jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christoph Lange | Isabel Thiele | Lara Santolin | Sebastian L. Riedel | M. Borisyak | Peter Neubauer | Mariano Nicolás Cruz Bournazou
Source: arXiv (Cornell University) (preprint, 2024-02-01)
Field: Chemistry — Analytical Chemistry

Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations In biotechnology Raman Spectroscopy is rapidly gaining popularity as a process analytical technology (PAT) that measures cell densities, substrate- and product concentrations. As it records vibrational modes of molecules it provides that information non-invasively in a single spectrum. Typically, partial least squares (PLS) is the model of choice to infer information about variables of interest from the spectra. However, biological processes are known for their complexity where convolutional neural networks (CNN) present a powerful alternative. They can handle non-Gaussian noise and account for beam misalignment, pixel malfunctions or the presence of additional substances. However, they require a lot of data during model training, and they pick up non-linear dependencies in the process variables. In this work, we exploit the additive nature of spectra in order to generate additional data points from a given dataset that have statistically independent labels so that a network trained on such data exhibits low correlations between the model predictions. We show that training a CNN on these generated data points improves the performance on datasets where the annotations do not bear the same correlation as the dataset that was used for model training. This data augmentation technique enables us to reuse spectra as training data for new contexts that exhibit different correlations. The additional data allows for building a better and more robust model. This is of interest in scenarios where large amounts of historical data are available but are currently not used for model training. We demonstrate the capabilities of the proposed method using synthetic spectra of Ralstonia eutropha batch cultivations to monitor substrate, biomass and polyhydroxyalkanoate (PHA) biopolymer concentrations during of the experiments. Raman spectroscopy ; Scheme (mathematics) ; Spectral line ; Computer science ; Data mining ; Physics ; Mathematics ; Optics ; Quantum mechanics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Time Series Analysis and Forecasting ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kirsten Lina | Michael S. Lodge | Javier A. González | Kenji Watanabe | Takashi Taniguchi | Robert E. Peale | Masa Ishigami
Source: MRS Advances (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Annotation labeling technique for machine learning-enhanced optical characterization of hexagonal boron nitride thickness on 300-nm oxide substrate Materials science ; Hexagonal boron nitride ; Characterization (materials science) ; Substrate (aquarium) ; Hexagonal crystal system ; Oxide ; Nitride ; Boron nitride ; Nanotechnology ; Optoelectronics ; Chemical engineering ; Graphene ; Metallurgy ; Crystallography Graphene research and applications ; 2D Materials and Applications ; Nanowire Synthesis and Applications
Authors: Prutha Varude | D. Satish
Source: Journal of Computational Biophysics and Chemistry (article, 2024-12-20)
Field: Materials Science — Biomaterials

Integrative Computational Design of PD-L1 Inhibitors, Utilizing MD Simulations and DFT Studies to Guide Cancer Treatment through Atomic and Electronic Properties The emphasis on immunological checkpoints, including Programmed Death-Ligand1 (PD-L1), is considered a critical strategy for enhancing cancer treatment in global cancer research. PD-L1 plays a pivotal role in enabling cancer cells to evade the immune system, thereby representing a primary target for cancer immunotherapy. Therefore, this study employs in silico methods to identify the small molecule inhibitors against PD-L1 protein. A machine-learning-based QSAR model was used to screen 412,903 natural compounds conjugated with Lipinski’s drug-likeness properties and toxicity evaluations. The Gradient Boosting Regressor was selected for the screening, which had the highest correlation of [Formula: see text] value (0.709) and lowest MSE (0.321) with the experimental binding, indicating its accurate binding affinity predictions. Screened