jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ammara Abdullah | Talha Saleem
Source: Cancer Research and Medicine (article, 2025-07-27)
Field: Chemistry — Organic Chemistry

Erufosine mediated regulation of apoptotic, cell cycle and cell survival genes in breast cancer cells Background: Breast cancer is a common cancer in females worldwide with continuously growing incidence over the decades. Novel compounds are needed to treat breast cancer effectively. Erufosine, a synthetic lipid, is a promising anticancer agent that acts on cell membranes to induce antitumor effects and interfere with lipid homeostasis leading to toxic effects. Methods: Toxic effects of erufosine on human breast cancer cell lines were determined by MTT dye assay. Cell lines (MCF-7 and MDA-MB-231) were cultured and treated with erufosine for 24-72 h. Expressional changes in apoptosis (BCL2), cell cycle (CDKN1A) and cell survival (MYB) related genes were determined by real-time PCRs. Results: The compound induced cytotoxic effects in the cells in concentration dependent format. Both cancer cell lines were found sensitive towards the exposure with erufosine. Selected genes were de-regulated by the compound in cell specific format. Cell cycle inhibitor gene (CDKN1A) was up-regulated while cell survival (MYB) was down-regulated in the cells. Apoptosis (BCL2) gene showed a variable response in the two cell lines. Conclusion: Erufosine induces cell death in breast cancer cells and de-regulate the associated genes. Erufosine mediated observed functional and molecular changes in breast cancer cells highlight its antineoplastic potential. Apoptosis ; Cell cycle ; Gene ; Breast cancer ; Cell ; Cancer research ; Cancer ; Biology ; Cell biology ; Oncology ; Medicine ; Genetics Carbohydrate Chemistry and Synthesis
Authors: Roya Majidi | Mohaddeseh Saffari | Ahmad I. Ayesh
Source: Physica Scripta (article, 2025-08-01)
Field: Chemistry — Organic Chemistry

Optical behavior of rectangular graphyne and graphdiyne sheets with phenyl building blocks: a density functional theory study Abstract In this study, we investigate recently reported rectangular graphyne and graphdiyne sheets, which are composed of units resembling phenyl rings interconnected by acetylenic chains, similar to hexagonal γ -graphyne. The optical properties of these sheets, referred to as rectangular γ -graphyne (r γ -GY) and rectangular γ -graphdiyne (r γ -GDY), have been studied by density functional theory. Their dielectric constants, absorption coefficients, optical conductivities, and transmission coefficients across a broad range of photon energies are calculated. Our findings reveal high dielectric constants of r γ -GY and r γ -GDY sheets. Their absorption spectra of these sheets, spanning from infrared to ultraviolet regions, indicate their potential as effective absorbers. Additionally, the transmission coefficients highlight high transparency of r γ -GY and r γ -GDY sheets, especially in the ultraviolet range. Overall, the optical properties of these materials suggest their promising applications in energy harvesting systems and optoelectronic devices. Graphyne ; Density functional theory ; Materials science ; Physics ; Quantum mechanics Synthesis and Properties of Aromatic Compounds ; Liquid Crystal Research Advancements
Authors: Lalit Mohan Nainwal | Poonam Arora | Vanshika Bansal | Priyanshu Poonia | Shwetakshi Sharma | Pallavi Barik | Nidhi Tiwari
Source: Asian Journal of Chemistry (article, 2025-11-30)
Field: Chemistry — Organic Chemistry

Pyrimidine Scaffolds as Versatile Platforms for Therapeutic Potential: A Review Nitrogen-bearing heterocycles continue to shape modern medicinal chemistry owing to their structural adaptability and capacity to participate in diverse biochemical interactions. Within this class, pyrimidine frameworks have emerged as especially influential, providing a chemically economical core from which varied pharmacological profiles can be engineered. Their modular architecture supports precise functionalization, enabling the design of molecules with activities spanning antiviral, anticancer, anti-inflammatory, antibacterial, antitubercular, antimalarial and neuroactive domains. Recent studies demonstrate that integrating pyrimidine cores with complementary heterocycles particularly thiazole units can substantially enhance target engagement, strengthen metabolic stability and yield more favourable pharmacodynamic profiles. This review summarizes the current progress in the development of pyrimidine-based scaffolds, emphasizing the relationship between structural modification and biological response. The collective findings underscore the continued relevance of pyrimidine chemistry as a driver in the discovery of next-generation therapeutic agents. Pyrimidine ; Thiazole ; Adaptability ; Modular design ; Metabolic stability ; Small molecule Multicomponent Synthesis of Heterocycles ; Synthesis and Reactivity of Heterocycles ; Synthesis and biological activity
Authors: Soumya Jyoti Basak | Jyotirmayee Dash
Source: Chemical Communications (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

KO<sup><i>t</i></sup>Bu-mediated annulation for the pot-economical synthesis of quinazolinobenzoxazepines A novel and cost-effective KO<sup><i>t</i></sup>Bu-mediated protocol has been developed for synthesizing quinazolinobenzoxazepines from 2-aminobenzonitriles and 2-(2-bromoethoxy)benzaldehydes. This transformation proceeds <i>via</i> a cascade pathway and affords products in moderate to good yields with broad substrate scope and excellent functional group tolerance. Annulation ; Cascade ; Combinatorial chemistry ; Computer science ; Transformation (genetics) ; Chemistry ; Protocol (science) ; Catalysis ; Organic chemistry Quinazolinone synthesis and applications ; Phenothiazines and Benzothiazines Synthesis and Activities ; Multicomponent Synthesis of Heterocycles
Authors: Marija Bartolić | Anita Bosak
Source: Periodicum Biologorum (article, 2025-09-30)
Field: Chemistry — Spectroscopy

