jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mahesh Kumar N | Aruna Priya | Shachindra L. Nargund | Sharmila A. Gote
Source: International Journal of Pharmaceutical Research and Development (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Benzimidazole derivatives in oncology: A review of molecular targets and therapeutic potential in clinical trial Cancer continues to be a leading global health burden, prompting the need for innovative therapeutic strategies that address treatment resistance, toxicity, and specificity. Benzimidazole and its derivatives have emerged as promising pharmacophores in oncology due to their versatile biological activities, including kinase inhibition, microtubule disruption, and modulation of apoptosis pathways. This review explores recent advances in the development of benzimidazole-based compounds targeting various cancers, such as glioblastoma, colorectal cancer, leukaemia, breast, and lung malignancies. Notable examples include inhibitors of the neddylation pathway, tubulin-binding agents, and kinase-targeting molecules, which demonstrate potent in vitro and in vivo efficacy. Several benzimidazole derivatives, such as compound 12b and 7c, exhibited low nanomolar cytotoxicity and overcame drug resistance mechanisms, particularly in paclitaxel- and EGFR-TKI-resistant models. Additionally, benzimidazole–triazole hybrids have shown dual functionality by serving as both cytotoxic agents and PET imaging probes through Galectin-1 targeting. The integration of computational modelling, cell line assays, molecular docking, and clinical trials underlines the translational relevance of these scaffolds. Collectively, benzimidazoles represent a promising platform for developing next-generation anticancer therapeutics and diagnostic agents, particularly in the context of precision medicine. Clinical trial ; Medicine ; Benzimidazole ; Oncology ; Internal medicine ; Medical physics ; Chemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis of Tetrazole Derivatives
Authors: Kim, Yong-Woon | Kang, Chulung | Byun, Yung-Cheol
Source: ArXiv.org (article, 2025-11-08)
Field: Materials Science — Materials Chemistry

Physics-Informed Neural Networks for Real-Time Gas Crossover Prediction in PEM Electrolyzers: First Application with Multi-Membrane Validation Green hydrogen production via polymer electrolyte membrane (PEM) water electrolysis is pivotal for energy transition, yet hydrogen crossover through membranes threatens safety and economic viability-approaching explosive limits (4 mol% H$_2$ in O$_2$) while reducing Faradaic efficiency and accelerating membrane degradation. Current physics-based models require extensive calibration and computational resources that preclude real-time implementation, while purely data-driven approaches fail to extrapolate beyond training conditions-critical for dynamic electrolyzer operation. Here we present the first application of physics-informed neural networks (PINNs) for hydrogen crossover prediction, trained on 184 published measurements augmented to 1,114 points and constrained by a constitutive physics model (Henry's law, Fick's diffusion, and Faraday-based gas production) embedded in the loss function. Our compact architecture (17,793 parameters), validated across six membranes under industrially relevant conditions (0.05-5.0 A/cm$^2$, 1-200 bar, 25-85°C), achieves exceptional accuracy (R$^2$ = 99.84% $\pm$ 0.15%, RMSE = 0.0932% $\pm$ 0.0438%) based on five-fold cross-validation, with sub-millisecond inference enabling real-time control. Remarkably, the model maintains R$^2$ > 86% when predicting crossover at pressures 2.5x beyond training range-substantially outperforming pure neural networks (R$^2$ = 43.4%). The hardware-agnostic deployment, from desktop CPUs to edge devices (Raspberry Pi 4), enables distributed safety monitoring essential for gigawatt-scale installations. By bridging physical rigor and computational efficiency, this work establishes a new paradigm for real-time electrolyzer monitoring, accelerating deployment of safe, efficient green hydrogen infrastructure crucial for net-zero emissions targets. Crossover ; Polymer electrolyte membrane electrolysis ; Artificial neural network ; Energy carrier ; Software deployment ; Electrolysis of water ; Bridging (networking) Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Electrocatalysts for Energy Conversion
Authors: Niki Vergadou | Vassilios Constantoudis
Source: ChemPlusChem (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches in Soft Matter Molecular Simulation and Materials Characterization: Challenges and Perspectives Machine learning (ML) techniques are currently investigated for their potential applicability in a wide range of disciplines and scientific domains as a powerful extension to existing state-of-the-art experimental and computational methods. The diverse scientific areas within the materials science field can largely benefit from the development of data-driven methods in the present era of advanced ML computational algorithms, efficient, and optimized hardware and large amounts of produced information. In this perspective, basic concepts are introduced and representative advances are showcased from the standpoint of materials characterization and soft matter molecular simulation. Prerequisites and challenges are discussed toward the construction of sound and efficient ML-aided approaches that can contribute via new auxiliary routes to fundamental understanding and thus facilitate scientific discovery and technological applications. Field (mathematics) ; Characterization (materials science) ; Range (aeronautics) ; Scientific field ; Soft materials ; Soft computing ; Soft matter Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Olena WELCHINSKA | Iryna NIZHENKOVSKA
Source: University science (article, 2025-05-15)
Field: Materials Science — Polymers and Plastics

