functionals and continuum solvation models. However, correction factors are often used to provide reliable models that adequately predict pKa. In this work, an accurate protocol based on a direct approach is proposed for computing phenols pKa. Importantly, this methodology does not require the use of correction factors or mathematical fitting, making it highly practical, easy to use and fast. Above all, DFT calculations performed in the presence two explicit water molecules using CAM-B3LYP functional with 6-311G+dp basis set and a solvation model based on density (SMD) led to accurate pKa values. In particular, calculations performed on a series of 13 differently substituted phenols provided reliable results, with a mean absolute error of 0.3. Furthermore, the model achieves accurate results with -CN and -NO<sub>2</sub> substituents, which are usually excluded from computational pKa studies, enabling easy and reliable pKa determination in a wide range of phenols. Solvation ; Implicit solvation ; Quantitative structure–activity relationship ; Phenols ; Computational chemistry ; Computer science ; Chemistry ; Set (abstract data type) ; Density functional theory ; In silico ; Mean absolute error ; Range (aeronautics) ; Molecule ; Biological system ; Machine learning ; Materials science ; Mathematics ; Mean squared error ; Organic chemistry Free Radicals and Antioxidants ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Przemysław J. Jodłowski | Klaudia Dymek | Grzegorz Kurowski | Jolanta Jaśkowska | Wojciech Bury | Marzena Pander | Sylwia Wnorowska | Katarzyna M. Targowska‐Duda | Witold Piskorz | Artur Wnorowski | Anna Boguszewska‐Czubara
Source: ACS Applied Materials & Interfaces (article, 2022-06-14)
Field: Chemistry — Inorganic Chemistry

Zirconium-Based Metal–Organic Frameworks as Acriflavine Cargos in the Battle against Coronaviruses─A Theoretical and Experimental Approach In this study, we present a complementary approach for obtaining an effective drug, based on acriflavine (ACF) and zirconium-based metal-organic frameworks (MOFs), against SARS-CoV-2. The experimental results showed that acriflavine inhibits the interaction between viral receptor-binding domain (RBD) of spike protein and angiotensin converting enzyme-2 (ACE2) host receptor driving viral cell entry. The prepared ACF@MOF composites exhibited low (MOF-808 and UiO-66) and high (UiO-67 and NU-1000) ACF loadings. The drug release profiles from prepared composites showed different release kinetics depending on the local pore environment. The long-term ACF release with the effective antiviral ACF concentration was observed for all studied ACF@MOF composites. The density functional theory (DFT) calculations allowed us to determine that π-π stacking together with electrostatic interaction plays an important role in acriflavine adsorption and release from ACF@MOF composites. The molecular docking results have shown that acriflavine interacts with several possible binding sites within the RBD and binding site at the RBD/ACE2 interface. The cytotoxicity and ecotoxicity results have confirmed that the prepared ACF@MOF composites may be considered potentially safe for living organisms. The complementary experimental and theoretical results presented in this study have confirmed that the ACF@MOF composites may be considered a potential candidate for the COVID-19 treatment, which makes them good candidates for clinical trials. Materials science ; Zirconium ; Metal-organic framework ; Acriflavine ; Metal ; Battle ; Nanotechnology ; Coronavirus ; Coronavirus disease 2019 (COVID-19) ; Organic chemistry ; Metallurgy ; Chemistry ; Medicine Metal-Organic Frameworks: Synthesis and Applications ; Extraction and Separation Processes ; Advanced Nanomaterials in Catalysis
Authors: Bowen Zheng | Zeyu Zheng | Grace X. Gu
Source: npj Computational Materials (article, 2022-11-05)
Field: Materials Science — Materials Chemistry

Designing mechanically tough graphene oxide materials using deep reinforcement learning Abstract Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formulated as a sequential decision process, and policy-gradient RL models are employed to maximize the toughness of GO. Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs. In addition, our RL approach reaches optimized functional group distributions within only 5000 rollouts, while the simplest design task has 2 × 10 11 possibilities. Finally, we show that our approach is scalable in terms of the functional group density and the GO size. The present research showcases the impact of functional group distribution on GO properties, and illustrates the effectiveness and data efficiency of the deep RL approach. Reinforcement learning ; Graphene ; Scalability ; Toughness ; Reinforcement ; Task (project management) ; Oxide ; Materials science ; Computer science ; Density functional theory ; Artificial intelligence ; Nanotechnology ; Mathematical optimization ; Composite material ; Engineering ; Mathematics ; Systems engineering ; Physics Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Fuel Cells and Related Materials
Authors: María Godoy‐Gallardo | Maria Merino-Gómez | Luisamaria C. Matiz | Miguel A. Mateos‐Timoneda | F.J. Gil | Román A. Pérez
Source: ACS Biomaterials Science & Engineering (review, 2022-12-16)
Field: Materials Science — Biomaterials

