jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: SARAVANAN RAVINDRAN | SHAIK MEHARAJ | Rajaganapathy Kaliyaperumal
Source: Asian Journal of Pharmaceutical and Clinical Research (article, 2026-02-07)
Field: Chemistry — Spectroscopy

LC-MS AS A VERSATILE TOOL FOR DRUG DISCOVERY, DEVELOPMENT, AND CLINICAL DIAGNOSTICS One of the crucial elements in the analysis of biomolecules is liquid chromatography-mass spectrometry (LC-MS), affording unique sensitivity and specificity to a broad spectrum of applications in the discovery of new drugs, proteomics, and metabolomics. Modern biomolecular science has been reshaped by new techniques in LC-MS technology by enabling the potential to deconstruct increasingly complex systems in biological and life sciences research. A highlight of current trends, hot instruments, and advancements, along with modern approaches for improvements in separations and processing the data for advancement in this sector, will also be addressed herein. Combining high-resolution MS (HRMS) and ultra-high-performance liquid chromatography (UHPLC) marks one of the most meaningful changes since this combination produces the best results, both in terms of better resolution and as regards increasing sample throughput. Many of these previous limitations for biological macromolecule analysis are fast being mitigated because improved ionization technologies, particularly in electrospray ionization and matrix-assisted laser desorption/ionization, expand the breadth of possible LC-MS analyses. Recent improvements in multidimensional separation technology and stationary phase diversity increase chromatographic efficiencies, especially to study more intricate biological matrices. Together with strategies to overcome these limitations, this paper also discusses challenges such as matrix effects, sample preparation challenges, and data quality. Researchers can uncover novel information on biomolecular interactions, disease mechanisms, and therapeutic targets by integrating state-of-the-art LC-MS technologies with innovative computational approaches. As LC-MS continues to evolve, it holds immense potential to shape the future of precision medicine and biomolecular research. Drug discovery ; Complex matrix ; Sample (material) ; Biomolecule ; Resolution (logic) ; Broad spectrum ; Electrospray ionization ; Analytical technique ; Biological fluids Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Pesticide Residue Analysis and Safety
Authors: Nicolas Schmid | Marc Wanner | Giulia Fischetti | Andreas Henrici | Mohsen Meshkian | Simon Bruderer | Rudolf M Füchslin | Bjoern Heitmann | Jan Dirk Wegner | Roland K O Sigel | Dirk Wilhelm
Source: ChemRxiv (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

MolDeTr: A Chemistry-Informed Deep Learning Model for Next-Generation Automated Analysis of 1H NMR Spectra Accurate interpretation of one-dimensional proton nuclear magnetic resonance (1H NMR) spectra remains a rate-limiting step in molecular structure elucidation, particularly when severe signal overlap, strong spin coupling, and instrumental distortions mask key features. Existing automated approaches depend on computationally intensive and sensitive iterative quantum-mechanical fitting and still require expert oversight. Here we introduce MolDeTr, a chemistry-informed deep-learning framework derived from the detection transformer architecture that unifies peak picking, multiplet identification, and extraction of chemical shifts, scalar coupling constants, relaxation-dependent decay times, and proton counts in a single-network pass. The method targets prototypical spin systems of single-component small molecules in 1D 1H NMR, with up to ten distinct groups of chemically equivalent spins (multiplets). MolDeTr is trained exclusively on synthetic spectra generated by spin-dynamics simulations and augmented with realistic experimental artefacts, enabling it to generalize to unseen compounds, including experimental spectra with crowded regions and strongly coupled multiplets, without reference standards or prior spin-system knowledge. Unlike structure-conditioned shift-prediction or calculation models, for example density functional theory (DFT), that assume the molecular structure is known, MolDeTr addresses the spectrum-conditioned inverse problem and extracts spin-system parameters directly from measured 1D 1H NMR spectra, thereby substantially improving chemical-shift prediction precision by one to two orders of magnitude compared to existing structure-conditioned approaches. Benchmarking against challenging experimental 1H NMR spectra of small molecules shows median absolute errors of 0.89 Hz for chemical shifts and 0.20 Hz for coupling constants, while absolute proton counts are predicted with 93.5 percent accuracy, outperforming state-of-the-art spectrum analysis software and experienced spectroscopists. Owing to its physics-guided, modality-agnostic design, MolDeTr can be extended to other spectroscopic and diffraction techniques. By eliminating iterative fitting and expert intervention, MolDeTr offers a scalable route to fully automated spectral analysis, accelerating molecular discovery across the chemical sciences. Spins ; Spectral line ; Multiplet ; Proton ; Chemical shift ; Scalar (mathematics) ; Spin (aerodynamics) ; NMR spectra database ; Density functional theory Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Linyu Meng | Zihan Pei