compounds proceeded under additional evaluation using molecular docking and Density Functional Theory (DFT), resulting in the identification of the two most potential binders. Moreover, the study employed Molecular Dynamics (MDs) simulations and binding free energy calculations (MM/GBSA) to evaluate the compounds’ ability to bind with PD-L1, potentially impeding its interaction with PD-1. Based on the results, it was observed that compound-2 consistently and reliably interacts with PD-L1. In order to assess the robustness and stability of the protein-ligand complex of compound-2 and PD-L1, the simulations were executed in triplicates, each lasting 100 ns. Compound-2 showed sufficient hydrogen bonding with functional residues of the protein, including Asp122(A), Tyr56(B) and Tyr56(A), which are crucial for the stability and inhibition of PD-L1. Additionally, compound-2 obtained the most favorable binding free energy (−13.74 kcal/mol). Overall, the study suggested that compound 2 has the ability to hinder the interaction between PD-L1 and PD-1, hence enhancing the immune response against cancer cells. Additional experimental studies into the application of compound 2 in cancer immunotherapy could yield supportive results. Computational chemistry ; Computer science ; Chemistry Nanoparticle-Based Drug Delivery
Authors: Shen Yan | Bernard Ciraulo | Dejana Jovanovic | Dina Hochuli | Veronica Akle Alvarez | Özgün Özalp | Stephan C. F. Neuhauss | Amin Allalou | Martin Baumgartner
Source: Neuro-Oncology (article, 2024-06-18)
Field: Materials Science — Biomaterials

MODL-09. IMAGE-BASED SCREENING IN MEDULLOBLASTOMA REVEALS TUMOR-SUPPRESSIVE EFFECTS OF THE NEUROPROTECTIVE AGENT URMC-12K Abstract BACKGROUND Enhanced motility and the invasive behavior of medulloblastoma (MB) influence microenvironment interactions and may promote distant spread. Current drug screening focuses on cytotoxic agents, leaving subtle but potentially therapeutically relevant drug phenotypes associated with motility and invasiveness unrecognized. The serine/threonine kinase MAP4K4 is upregulated in MB and promotes invasiveness1,2. Toxicities associated with existing MAP4K4 inhibitors in the CNS prevented further pre-clinical evaluation. URMC-12k is a novel MAP4K inhibitor identified as a motor-neuron-protective agent in ALS. Using advanced imaging approaches, we investigated URMC-12k as a tumor-suppressive drug in MB and characterized its effects at the molecular, cellular, and tissue levels. METHODS We use cell-based 3D invasion assays and quantitative microscopy to assess tumor-restraining activities of URMC-12k, and machine learning to detect URMC-12k phenotypes associated with repressed motility. We use tissue and zebrafish larval models to explore URMC-12k effects on cell and tissue viability and anti-invasion efficacy. We determined URMC-12k targets in MB cells using phosphoproteomics. RESULTS URMC-12k outcompetes existing MAP4K4 inhibitors in repressing basal and growth-factor-induced matrix invasion with no detectable effects on vertebrate developmental processes. A computational model trained to detect morphological alterations in the actin cytoskeleton associated with cell dynamics established a firm correlation between the URMC-12k-repressed motile behavior and alterations in the tumor cell actin cytoskeleton. Quantitative imaging further revealed that URMC-12k promoted increased cell clustering and reduced deposition of extracellular vesicles. Phosphoproteomics identified proteins involved in cell adhesion organization and vesicle trafficking as potential molecular effectors targeted by URMC-12k. CONCLUSIONS URMC-12k effectively inhibits MB cell migration and displays no toxicities at effective concentrations. The regulation of cell-cell adhesion molecules and the deposition of extracellular vesicles revealed by URMC-12k are tumor-promoting, druggable mechanisms of MAP4Ks in MB. 1. 1Migliavacca, J., et al. Commun Biol5, (2022). 2. 2Tripolitsioti, D. et al. Oncotarget9, 23220–23236 (2018). Medulloblastoma ; Neuroprotection ; Medicine ; Internal medicine ; Neuroscience ; Cancer research ; Psychology Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment
Authors: Gaoyang Xiong | Haining Ji | Yongxing Chen | Bin Liu | Yi Wang | Long Peng | Jinfang Zeng | Jundong Tao | Cong Deng