CHROMATOGRAPHIC STUDIES OF DRUG SUBSTANCES: A CASE STUDY IN UNIVERSITY SCIENTIFIC ACTIVITIES Studying natural sciences at the O.O. Bogomolets National Medical University opens up for students the opportunity to become familiar with advanced scientific discoveries, the prospect of direct participation in conducting experimental research in drug quality control laboratories. The Case study method is used to investigate a problem in a specific context and discuss how the results relate to theory. Case study is actively used in chromatographic studies of medicinal substances, during which ways of solving pharmaceutical analysis problems. The main stages of scientific experimental work according to the requirements of the Case study are determining the purpose of the study with the motivation for the need to carry out this study, motivated selection of samples for the study, drawing up a research plan with a deadline, systematization of the obtained data. In chromatographic studies, an important role is played by the selection of a large number of objects and studies, which provides a high expected increase in information and results. In chemical experiments, it is advisable to use the design of single and multiple studies to compare the results obtained with standards and the design of establishing "anomalies" to register the formation of substances. During chromatographic studies of each specific pharmaceutical object, complex approaches are developed in the analysis of substances, since similar trends are often observed in chromatographic separation, the formation of chemical degradation products. The current task remains the implementation of the Case Study learning model in the performance of scientific work by students by HPLC method. Context (archaeology) ; Selection (genetic algorithm) ; Task (project management) ; Quality (philosophy) ; Control (management) ; Plan (archaeology) ; Drug ; Work (physics) Polymer Synthesis and Characterization ; Chemical synthesis and pharmacological studies ; Medical and Biological Sciences
Authors: Salman N. Salman | Sergey A. Shteingolts | Ron Levie | Dan Mendels
Source: ArXiv.org (preprint, 2025-05-27)
Field: Materials Science — Materials Chemistry

Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks Machine learning models often require large datasets and struggle to generalize beyond their training distribution. These limitations pose significant challenges in scientific and engineering contexts, where generating exhaustive datasets is often impractical and the goal is frequently to discover novel solutions outside the training domain. In this work, we explore the use of dynamical data through a graph neural network-based simulator to enable efficient system-to-property learning and out-of-distribution prediction in the context of uniaxial compression of two-dimensional disordered elastic networks. We find that the simulator can learn the underlying physical dynamics from a small number of training examples and accurately reproduce the temporal evolution of unseen networks. Notably, the simulator is able to accurately predict emergent properties such as the Poisson's ratio and its dependence on strain, even though it was not explicitly trained for this task. In addition, it generalizes well across variations in system temperature, strain amplitude, and most significantly, Poisson's ratios beyond the training range. These findings suggest that using dynamical data to train machine learning models can support more data efficient and generalizable approaches for materials and molecular design, especially in data-scarce settings. Context (archaeology) ; Graph ; Artificial neural network ; Training set ; Dynamical systems theory ; Physical system ; Experimental data ; Training (meteorology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Vyas Mitali Virendrabhai
Source:  (article, 2025-04-11)
Field: Chemistry — Organic Chemistry

POTENTIAL NICOTINIC RECEPTOR ANTAGONISTS: CHEMISTRY, RATIONALE, AND RESEARCH ROADMAP Background: Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels that regulate a wide array of physiological functions, such as cognition, pain, and autonomic functions. Their pathological activity has been associated with numerous disorders, including addiction, neurodegenerative diseases, and chronic pain. In spite of the nAChR's central role in physiological and pathological conditions, there are few effective and selective nAChR antagonists under development, primarily because of the variety of receptor subtypes and complex functional roles. Objective: The objective of this study is to examine the current state of chemical agents acting as nicotinic receptor antagonists, to provide the scientific rationale for their development, and to establish a roadmap for moving forward in future research towards antagonists for clinical use. Methods: A thorough review of literature was conducted that looked at chemical structure, binding characteristics, and functional effects of existing and potential new nicotinic receptor antagonists. Comparisons were then made of selective receptor subtype properties, pharmacological properties, and possible use as a treatment to illustrate trends and obstacles. Results: Antagonistic activity at various nAChR subtypes is exhibited by diverse chemical classes including natural alkaloids, synthetic small molecules, or peptide-based components. Receptor modeling and high-throughput screening techniques have increased the number of selective antagonists; however, clinical translation is still hampered by low bioavailability, non-specific binding, and ever-changing subtype characterization. Conclusions: The evolution of selective nicotinic receptor antagonists is still a promising but complex one, and it would be beneficial to incorporate structural and pharmacological research progress to inform a targeted research agenda that will focus on specificity, improved drug-like properties, and stronger preclinical assessment in an effort to expedite the innovation of therapeutics. Nicotinic agonist ; Nicotinic acetylcholine receptor ; Receptor ; Alpha-4 beta-2 nicotinic receptor ; Preclinical research ; Acetylcholine receptor ; Ganglion type nicotinic receptor Synthesis and Biological Evaluation ; Free Radicals and Antioxidants
Authors: Onuh Matthew Ijiga | Olanrewaju Awoyemi | Fadeke Adeola Atobatele | Chioma Angela Okonkwo
Source: Engineering and Technology Journal (article, 2025-07-30)
Field: Chemistry — Physical and Theoretical Chemistry