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Multi-Task Attention Network for Elucidating Synergistic Effects of Multiple Vacancies on Oxygen Evolution Reaction Activity in High-Entropy Layered Double Hydroxides: A Synthetic Data Study This study introduces and validates a novel Multi-Task Attention Network (MTAN) framework designed to predict and interpret the complex synergistic effects of "multiple vacancies" on Oxygen Evolution Reaction (OER) activity in High-Entropy Layered Double Hydroxides (HE-LDHs). Utilizing combined chemical and characterization data, MTAN employs a shared encoder and task-specific attention mechanisms. The framework was evaluated on a comprehensive synthetic dataset and benchmarked against established supervised learning models, including Random Forest, XGBoost, and Graph Neural Networks, for both regression (predicting overpotential) and binary classification (categorizing activity). Our results demonstrate that MTAN consistently achieves superior predictive performance across all metrics, substantially outperforming baselines for overpotential prediction and activity classification. An ablation study further confirmed the critical contributions of both the attention mechanism and the multi-task learning paradigm. Interpretability tools, including MTAN's attention weights and SHAP analysis, elucidated task-specific feature importance. This analysis highlighted the synergistic roles of oxygen vacancy density, Ni coordination number, metal vacancy density, and Bader charge of Ni, thereby offering precise data-driven guidance for rational materials design. Interpretability ; Oxygen evolution ; Binary number ; Overpotential ; Feature (linguistics) ; Encoder ; Binary classification ; Artificial neural network Machine Learning in Materials Science ; Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Salma Zahran | Zhou Ao | Zhengyang Zhang | Chen Chi | Chenchen Yuan | Yanming Wang
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis Semantic segmentation of microscopy images is a critical task for high-throughput materials characterisation, yet its automation is severely constrained by the prohibitive cost, subjectivity, and scarcity of expert-annotated data. While physics-based simulations offer a scalable alternative to manual labelling, models trained on such data historically fail to generalise due to a significant domain gap, lacking the complex textures, noise patterns, and imaging artefacts inherent to experimental data. This paper introduces a novel framework for labour-free segmentation that successfully bridges this simulation-to-reality gap. Our pipeline leverages phase-field simulations to generate an abundant source of microstructural morphologies with perfect, intrinsically-derived ground-truth masks. We then employ a Cycle-Consistent Generative Adversarial Network (CycleGAN) for unpaired image-to-image translation, transforming the clean simulations into a large-scale dataset of high-fidelity, realistic SEM images. A U-Net model, trained exclusively on this synthetic data, demonstrated remarkable generalisation when deployed on unseen experimental images, achieving a mean Boundary F1-Score of 0.90 and an Intersection over Union (IOU) of 0.88. Comprehensive validation using t-SNE feature-space projection and Shannon entropy analysis confirms that our synthetic images are statistically and featurally indistinguishable from the real data manifold. By completely decoupling model training from manual annotation, our generative framework transforms a data-scarce problem into one of data abundance, providing a robust and fully automated solution to accelerate materials discovery and analysis. Segmentation ; Generative grammar ; Automation ; Pipeline (software) ; Synthetic data ; Intersection (aeronautics) ; Noise (video) ; Generative model ; Image segmentation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Generative Adversarial Networks and Image Synthesis
Authors: Alba Grayston | Miguel García‐Gabilondo | Neus Otero-Fornés | Anna Solé-Porta | Itsasne de la Torre-Sánchez | Wid Mekseriwattana | M. Timko | Jozef Kovac | P. Kopčanský | Jiahui Li | Riccardo Tiberi | Marielle Esteves | Alejandro Tomasello | David Reding Hernández | Marc Ribó | A Roig | Anna Rosell
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Biomaterials

Endovascular administration and magnetic retention of nanocapsules for improved brain delivery in large cerebral vascular models Brain delivery remains a challenge for the clinical translation of therapeutic nanomedicines, particularly in focal diseases with specific delivery needs, such as stroke. In this scenario, clinically relevant endovascular interventions are recently being proposed as strategies to enhance delivery into specific cerebral vascular territories. In this study, we assess the feasibility of endovascular delivery and magnetic retention of biocompatible magnetic nanocapsules (NCs) in cerebral circulation models that better predict human responses. More specifically, polymeric NCs synthesized with magnetic properties (superparamagnetic oxide nanoparticles, SPIONs) and fluorescent (Cy5) moieties were infused into