jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kun Li | Lening Hu | Yida Xiong | Jiajun Yu | Hongzhi Zhang | Jiameng Chen | Xiantao Cai | Jia Wu | Wenbin Hu
Source: Open MIND (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

PCEvo: Path-Consistent Molecular Representation via Virtual Evolutionary Molecular representation learning aims to learn vector embeddings that capture molecular structure and geometry, thereby enabling property prediction and downstream scientific applications. In many AI for science tasks, labeled data are expensive to obtain and therefore limited in availability. Under the few-shot setting, models trained with scarce supervision often learn brittle structure-property relationships, resulting in substantially higher prediction errors and reduced generalization to unseen molecules. To address this limitation, we propose PCEvo, a path-consistent representation method that learns from virtual paths through dynamic structural evolution. PCEvo enumerates multiple chemically feasible edit paths between retrieved similar molecular pairs under topological dependency constraints. It transforms the labels of the two molecules into stepwise supervision along each virtual evolutionary path. It introduces a path-consistency objective that enforces prediction invariance across alternative paths connecting the same two molecules. Comprehensive experiments on the QM9 and MoleculeNet datasets demonstrate that PCEvo substantially improves the few-shot generalization performance of baseline methods. The code is available at https://anonymous.4open.science/r/PCEvo-4BF2. Generalization ; Representation (politics) ; Property (philosophy) ; Dependency (UML) ; Evolutionary algorithm ; Code (set theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Jianliang Gong | Han Zhou | Shicheng Yu | Yiwang Chen
Source: Materials Horizons (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Cognitive AI beyond prediction: toward reasoning and discovery Artificial intelligence (AI) has become indispensable in the investigation of materials and systems like batteries, facilitating expedited evaluation of electrolytes, interfaces, and structural frameworks. While modern AI excels in property prediction, it is constrained in its ability to achieve fundamental scientific objectives, including understanding, explanation, and adaptive reasoning. This opinion proposes that AI advancements in materials discovery are developing toward scientific reasoning, encompassing the ability to construct representations, infer mechanisms, formulate hypotheses, conduct experiments, and revise beliefs. A modular cognitive architecture, informed by recent AI developments, can integrate these capabilities to address emerging issues in battery research, such as interfacial instability, failure analysis, and electrolyte design under uncertainty. This paradigm draws inspiration from recent advances in neuro-symbolic reasoning, hypothesis generation, and autonomous systems, which provide the conceptual and technical foundations for future implementation. Cognitively enabled AI systems can work as reasoning colleagues for scientists, enhancing discoveries while maintaining the epistemic integrity of scientific activity. Construct (python library) ; Modular design ; Cognition ; Scientific discovery ; Cognitive robotics ; Work (physics) Machine Learning in Materials Science ; Bayesian Modeling and Causal Inference ; AI-based Problem Solving and Planning
Authors: Xin Li | Wenqi Cao | Ning Man | Jiayi Wang | Jianglei Fan | Shen Wu | Xiangkui Zhou | Feng Mao | Kunlin Miao | Shizhong Wei
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Microstructure Segmentation and Mechanical Property Prediction of 45 Steel doped with 0.15 wt.% REFeSi Using an Improved U-Net Model Microstructure ; Ultimate tensile strength ; Segmentation ; Brinell scale ; Robustness (evolution) ; Property (philosophy) ; Ferrite (magnet) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Titanium Alloys Microstructure and Properties
Authors: A. Pizzi | Giancarlo Terraneo | Cristina Lo Iacono | Roberta Beccaria | Arun Dhaka | Giuseppe Resnati
Source: Angewandte Chemie (article, 2026-02-25)
Field: Chemistry — Physical and Theoretical Chemistry