Integrating Chemistry and Social Studies: Teaching the Impact of Chemical Advances on Society This paper explores the integration of chemistry and social studies in education to provide a comprehensive understanding of chemical advancements' historical, social, and ethical implications. It highlights the significance of interdisciplinary education, emphasizing how key chemical discoveries have influenced societal structures and public policies. The paper discusses effective teaching methods, such as project-based learning, thematic instruction, and collaborative teaching, to facilitate interdisciplinary learning. Examples of integrated curriculum and lesson plans are provided to illustrate practical implementation. The paper also addresses chemical developments' social and ethical considerations, advocating for responsible scientific practices and the importance of ethical awareness. Recommendations for educators on implementing interdisciplinary teaching strategies are presented, aiming to enrich students' learning experiences and foster critical thinking and problem-solving skills. Ultimately, this approach prepares students to navigate and address complex real-world challenges, promoting informed and responsible global citizenship. Chemistry ; Environmental chemistry ; Engineering ethics ; Engineering physics ; Nanotechnology ; Engineering ; Materials science Various Chemistry Research Topics
Authors: Weina Ke | Rui He | Mark A. Jensen | Marina A. Dobrovolskaia
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2025-07-01)
Field: Materials Science — Materials Chemistry

Transforming Cancer Nanotechnology Data Analysis and User Experience. Part <scp>II</scp>: Providing Future Solutions Using Large Language Models The advances in cancer nanotechnologies and current efforts focused on data sharing, along with the associated challenges, have been summarized in the first part of this review. Herein, we explore the potential of Large Language Models (LLMs) to enhance user experience, using the federally funded data repository caNanoLab as a case study. By training the LLM on caNanoLab data, we demonstrate its ability to provide more comprehensive search results, well-guided data entry, and a personalized, assisted search experience. We also discuss how integrating LLMs would optimize user experience, making it easier for researchers to navigate complex data and find relevant information. Our findings suggest that LLMs have significant potential to transform cancer nanotechnology data analysis and user experience, potentially paving the way for more efficient and effective advancements in cancer research. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Emerging Technologies. Data sharing ; Data science ; Nanotechnology ; Computer science ; Engineering ; Medicine ; Materials science ; Alternative medicine Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Misinformation and Its Impacts
Authors: Hao Xu | Yuntian Chen | Dongxiao Zhang
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery Constitutive models are fundamental to solid mechanics and materials science, underpinning the quantitative description and prediction of material responses under diverse loading conditions. Traditional phenomenological models, which are derived through empirical fitting, often lack generalizability and rely heavily on expert intuition and predefined functional forms. In this work, we propose a graph-based equation discovery framework for the automated discovery of constitutive laws directly from multisource experimental data. This framework expresses equations as directed graphs, where nodes represent operators and variables, edges denote computational relations, and edge features encode parametric dependencies. This enables the generation and optimization of free-form symbolic expressions with undetermined material-specific parameters. Through the proposed framework, we have discovered new constitutive models for strain-rate effects in alloy steel materials and the deformation behavior of lithium metal. Compared with conventional empirical models, these new models exhibit compact analytical structures and achieve higher accuracy. The proposed graph-based equation discovery framework provides a generalizable and interpretable approach for data-driven scientific modelling, particularly in contexts where traditional empirical formulations are inadequate for representing complex physical phenomena. Constitutive equation ; Parametric statistics ; Generalizability theory ; Underpinning ; Relation (database) ; Continuum mechanics ; Experimental data ; Physical law ; Scientific discovery Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Elasticity and Material Modeling
Authors: Lucija Božičević | Karolina Jagiełło | Anita Sosnowska | M. Stępnik | Mária Dušinská | Iseult Lynch | Nikolina Peranić | Ivona Capjak | Valérie Fessard | Mihaela Roxana Cimpan | Arno C. Gutleb | Elise Rundén‐Pran | Tomasz Puzyn | Ivana Vinković Vrček
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2025-09-01)
Field: Materials Science — Materials Chemistry