pigs <i>via</i> a femoral microcatheter reaching the brain vasculature and showing greater efficacy in targeting the ipsilateral brain hemisphere with preferential accumulation in microvessels when compared to intravenous administration which resulted in very little accumulation. Transient adverse effects related to hemodynamic instability upon nanocapsule administration were observed in both administration groups related to acute complement activation. Successful endovascular brain NC delivery is further demonstrated in a 3D-vascular model of the human large arterial vessel brain supply, with successful NC accumulation in the target arterial segment (the proximal middle cerebral artery) with sensible enhancement when using local magnetic fields. This study demonstrates the feasibility of endovascular NC delivery for focal brain nanotargeting <i>via</i> clinically relevant and minimally invasive procedures and proves the advantages of using magnetized nanomaterials to improve local vascular NC retention. Further safety and efficacy studies, including drug nanocapsule formulations, are needed to establish the clinical relevance of the proposed approach. Nanocapsules ; Drug delivery ; Cerebral circulation ; Systemic administration ; Bench to bedside ; Biocompatible material ; Magnetic resonance imaging ; Endovascular treatment Nanoparticle-Based Drug Delivery ; Barrier Structure and Function Studies ; RNA Interference and Gene Delivery
Authors: Aashwin Mishra | Matt Seaberg | Ryan Roussel | Daniel Ratner | Apurva Mehta
Source: ArXiv.org (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled parameters and highly asymmetric objective landscapes, where rewards are sparse. In such needle-in-a-haystack scenarios, even advanced methods like trust-region BO (TurBO) often lead to unsatisfactory results. We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to fundamentally simplify the search problem by transforming coordinates to decouple input features and align the active subspaces with the primary search axes. We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system, where conventional approaches, including standard BO, TuRBO and multi-objective BO, consistently led to unsatisfactory results. When combined with an reverse annealing exploration strategy, this approach reliably converges to the global optimum. The coordinate transformation itself is the key to this success, significantly accelerating the search by aligning input co-ordinate axes with the problem's active subspaces. As increasingly complex scientific instruments, from large telescopes to new spectrometers at X-ray Free Electron Lasers are deployed, the demand for robust high-dimensional optimization grows. Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning for consistent high performance while still retaining current optimization algorithms. Bayesian optimization ; Bayesian probability ; Domain (mathematical analysis) ; Key (lock) ; Optimization problem ; Simulated annealing ; Transformation (genetics) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced Bandit Algorithms Research
Authors: Ramcharan Singh Angom | Hari Krishnareddy Rachamala | Naga Malleswara Rao Nakka | Vijay Sagar Madamsetty | Paola Suarez-Meade | Beatriz I. Fernández-Gil | Tanmay Kulkarni | Raegan M. Weil | Shamit K. Dutta | Enfeng Wang | S. Bhattacharya | Krishnendu Pal | Alfredo Quinones-Hinojosa | Debabrata Mukhopadhyay
Source: Communications Medicine (article, 2026-03-18)
Field: Materials Science — Biomaterials

Surface-engineered dual drug-loaded tumor-targeted liposomal nanoparticles to overcome the therapeutic resistance in glioblastoma multiforme TTL crossed the blood-brain barrier, targeting tumors effectively. Radiation plus TTL-EV/RV enhanced tumor suppression and survival in GBM models. Gene expression analysis identified DEGs related to DNA damage and cell death. Mechanistic studies suggest TTL-EV plus radiation inhibits mTOR/MAPK pathways and sensitizes tumors to radiation. These findings offer a potential approach for improving GBM treatment. Glioblastoma ; Radiation therapy ; DNA damage ; Gene ; Cell ; Temozolomide ; Liposome ; Cell culture ; Tumor cells Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment ; Nanoplatforms for cancer theranostics
Authors: Eric J. Anderson | Vittorio Sansalone | Luke P. Lee | Rebecca Grainger | Sohie Lee | Albert L. Juhasz | Melissa L. Knothe Tate
Source: Global Challenges (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Rivers of Tears – Convergent, Multi‐Scale Approaches to Monitor and Optimize the Health of Our World's Inhabitants The connectivity and interdependence of our world and its inhabitants' health have come under increasing focus. Elucidation of the common and interdependent mechanisms of health and disease requires approaches that facilitate understanding of complex systems behavior and probing of both individual and collective system parameters. To this end, multiscale physical and computational modeling offers a particularly powerful tool to predict behavior over vast time and length scales. Other novel technologies, e.g., rapid isolation nanotechnology developed to analyze nanoscale small extracellular vesicles in ocular tears, enable tracking of "fingerprints" from diseases as diverse as ocular to