Benefits of Categorizing Noncovalent Bonds Based on Hydrogen, Halogen, Chalcogen, and Pnictogen Bonds ABSTRACT Recent IUPAC recommendations define the halogen bond, the chalcogen bond, and the pnictogen bond as the interactions between nucleophilic sites and electrophilic elements of groups 17, 16, and 15 of the periodic table. In a recent paper Robin Taylor states that, the use of these three terms should be “deprecated” and he raises concern about the proposal that we made in a scientific perspective on this journal to use these three terms, along with the hydrogen bond one, as the core of a taxonomy of chemical interactions, wherein names refer to the electrophile group/atom. Here, we show how Taylor's position conflicts not only with the authoritative position of IUPAC, but also with the common practices of the chemists’ community. Our proposed naming scheme which refers to the electrophile group/atom far from concealing key information, as assumed by Taylor, enables for a precise language whose terminological richness is instrumental in communication efficiency. Our naming scheme presents the advantage to be invariant with respect to the evolving understanding of the nature of chemical interactions and to be tailored to automated database mining, ontology construction, and AI‐driven structure–property prediction. Pnictogen ; Electrophile ; Non-covalent interactions ; Nucleophile ; Chalcogen ; Hydrogen bond ; Reciprocal ; Philosophy of science History and advancements in chemistry ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Teufel, Jonas | Leinweber, Annika | Friederich, Pascal
Source: KITopen (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Improving Counterfactual Truthfulness for Molecular Property Prediction Through Uncertainty Quantification Counterfactual thinking ; Interpretability ; Property (philosophy) ; Uncertainty quantification ; Feature (linguistics) ; Filter (signal processing) ; Measurement uncertainty ; Estimation Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Computational Drug Discovery Methods
Authors: Zihan Liu | Shuyuan Zheng | Guofeng Liu
Source: Chinese Journal of Chemistry (article, 2026-02-05)
Field: Materials Science — Biomaterials

Controlling Biomedical Functionality through Stereochemistry: A Foundation for Precision Diagnostics and Therapeutics <sup>†</sup> Comprehensive Summary As a fundamental feature of physiological regulation in living systems, chirality has emerged as a critical factor in the rapid development of nanomaterials for biomedical applications. Through precise structural design, chiral nanomaterials enable high‐precision biological analysis and efficient therapeutic interventions by exploiting handedness‐dependent interactions at chiral organic–inorganic hybrid interfaces or through chiral light–matter coupling. This review systematically outlines synthetic strategies for diverse chiral nanomaterials, spanning three primary material categories: inorganic ( e.g. , chiral metal nanoparticles, nanographenes, carbon nanotubes, mesoporous silica nanoparticles, and quantum dots), organic ( e.g. , chiral covalent organic frameworks and DNA nanomaterials), and organic–inorganic hybrid systems ( e.g. , chiral metal nanoclusters and metal−organic frameworks). For each category, we undertake a comprehensive discussion of established methodologies for imparting or controlling chirality, including direct synthesis, post‐synthetic modification, and template‐guided assembly, among others. Furthermore, we summarize the applications of these chiral nanomaterials across several frontier biomedical domains, including biosensing, antitumor therapy, antibacterial treatment, as well as tissue engineering and regeneration. Particularly, the focus is on elucidating how nanoscale chirality influences fundamental biological processes, including molecular signaling, cellular uptake, proliferation, and immune responses in order to establish structure‐activity relationships that link the design of chiral nanomaterials with their macroscopic biological functions. Finally, we examine the key challenges impeding clinical translation, including the unclear mechanisms governing the interactions of chiral nanomaterials with biological systems, the paucity of diverse functional chiral ligands, and unresolved issues concerning long‐term biosafety. We also offer perspectives on future directions for chiral nanomaterials, including AI‐driven design and advanced biorelevant testing platforms to study systematic structure–activity relationships, thereby accelerating the development of enantioselective nanomedicine. This work aims to provide theoretical guidance for the rational design of chiral nanomaterials with tailored biological functionalities, thereby advancing solutions to pressing challenges in life sciences. Key Scientists Nanomaterials ; Chirality (physics) ; Nanoclusters ; Mesoporous silica ; Nanomedicine ; Carbon nanotube Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Luis Eduardo Ramirez Cardenas | Rachid Ouaret | Vincent Gerbaud | Ivonne RODRIGUEZ DONIS | Sophie Thiebaud-Roux