Sex and Gender Dimensions in Hazard and Risk Assessment of Engineered Nanomaterials The knowledge on hazards and risks connected to human exposure to engineered nanomaterials (ENMs) is still very limited, despite several decades of research and regulatory efforts at the international level. In particular, sex/gender-related responses to such exposure have not been clearly articulated so far in any of the existing guidance documents or regulatory relevant opinions provided to the parties involved in the risk assessment and risk management of ENMs. We aimed to demonstrate the relevance of the sex/gender dimension for the characterization of the risks and hazards associated with ENMs by analyzing existing scientific data on sex-related differences in response to ENMs exposure. This was achieved by performing an extensive review of in vivo mammalian toxicity studies published in PubMed and Web of Science databases. Further analysis was performed only for data reported in publications that satisfied scientific quality criteria assessed using the GUIDEnano approach. Finally, we demonstrated the importance of the sex/gender dimension for safety testing of ENMs in the future and provided recommendations on how to include the sex/gender dimension in toxicity testing of ENMs to ensure precise, transparent, and reliable conclusions in the process of hazard and risk assessments. This article is categorized under: Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Toxicology and Regulatory Issues in Nanomedicine > Regulatory and Policy Issues in Nanomedicine. Risk assessment ; Hazard ; Relevance (law) ; Regulatory science ; Hazard analysis ; Risk management ; Nanomedicine Nanoparticles: synthesis and applications ; Science, Research, and Medicine
Authors: Salman N. Salman | Sergey A. Shteingolts | Ron Levie | Dan Mendels
Source: The Journal of Chemical Physics (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Evaluating the use of a machine learning simulator for structure–property prediction: A case study on disordered elastic networks Machine learning models often require large datasets and struggle to generalize beyond their training distribution. These limitations pose significant challenges in scientific and engineering contexts, where generating exhaustive datasets is often impractical and the goal is to frequently discover novel solutions outside the training domain. In this work, we explore the use of dynamical data through a graph neural network-based simulator to enable efficient system-to-property learning and out-of-distribution prediction in the context of uniaxial compression of two-dimensional disordered elastic networks. We find that the simulator can learn the underlying physical dynamics from a small number of training examples and accurately reproduce the temporal evolution of unseen networks. Notably, the simulator is able to accurately predict emergent properties such as Poisson's ratio and its dependence on strain, even though it was not explicitly trained for this task. In addition, it generalizes well across variations in system temperature, strain amplitude, and most significantly, Poisson's ratios beyond the training range. These findings suggest that using dynamical data to train machine learning models can support more information efficient and generalizable approaches for materials and molecular design, especially in data-scarce settings. Graph ; Artificial neural network ; Context (archaeology) ; Training set ; Training (meteorology) ; Experimental data ; Physical system Machine Learning in Materials Science ; Functional Brain Connectivity Studies ; Protein Structure and Dynamics
Authors: Md Rahatul Islam Udoy | J Lee Hutchins | Shamiul Alam | Catherine D. Schuman | Ahmedullah Aziz
Source:  (preprint, 2025-08-03)
Field: Materials Science — Materials Chemistry

Integrating Atomistic Insights with Circuit Simulations via Transformer-Driven Symbolic Regression This paper introduces a framework that establishes a cohesive link between first principlesbased simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, are processed using machine learning algorithms to identify complex patterns and relationships. As these machine-learning models develop, they become tools for predicting behaviors beyond the reach of conventional modeling and simulation methods. Applied to circuit simulation, the framework improves understanding of electrical interactions, enhancing accuracy and speeding up design automation. As a proof of concept, we perform first principles-based simulations of the Graphene Nanoribbon Field Effect Transistor (GNRFET), an exploratory device, and create a symbolic-regression-based machine learning model that can readily be integrated into advanced circuit simulation. This framework presents a template offering a unified approach that synergizes the strengths of first principles-based simulations and circuit-level design tools. Transformer ; Regression ; Computer science ; Regression analysis ; Statistical physics ; Physics ; Electrical engineering ; Engineering ; Mathematics ; Machine learning ; Statistics ; Voltage Machine Learning in Materials Science ; Protein Structure and Dynamics