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning Vanadium dioxide ; Thermochromism ; Vanadium ; Materials science ; Chemical engineering ; Nanotechnology ; Metallurgy ; Chemistry ; Thin film ; Engineering ; Organic chemistry Transition Metal Oxide Nanomaterials ; Catalysis and Oxidation Reactions ; Gas Sensing Nanomaterials and Sensors
Authors: Nisha Vashishat | R. S. Rimal Isaac
Source:  (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Advanced Toxicity Assessment: A BiLSTM Approach for Nanoparticle Safety Nanotoxicity prediction is crucial for safeguarding human health and the environment due to the unique properties of nanoparticles (NPs) that lead to unforeseen biological interactions. With growing applications of nanomaterials in consumer products, pharmaceuticals, and industry, early detection of their toxic effects is vital for risk assessment, regulatory compliance, and the informed design of safer materials. In this study, a deep learning (DL) model, Bidirectional Long Short-Term Memory (BiLSTM), is proposed for the effective prediction of nanoparticle toxicity. Utilizing a publicly available dataset, the model analyzes physicochemical attributes of various NPs, including Al203, Fe203, CuO,Ti02, and ZnO. The dataset encompasses 476 toxic and 405 non-toxic samples, with features such as core size, surface area, and exposure time. The methodology involves rigorous data preprocessing, including outlier detection using Z-scores and one-hot encoding for categorical variables. The BiLSTM architecture processes data in both forward and backward directions, effectively capturing temporal dependencies and enhancing toxicity prediction accuracy. The model's performance was evaluated, achieving remarkable 98.28% accuracy, 98.01 % precision, 97.82% recall, and 97.91 % Fl-score. Additionally, the ROC curve demonstrated excellent discrimination between classes, with an AUC of 0.96. The findings highlight the potential of DL in advancing nanotoxicity prediction, thereby contributing to safer applications of nanotechnology. Toxicity ; Computer science ; Nanoparticle ; Nanotechnology ; Chemistry ; Materials science Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications ; Carbon Nanotubes in Composites
Authors: Gabriel César Pereira | Rogério Custódio
Source: Journal of Chemical Theory and Computation (article, 2024-12-12)
Field: Chemistry — Organic Chemistry

A Preliminary Neural Network-Based Composite Method for Accurate Prediction of Enthalpies of Formation A composite method, named ANN-G3S, is introduced, adapting from G3S theory and employing distinct sets of multiplicative scale factors. An artificial neural network (ANN)-based classification model is utilized to select optimal sets of four scale factors for electronic correlation and basis set expansion terms in electronic systems. The correlation and basis set terms are scaled by four parameters, two for atoms and the other two for molecules. The ANN model is trained on the G3/05 test set to identify the best parameter set for each electronic system. To validate the method, 10% of the structures from the test set are randomly excluded from training and optimization, forming a separate validation set. The method demonstrates a mean deviation of 1.11 kcal mol<sup>-1</sup> for the G3/05 set and 0.89 kcal mol<sup>-1</sup> for the validation set, close to the value presented by the G4 method and surpassing the accuracy of the G3 method of 1.19 kcal mol<sup>-1</sup> with significantly reduced computational cost. This method shows advantages by eliminating the need for purely empirical corrections, thereby enhancing both efficiency and accuracy in predicting heats of formation. Artificial neural network ; Composite number ; Computer science ; Machine learning ; Data mining ; Artificial intelligence ; Biological system ; Algorithm Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Phase Equilibria and Thermodynamics
Authors: Andre Karius | Johanna Grigo | Vratislav Strnad | Claudia Schweizer | Ricarda Merten | Florian Putz | Rainer Fietkau | Christoph Bert
Source: Brachytherapy (article, 2024-10-25)
Field: Chemistry — Inorganic Chemistry

PO0408 Medicine ; Family medicine Inorganic Chemistry and Materials ; Intermetallics and Advanced Alloy Properties
Authors: Diego Vicente Moreno Rodríguez | KENYI JAVIER CALDERÓN SÁNCHEZ | Daniel Ricardo Izquierdo Peña | Danny Wilson Sanjuanelo Corredor