Nucleoside-Based Supramolecular Hydrogels: From Synthesis and Structural Properties to Biomedical and Tissue Engineering Applications Supramolecular hydrogels are of great interest in tissue scaffolding, diagnostics, and drug delivery due to their biocompatibility and stimuli-responsive properties. In particular, nucleosides are promising candidates as building blocks due to their manifold noncovalent interactions and ease of chemical modification. Significant progress in the field has been made over recent years to allow the use of nucleoside-based supramolecular hydrogels in the biomedical field, namely drug delivery and 3D bioprinting. For example, their long-term stability, printability, functionality, and bioactivity have been greatly improved by employing more than one gelator, incorporating different cations, including silver for antibacterial activity, or using additives such as boric acid or even biomolecules. This now permits their use as bioinks for 3D printing to produce cell-laden scaffolds with specified geometries and pore sizes as well as a homogeneous distribution of living cells and bioactive molecules. We have summarized the latest advances in nucleoside-based supramolecular hydrogels. Additionally, we discuss their synthesis, structural properties, and potential applications in tissue engineering and provide an outlook and future perspective on ongoing developments in the field. Self-healing hydrogels ; Supramolecular chemistry ; Nanotechnology ; Drug delivery ; Biocompatibility ; Tissue engineering ; Materials science ; Scaffold ; Chemistry ; Biomedical engineering ; Molecule ; Organic chemistry ; Engineering ; Polymer chemistry Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery
Authors: Elena Kutumova | Ilya R. Akberdin | Ilya Kiselev | Ruslan Sharipov | Vera S. Egorova | Anastasiia O. Syrocheva | Alessandro Parodi | Andrey A. Zamyatnin | Fedor Kolpakov
Source: International Journal of Molecular Sciences (review, 2022-10-19)
Field: Materials Science — Biomaterials

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the "cords" of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body. Nanomedicine ; Computer science ; Biodistribution ; Risk analysis (engineering) ; Nanotechnology ; Systems engineering ; Engineering ; Medicine ; Nanoparticle ; Chemistry Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Alexander Kensert | Emery Bosten | Gilles Collaerts | Kyriakos Efthymiadis | Peter Van den Broeck | Gert Desmet | Deirdre Cabooter
Source: Journal of Chromatography A (article, 2022-03-31)
Field: Chemistry — Spectroscopy

Convolutional neural network for automated peak detection in reversed-phase liquid chromatography Convolutional neural network ; False positive paradox ; Artificial intelligence ; Pattern recognition (psychology) ; Chromatography ; Chemistry ; Set (abstract data type) ; Artificial neural network ; Computer science Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies
Authors: Marko Popovic
Source: Vaccines (review, 2022-12-09)
Field: Chemistry — Physical and Theoretical Chemistry