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Dimensionality reduction of COSMO-RS molecular descriptor using functional principal component analysis (FPCA) for organic solvent mapping Within the context of a transition towards greener and safer solvents, we describe a framework facilitating solvent screening. Traditional approaches rely on experimental solubility data or computational methods such as COSMO-RS. In parallel, similarity maps can be helpful to explore alternative molecules similar to working solvents. For developing solvent maps, Principal Component Analysis (PCA) offers limited applicability when dealing with complex molecular descriptors such as the σ-potential derived from COSMO-RS theory. In this study, we propose the application of Functional Principal Component Analysis (FPCA) as a more suitable dimensionality reduction technique for solvent mapping, leveraging the functional nature of σ-potentials. A database of 1588 solvents was analyzed, extending previous reported datasets with the inclusion of industrially relevant and sustainable candidates. FPCA enables a two-dimensional representation of the solvent space with minimal information loss (0.5%), directly associating the principal components with electron donor and acceptor characteristics. In this space, solvent clustering naturally emerges, facilitating the identification of structurally and functionally similar solvents. Three case studies are presented to illustrate the practical implications of the approach. Overall, this methodology provides a suitable framework for solvent substitution, whether as a preliminary screening step or as a part of computer-aided solvent design tools, contributing to more sustainable chemical practices. Dimensionality reduction ; Principal component analysis ; Curse of dimensionality ; Context (archaeology) ; Solvent ; Representation (politics) ; Similarity (geometry) ; Cluster analysis ; Chemical space Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Molecular Sensors and Ion Detection
Authors: Manuel Valenzuela-Valderrama | Aranxa Varas | Mariaignacia Rubilar | Marcos Lorca | Jaime Mella | Christian Espinosa-Bustos | Marco Mellado | Javier Echeverría
Source: Frontiers in Pharmacology (article, 2026-03-09)
Field: Chemistry — Organic Chemistry

3D-QSAR study for the development of chalcone-based inhibitors targeting ovarian cancer cells with experimental validation CoMFA and CoMSIA models accurately predicted the activity of synthetic chalcones, and the biological findings identify these derivatives as promising candidates for the treatment of ovarian cancer, including chemoresistant forms. Chalcone ; Clonogenic assay ; Glutathione ; Ovarian cancer ; Reactive oxygen species ; Intracellular ; Mechanism of action ; Cell growth ; Cancer cell Synthesis and biological activity ; Curcumin's Biomedical Applications ; Genomics, phytochemicals, and oxidative stress
Authors: Afshin Nabiyan | Mitra Esfandiari | Sergio Kogikoski | Ilko Bald | Evgenii Titov | Michael U. Kumke | Nora Kulak | Helmut Schlaad
Source: ACS Applied Materials & Interfaces (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Polyelectrolyte Copolymer Nanoreactors: From Colloidal Assembly to Photoredox Activity in Water Performing organic photoredox reactions in water remains challenging because most catalysts cannot simultaneously solubilize substrates, control nanoscale organization, and maintain activity under aqueous conditions. We report a photoredox-active polyelectrolyte based on a polydehydroalanine (PDha) backbone covalently functionalized with polypyridyl complexes to address some of these limitations. The copolymer undergoes substrate-triggered self-assembly in water, forming photocatalytically active spherical colloidal nanostructures (∼30 nm), as confirmed by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The assemblies efficiently catalyze the hydroxylation of arylboronic acids, a representative water-insoluble photoredox transformation. Mechanistic studies using UV-visible spectroscopy, Raman spectroscopy, time-resolved emission spectroscopy, electrochemistry, and density functional theory (DFT) indicate that dual hydrogen bonding between PDha carboxylates and arylboronic acids governs both self-assembly and catalytic performance. The nanostructures retain high activity over multiple cycles. These findings establish adaptive polymer self-assembly as a general strategy for creating enzyme-like, water-compatible photoredox systems and provide a platform for transferring organic photoredox chemistry into aqueous media. Photoredox catalysis ; Aqueous solution ; Copolymer ; Catalysis ; Polymer ; Nanostructure ; Polyelectrolyte ; Colloid ; Covalent bond Luminescence and Fluorescent Materials ; Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies
Authors: Ihsane Achour | Boutaina Addoum | Wafae Zennouhi | Farid Khallouki | M. El Mzibri | M.R. Tagajdid | Imane Chaoui | Mohammed ATTALEB | Abdelhakim Elmakssoudi | Laïla Benbacer
Source: Arabian Journal of Chemistry (article, 2026-03-16)
Field: Chemistry — Organic Chemistry

From quinoline to cancer therapy: A systematic overview of its biological mechanisms and therapeutic roles Cancer remains a leading cause of mortality worldwide, presenting a major challenge to global health. Quinolines, a class of nitrogen-containing heterocycles, have proven to be promising compounds for anticancer therapeutic development due to their diverse pharmacological properties, including cytotoxic, antiproliferative, pro-apoptotic, and immunomodulatory effects. This systematic review provides an updated synthesis of the anticancer applications of quinoline-based compounds, including their chemical synthesis, cytotoxic potential, clinical relevance, and biological mechanisms of action. Rigorous literature searching was conducted across multiple databases, identifying studies that evaluated quinoline derivatives using a range of methodologies, including computational modeling, cell-based cytotoxicity assays, 3D tumor spheroid models, and animal models. Quinoline-based compounds express broad-spectrum anticancer potential by acting on multiple cellular targets and pathways. Their mechanisms include inducing mitochondrial apoptosis, inhibiting key oncogenic kinases, modulating epigenetic regulators, damaging DNA, regulating autophagy, and triggering ferroptosis. Overall, this review highlights quinolines as a versatile and promising chemical scaffold for developing multi-targeted anticancer agents. Continued preclinical and clinical research is essential to optimize their efficacy and expand their therapeutic applications in oncology. Cancer ; Epigenetics ; Cytotoxicity ; Quinoline ; Drug discovery ; Cancer cell ; Anticancer drug ; Drug Ferrocene Chemistry and Applications ; Synthesis and biological activity ; Cancer Research and Treatment
Authors: Burcu Baba | Çağla Zübeyde Köprü | Ilkay Yildiz | Elif Yardimci | Ayşegül Akbay
Source: Journal of Clinical Practice and Research (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Anticancer Activity and Molecular Docking Analysis of Selected Benzoxazole Derivatives as Apoptosis Inducers in Non-Small Cell Lung Cancer Our results demonstrate that the benzoxazole derivatives 1a and 1b exhibit significant anticancer effects by inhibiting lung cancer cell proliferation at low concentrations, similar to cisplatin. The structure-activity relationship suggests that substitution of a phenyl group at the 2-position of the benzoxazole ring with a tert-butyl group at the para position enhances anticancer activity against A549 cells. This preliminary study indicates that these benzoxazole derivatives have promising potential as cytotoxic agents for the treatment of NSCLC. Benzoxazole ; Apoptosis ; Lung cancer ; Inducer ; Cell culture ; Cytotoxicity Synthesis and biological activity ; Lung Cancer Treatments and Mutations ; Flavonoids in Medical Research
Authors: Yashvant Vitthal Khandare | Prashant N Muskawar
Source: International Journal of Chemical Research and Development (article, 2026-04-01)
Field: Chemistry — Organic Chemistry

A greener and novel Lewis acid-decyl benzimidazolium based ionic liquid surfactant combined catalyst used for the synthesis of quinoxaline derivatives in water Lewis Acid-Decyl Benzimidazolium based surfactant combined catalyst efficiently catalyses the synthesis of quinoxaline derivatives via reaction of 1,2-diamines with 1,2-diketones in water as a green solvent at room temperature. Various 1,2-diamines were successfully reacted with 1,2-diketones in a less time. This method bestowed several benefits, such as simple process, shorter time of reaction, and environmental friendliness. Lewis Acid-Hexyl Benzimidazolium based surfactant combined catalyst demonstrates a high yield in the range of 85-99% of all quinoxaline derivatives. Structural characterization of were performed using 1H NMR, 13C NMR, FTIR, and HRMS spectroscopy. Quinoxaline ; Catalysis ; Pulmonary surfactant ; Ionic liquid ; Yield (engineering) ; Lewis acids and bases Synthesis and Biological Evaluation ; Carbon dioxide utilization in catalysis ; Ionic liquids properties and applications
Authors: Huda Thamer | Mohammed Mahal | Wedyan H. Mahdi
Source: International Journal for Scientific Research (article, 2026-01-15)
Field: Chemistry — Organic Chemistry