Source: Repositorio - Universidad de Ciencias Aplicadas y Ambientales UDCA. (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Kaxan: Un programa de cómputo para la Búsqueda de Caminos de reacción para la descomposición de Compuestos Orgánicos Volátiles El estudio de mecanismos de reacción lleva consigo un importante esfuerzo computacional y humano, al intentar construir rutas que puedas explicar los fenómenos y barreras de reacción observadas, el estudio de estos caminos de reacción implica un esfuerzo computacional importante, además de tiempo computacional y humano en la caracterización de estos caminos. Sin embargo no se garantiza que sea posible estudiar todos las posible rutas de reacción. En este sentido se desarrollo el programa KAXAN. KAXAN es un programa que nace como la necesidad de encontrar las estructuras químicas de un compuesto permitiendo la exploración de la superficie de energía potencial molecular para dar cuenta de las posibles transformaciones de los compuestos generados uniendo los diferentes isómeros o fragmentos del compuesto en rutas químicas llamadas mecanismos de reacción. En un proceso automático empleando aprendizaje de máquina (Machine Learning), el programa genera posibles caminos de degradación para un estequeometría dada por el usuario. El programa se encuentra en etapa de evaluación/producción, en la obtención de los puntos críticos para las posibles rutas de degradación, el programa cuenta con algoritmos para la evaluación de la similaridad molecular y así aumentar la diversidad, Un algoritmo genético que permite la evaluación de los posibles puntos críticos sobre la superficie de energía potencial molecular. El programa está escrito en lenguaje FORTRAN 90/2003 y algunas rutinas en lenguaje C. Context (archaeology) Machine Learning in Materials Science ; History and advancements in chemistry ; Chemistry and Chemical Engineering
Authors: M. Bouziani Idrissi | Idriss Moumen | Sara Taghzouti | Koray Sayın | El Mahjoub Chakir | H. Zarrok | H. Oudda
Source: Current Analytical Chemistry (article, 2024-09-05)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning for QSPR Modeling of Corrosion Inhibitors in HCl for Mild Steel Protection Background: The corrosion of Mild Steel (MS) in harsh acidic environments, such as Hydrochloric acid (HCl), is a significant industrial issue with environmental consequences. Corrosion inhibitors, particularly those containing heteroatoms and aromatic rings, are a proven method for mitigating corrosion. Traditional methods for studying corrosion inhibitors often require resource- intensive experiments. Methods: This study explores the use of Quantitative Structure-Property Relationship (QSPR) modeling, a Machine Learning (ML) technique, to predict the inhibition efficiency of organic corrosion inhibitors in HCl environments. Several ML models were employed: Linear Regression (LR), Random Forest Regression (RF), Support Vector Regression (SVR), Multilayer Perceptron Regression (MLP), and XGBoost Regression (XGB). Results: The investigation revealed that some models achieved exceptional predictive accuracy with significantly reduced errors and high precision. These models offer a promising avenue for efficient corrosion inhibitor design, reducing reliance on extensive experimentation. Conclusion: This study contributes to the advancement of corrosion science and materials engineering by introducing innovative strategies for developing effective corrosion inhibitors using machinelearning- driven QSPR models. Quantitative structure–activity relationship ; Corrosion ; Combinatorial chemistry ; Chemistry ; Computer science ; Biochemical engineering ; Artificial intelligence ; Engineering ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: S. Dogra | Sandeep Kumar Dhanda