neurodegenerative (e.g., dementia) and cancer. In the future, it will be possible to track the health and disease of ecosystems and their inhabitants, using geospatial and epidemiological approaches, as well as novel biotechnologies, to prevent and mitigate disease processes and enhance well-being. These concepts are applied by way of an exemplary approach to understand and address the impact of toxic, recalcitrant manmade chemicals (i.e., PFAS) on the health of ecosystems and their diverse inhabitants. Such convergent efforts will be necessary and a priority for solving the complex problems threatening the health of our planet and its inhabitants. Interdependence ; Disease ; Isolation (microbiology) ; One Health ; Geospatial analysis ; Healthcare system ; Human health ; Ecosystem health Polydiacetylene-based materials and applications ; Health, Environment, Cognitive Aging ; Nanoparticles: synthesis and applications
Authors: Pramod K | Varun MS | Sandeepa NL | Murthy S | Likhit Kumar R | Abhilash Demanagouda Patil
Source: International Journal of Advanced Biochemistry Research (article, 2026-03-01)
Field: Materials Science — Biomaterials

Biotechnological advances in Silkworm (Bombyx mori) research: From genomics to silk biomaterials The domesticated silkworm, Bombyx mori, has transitioned from its traditional role in sericulture to a powerful and versatile platform in modern biotechnology. This review critically synthesizes the monumental progress in silkworm research, charting its evolution from a genomic model to a source of advanced biomaterials. The completion of the silkworm genome has catalyzed a cascade of innovations, providing foundational resources for functional genomics, transcriptomics and proteomics. These multi-omics approaches have unraveled the complex molecular machinery governing silk protein synthesis, developmental biology and immune responses. We discuss the transformative impact of genetic engineering technologies, including the pioneering piggyBac transposon system and the revolutionary CRISPR-Cas9 platform, which have enabled precise genome editing for enhancing traits such as disease resistance and silk yield. Furthermore, these tools have repurposed the silkworm silk gland into a highly efficient bioreactor for the large-scale production of valuable recombinant proteins, including pharmaceuticals and vaccines. The unique biochemical and mechanical properties of silk proteins, particularly fibroin, have been harnessed to fabricate a new generation of biomaterials. We explore the burgeoning applications of silk fibroin in tissue engineering scaffolds, controlled drug delivery systems and biodegradable medical implants. The development of silk-based nanoparticles, hydrogels and Electrospun nanofibers further expands their utility in nanotechnology and regenerative medicine. This review concludes by addressing the current challenges, including scalability and regulatory hurdles and outlines future prospects, such as the integration of synthetic biology to design smart silk biomaterials and advance precision sericulture, underscoring the silkworm’s enduring significance in science and industry. Fibroin ; SILK ; Genome editing ; Sericulture ; Synthetic biology ; Genomics ; Regenerative medicine ; Bombyx mori ; Genetically engineered Silk-based biomaterials and applications ; RNA Interference and Gene Delivery ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Junjun Xu
Source: eCommons (Cornell University) (article, 2026-04-02)
Field: Materials Science — Biomaterials

REGULATON OF TYPE I INTERFERON SIGNALING AND HOST DEFENSE AGANST BACTERIA BY 2,3,7,8-TETRACHLORODIBENZO-p-DIOXIN-INDUCIBLE POLY(ADP-RIBOSE) POLYMERASE 178 pages Bacteria ; Interferon ; Polymerase ; Host (biology) ; Innate immune system ; Interferon type I ; Immune system ; Interferon γ biodegradable polymer synthesis and properties ; Biopolymer Synthesis and Applications ; Antimicrobial agents and applications
Authors: Anuj Karpatne | Xiaowei Jia | Vipin Kumar
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Knowledge-Guided Machine Learning: A Paradigm Shift in AI for Science As advances in artificial intelligence (AI) and machine learning (ML) continue to transform commercial applications, the scientific community is increasingly eager to harness AI/ML’s power to accelerate modeling and discovery. However, purely data-driven AI methods often lack interpretability, generalizability, and consistency with established scientific principles. Conversely, traditional process-based models embody deep scientific knowledge but suffer from limited scalability or incomplete representation of complex systems. Knowledge-guided machine learning (KGML) offers a promising path forward by integrating scientific knowledge with data-driven approaches to produce AI models that are robust, trustworthy, and capable of advancing both AI and science. This talk summarizes the foundations of KGML, outlines a taxonomy for organizing research efforts, and highlights emerging opportunities for broad scientific impact. Paradigm shift ; Consistency (knowledge bases) ; Representation (politics) ; Scientific discovery ; Deep learning ; Applications of artificial intelligence ; Scalability Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Advanced Graph Neural Networks