Biothermodynamics of Viruses from Absolute Zero (1950) to Virothermodynamics (2022) Biothermodynamics of viruses is among the youngest but most rapidly developing scientific disciplines. During the COVID-19 pandemic, it closely followed the results published by molecular biologists. Empirical formulas were published for 50 viruses and thermodynamic properties for multiple viruses and virus variants, including all variants of concern of SARS-CoV-2, SARS-CoV, MERS-CoV, Ebola virus, Vaccinia and Monkeypox virus. A review of the development of biothermodynamics of viruses during the last several decades and intense development during the last 3 years is described in this paper. Monkeypox ; Virology ; Vaccinia ; Virus ; Biology ; Pandemic ; Virus classification ; Coronavirus disease 2019 (COVID-19) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Smallpox ; Medicine ; Infectious disease (medical specialty) ; Vaccination ; Disease ; Genetics ; Genome thermodynamics and calorimetric analyses ; SARS-CoV-2 and COVID-19 Research ; Evolution and Genetic Dynamics
Authors: Chao-Tsung Yeh | Phuong Nguyen Thanh | Ming-Yuan Cho
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Real-Time Leakage Current Classification of 15kV and 25kV Distribution Insulators Based on Bidirectional Long Short-Term Memory Networks With Deep Learning Machine This paper presents an online monitoring system to classify the leakage/discharge current of the insulator in coastal sites using Bidirectional Long short-term memory (Bi-LSTM) models on a web-based service. The remote monitoring methodology uses environmental parameters to classify the peak level of leakage/discharge current of the 15kV and 25kV distribution insulators. The sequential weather data, the humidity, temperature, rainfall, dew point, solar illumination, wind speed, air pressure, and wind direction are automatically collected hourly in real-time and transferred to data servers. The hyperparameter optimization for the structure of Bi-LSTM is utilized through the grid search capability in a deep learning machine. The optimized design of Bi-LSTMs improves the performance and accuracy in predicting the leakage/discharge current classification for HDPE and SR of 15kV and 25kV insulators. Compared with persistent methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU), the Bi-LSTM has better performance and higher accuracy in predicting the leakage/discharge levels, which are utilized to evaluate the surface pollution of insulators. The optimized structure of the proposed Bi-LSTM model could achieve a maximum improvement of 49.529&#x0025; error, 12.761&#x0025; accuracy, 72.736&#x0025; error, and 36.641&#x0025; accuracy for training and validating data compared with other models. Moreover, a web-based service is developed for maintenance staff to interact with all current and predicted status of insulators. This online leakage current classification has been installed in Mailiao Township in Taiwan and could establish a reasonable maintenance mechanism for 15kV and 25kV distribution insulators. Computer science ; Recurrent neural network ; Artificial neural network ; Wind speed ; Hyperparameter ; Artificial intelligence ; Machine learning ; Dew point ; Deep learning ; Real-time computing ; Leakage (economics) ; Simulation ; Meteorology High voltage insulation and dielectric phenomena ; Water Systems and Optimization ; Water Quality Monitoring Technologies
Authors: Jin Li | Fugang Liu | Chang He | Feng Shen | Jian Ye
Source: Nanophotonics (article, 2022-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orthogonal gap‐enhanced Raman tags for interference‐free and ultrastable surface‐enhanced Raman scattering Spectral interference from backgrounds is not negligible for surface-enhanced Raman scattering (SERS) tags and often influences the accuracy and reliability of SERS applications. We report the design and synthesis of orthogonal gap-enhanced Raman tags (O-GERTs) by embedding alkyne and deuterium-based reporters in the interior metallic nanogaps of core-shell nanoparticles and explore their signal orthogonality as optical probes against different backgrounds from common substrates and media (e.g., glass and polymer) to related targets (e.g., bacteria, cancer cells, and tissues). Proof-of-concept experiments show that the O-GERT signals in the fingerprint region (200-1800 cm<sup>-1</sup>) are likely interfered by various backgrounds, leading to difficulty of accurate quantification, while the silent-region (1800-2800 cm<sup>-1</sup>) signals are completely interference-free. Moreover, O-GERTs show much higher photo and biological stability compared to conventional SERS tags. This work not only demonstrates O-GERTs as universal optical tags for accurate and reliable detection onto various substrates and in complex media, but also opens new opportunities in a variety of frontier applications, such as three-dimensional data storage and security labeling. Raman scattering ; Raman spectroscopy ; Interference (communication) ; Materials science ; Optoelectronics ; Nanomaterials ; Optics ; Nanotechnology ; Telecommunications ; Physics ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Orbital Angular Momentum in Optics
Authors: Tae Kyung Kim | Jeonghyun Hong | Daun Ryu | Sukyung Kim | Si Yeon Byeon | Woojin Huh | Kunhyo Kim | Gyu Heon Baek | Hyun Seok Kim
Source: Scientific Reports (article, 2022-03-19)
Field: Chemistry — Analytical Chemistry

Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs. Convolutional neural network ; Key (lock) ; Computer science ; Class (philosophy) ; Bark (sound) ; Tree (set theory) ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Computational biology ; Biology ; Forestry ; Geography ; Mathematics ; Combinatorics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Qingsong Wu | Lijia Xu | Zhiyong Zou | Jian Wang | Qifeng Zeng | Qianlong Wang | Jiangbo Zhen | Yuchao Wang | Yongpeng Zhao | Man Zhou
Source: Frontiers in Plant Science (article, 2022-11-10)
Field: Chemistry — Analytical Chemistry

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields. Hyperspectral imaging ; Boosting (machine learning) ; Artificial intelligence ; Gradient boosting ; Machine learning ; Feature selection ; Extreme learning machine ; Pattern recognition (psychology) ; Computer science ; Support vector machine ; Mathematics ; Random forest ; Artificial neural network Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research