Source:  (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Investigating the Underlying Molecular Patterns in Aggressive Brain Tumor ETMR to Develop a Robust Diagnostic Prediction Model Rare, aggressive pediatric brain tumor Embryonal tumor with multilayered rosettes (ETMR) has been minimally researched and is frequently misdiagnosed, reducing chances of survival. While ETMR is characterized by a general amplification in the C19MC gene cluster, this isn't a unique molecular identifier. This study aims to differentiate ETMR from other brain tumors through specific gene amplification patterns within C19MC and development of a robust diagnostic model. Various publicly available methylation patient data was compiled and fine-tuning methods to reduce model memorization and optimize the limited available data. The most amplified genes in the C19MC cluster and genes with high feature-importance from the model were found to consist of oncogenes controlling ETMR indicators. Many tumor suppressor genes were also present, potentially due to their deactivation from hypermethylation. These amplifications provide more specific hallmarks and potential targets to enhance therapeutic treatment. Additionally, testing an unseen dataset on the diagnostic model after training with fivefold cross validation revealed a 0.932 Area Under the Curve (AUC), 0.916 specificity, and 0.833 sensitivity to differentiate between children with or without ETMR from epigenetic factors. This novel machine learning application can assist physicians with prompt identification and therefore reduced mortality rates of ETMR. Computer science ; Artificial intelligence ; Natural language processing Machine Learning in Materials Science
Authors: Rui Pimentel de Figueiredo | Stefan Nordborg | Simon Bøgh | Ignacio Rodríguez
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Complete System for Automated Semantic-Geometric Mapping of Corrosion in Industrial Environments Corrosion ; Computer science ; Artificial intelligence ; Metallurgy ; Materials science Material Properties and Failure Mechanisms ; Optics and Image Analysis ; Engineering Diagnostics and Reliability
Authors: Tong Zhao | Zhencai Shen | Ping Zhong | Hui Zou | Mingming Han
Source: Environmental Science and Pollution Research (article, 2024-01-04)
Field: Chemistry — Analytical Chemistry

Detection and prediction of pathogenic microorganisms in aquaculture (Zhejiang Province, China) Computer science ; Warning system ; Microorganism ; Artificial intelligence ; Biology Spectroscopy and Chemometric Analyses ; Machine Learning in Bioinformatics ; Identification and Quantification in Food
Authors: Miroslava Nedyalkova | Diana Heredia | Marco Lattuada | Joaquı́n Barroso-Flores
Source: ChemRxiv (preprint, 2024-07-02)
Field: Chemistry — Electrochemistry

pKa predictions for arsonic acid derivatives. Contrasting ML and DFT-based models Food, water, air, and soil are regularly contaminated with natural and artificially occurring forms of arsenic, from which, arsonic acid derivatives RAsO(OH)2 are the major pentavalent compounds present in aqueous media. At a given pH, the resulting ionization state for these derivatives affects their lipophilicity, solubility, protein binding, and their ability to cross plasma membranes, potentially increasing their toxicity. Knowing their pKa values not only characterizes them but helps design a specific strategy for their bioremediation. There are numerous challenges associated with predicting pKa, and existing models are limited to certain chemical spaces. To leverage a pKa model for arsonic acids, we contrast machine learning (ML) methods based in Support Vector Machine and three DFT-based models: correlation to the maximum surface electrostatic potential (VS,max) at the ωB97XD/cc-pVTZ level of theory; correlation to carboxylate atomic charges in conjunction with a density-based solvation model (SMD) at the level of M06L/6-311G(d,p); and the scaled solvent-accessible surface approach, which yielded high mean unsigned errors for predicted pKa, and therefore it is not an efficient method for calculating the pKas of arsenic acids, in contrast with reported data for carboxylic acids, aliphatic amines, and thiols. The highest agreement was obtained with the atomic charges calculation on the conjugated arsonate base. ML based and Vs,max models rank second and third, respectively, in terms of prediction performance. Protonation ; Lipophilicity ; Acid dissociation constant ; Chemistry ; Polar surface area ; Solvation ; Leverage (statistics) ; Computational chemistry ; Carboxylate ; Density functional theory ; Aqueous solution ; Solvent ; Organic chemistry ; Computer science ; Molecule ; Machine learning ; Ion Electrochemical Analysis and Applications ; Free Radicals and Antioxidants ; Chemistry and Chemical Engineering