Green Synthesis of Heterocyclic Compounds Using Eco-Friendly Catalysts This study investigates the green methods of making heterocyclic compounds with safe catalysts. Results are analyzed using data-driven techniques to assess their efficiency, spectral authenticity and activity towards living organisms. For this study, a set of 1000 heterocycles was made using experimental values from the literature for reaction yield, melting point, NMR, IR, UV-V is spectra and both antioxidant and antimicrobial properties. The investigation of interactions between synthetic and functional properties was made possible by the application of descriptive analytics, Pearson correlation, Principal Component Analysis (PCA), KMeans clustering and hypothesis testing. Most of the developed compounds showed a high yield (85–95%) and could withstand heat (melting point 200–250°C), in addition to showing effective antioxidant (80–95%) and antibacterial (20–25 mm inhibition zone) qualities. The study's calculations matched the spectra expected for actual compounds containing aromatic and conjugated groups. Significantly, using statistics revealed no conflict between drug effectiveness and drug production which means pharmaceutical goals can be met with green chemistry. The study shows a repeatable method that can be used outside a lab, where cheminformatics, spectral modeling and statistical learning are incorporated to support green synthesis and preserve bioactivity. It allows the use of a flexible system to test, group and improve the use of heterocycles for sustainable pharmaceutical chemistry. Principal component analysis ; Catalysis ; Yield (engineering) ; Statistical analysis ; Antimicrobial ; Cluster analysis ; Green chemistry ; Conjugated system Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Chemistry and Chemical Engineering
Authors: Prashant Kumar | Priya | S. Dutta | Deepak R. Mishra | Naresh Kumar
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Selective Functionalization of 1-substituted-3-arylquinoxalin-2(1 <i>H</i> )-ones <i>via</i> C–H activation Over the past few decades, transition-metal-catalyzed selective functionalization of C-H bonds has fascinated the scientific community by offering striking advantages in comparison to traditional methods, such as high atom economy, a reduced number of reaction steps, and milder reaction conditions. Moreover, directing-group-assisted C-H activation enhances the significance of transition-metal catalysis further by enabling precise and site-selective functionalization. This mini-review outlines recent advancements in the imine-directed, transition-metal-catalyzed selective functionalization of <i>N</i>-alkyl-3-phenylquinoxalin-2(1<i>H</i>)-ones, highlighting C-C, C-O, C-X (X = Cl, Br, and I), C-S, and C-Se bond formation reactions and mechanistic aspects of these transformations. Surface modification ; Regioselectivity ; Ring (chemistry) ; Selective surface Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Synthesis and Biological Evaluation
Authors: Atziri Corin Chavez Alvarez | Emmanuel Moreau
Source: Pharmaceutics (article, 2026-03-10)
Field: Chemistry — Organic Chemistry

Coumarin-Based Prodrugs: Therapeutic Promise or Still Confined to Preclinical Exploration? Coumarin-based compounds are recognized for their chemical versatility and diverse biological activities, yet clinical applications remain largely confined to 4-hydroxycoumarin anticoagulants. To bridge this translational gap, coumarin scaffolds have been increasingly employed in prodrug design to enable controlled activation, targeted delivery, and theranostic functionality. This review critically evaluates whether coumarin-based prodrugs fulfill their therapeutic promise or remain primarily preclinical tools across oncology, inflammation, infectious diseases, and cardiovascular disorders. Strategies including enzymatic-, pH-, redox-, and light-triggered activation, as well as subcellular targeting and multifunctional hybrids, are discussed. Preclinical studies demonstrate improved bioavailability, reduced off-target toxicity, and real-time fluorescence monitoring, yet most compounds remain at the in vitro or small-animal model stage. Despite their mechanistic and conceptual potential, clinical translation is constrained by molecular complexity, pharmacokinetics, safety, and regulatory challenges. Overall, coumarins constitute a versatile multifunctional platform whose therapeutic impact relies on rigorous in vivo validation and strategic optimization. Prodrug ; Clinical trial ; Translational research ; Clinical Practice ; Translational medicine ; In vivo Click Chemistry and Applications ; Nanoplatforms for cancer theranostics ; Synthesis and biological activity