Overview of BACE1 structure and effect of genetic polymorphisms and protein isoforms on enzyme activity One of the major hallmarks of Alzheimer’s disease (AD), deposition of amyloid plaques in the brain, arises from the proteolytic processing of the amyloid precursor protein (APP), a process in which the rate-limiting step is carried by β-secretase (BACE1). Due to this essential role in the development of AD, BACE1 has become a widely studied drug target for the development of new drugs aimed at blocking amyloid β peptide formation and aggregation through inhibition of BACE1 activity. The development of the first BACE1 inhibitors allowed the determination of its crystal structure and description of its active site, confirming the existence of eleven well-defined subsites able to accommodate a range of substrates, which enabled BACE1 to participate in a number of biological processes. For this reason, the study of BACE1 structure and determination of specific structural motifs responsible for the accommodation of known substrates could enable defining drugs with very specific targets, as well as provide insight into the mode of action of BACE1 itself. This review will focus on the overall structure of BACE1 and its active site, as well as on the effect of genetic polymorphisms and isoforms on its activity. Gene isoform ; Enzyme ; Drug development ; Peptide ; Amyloid precursor protein ; Amyloid precursor protein secretase ; Drug ; Amyloid (mycology) ; Mode of action Advanced Proteomics Techniques and Applications ; Biomedical Text Mining and Ontologies ; Enzyme Production and Characterization
Authors: Mustafa Talib | A. H. Ulaiwi
Source: IRAQI JOURNAL OF AGRICULTURAL SCIENCES (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

IMMUONOLOGICAL AND BIOCHEMICAL EVALUATION OF AVIAN INFLUEZA VACCINES SUPPLEMENTED WITH CHROMIUM IN LAYERS This study was aimed to investigate the immune response of the vaccinated chickens through avian influenza virus antibody (AIV-Ab) titers as well as evaluate liver function. The study measure of immunological parameters, namely the hemaglutinine inhibition test (HI) and a biochemical parameter called the liver function test, as well as a comparison between the highly pathogenic avian influenza (HPAI) recombinant (rH5N1) vaccine and the classical (cH5N8) vaccine. The study was designed as follows: {G1: given rH5N1 vaccine only; G2: given cH5N8 only; G3: given rH5N1 with Cr III supplement; G4: given cH5N8 with Cr III supplement; G5: given Cr III supplement; G6: negative control with no vaccination or supplementation}. The titer of AIV-Ab at 21 days old showed a significant increase in G3 as compared with other groups. At 42 and 60 days old, G3 and G4 revealed the highest levels as compared with another groups. The results of ALT enzyme levels at 30 and 60 days old showed the lowest levels in G3 and G5 as compared to other groups. Additionally, there was a significant decrease in AST enzyme levels in all groups except G6 at both ages 30 and 60 days old. In conclusion, Cr III supplementation can improve the immune response against AI vaccines and has a positive impact on liver health. Chromium ; Virology ; Biology ; Computational biology ; Materials science ; Metallurgy Nanoparticles: synthesis and applications
Authors: Xiaobo Lin (691229) | Debsindhu Bhowmik (2545723) | Logan T. Kearney (8408748) | Amit K. Naskar (1469602)
Source: Figshare (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

RLMolLM: Reinforcement Learning-Enhanced LanguageModel Framework for Inverse Molecular Design Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. This versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation. Novelty ; Reinforcement learning ; Property (philosophy) ; Chemical space ; Inverse ; Language model ; Class (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiaobo Lin | Debsindhu Bhowmik | Logan T. Kearney | Amit K. Naskar
Source: Journal of Chemical Information and Modeling (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. This versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation. Novelty ; Reinforcement learning ; Inverse ; Chemical space ; Property (philosophy) ; Language model ; Class (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Haiyan Gong | Lingwei Ma | Dongfang Liu | Dawei Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Ai-Driven Discovery of High-Performance Corrosion Inhibitors Using a Bert-Gpt Framework for Molecular Generation Corrosion ; Computer science ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Dongkwan Ryu | Jae‐Hoon Kim
Source: Expert Systems with Applications (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Rule enforced graph growing for molecular design by lightweight reinforcement learning Computer science ; Reinforcement learning ; Graph ; Reinforcement ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Materials science ; Composite material Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Surface Chemistry and Catalysis
Authors: Dominik Dekleva | Alexey Voronov | Jon Paul Janet | Albin Ekborg | Jure Borišek | Martina Hrast | Hannes H. Loeffler
Source: ChemRxiv (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Synthesizability via Reward Engineering: Expanding Generative Molecular Design into Synthetic Space Chemical space ; Workflow ; Reinforcement learning ; Synthetic data ; Synthetic biology ; Generative grammar ; Coherence (philosophical gambling strategy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Luisetto M | Edbey K | Abdul Haamid G | Mashori GR | Gadama GP | Cabianca L | Latyshev OY
Source: Mathews Journal of Pharmaceutical Science (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemistry: Evaluating Innovations and Risks in Research and Applications Aim: This study evaluates the applications, benefits, and risks of artificial intelligence (AI) in chemical research and industrial practice.Methods: A literature review assessed AI's role across chemical disciplines, followed by an experimental project testing a publicly accessible AI chatbot's accuracy in answering fundamental chemistry questions.Results: The literature highlights AI's transformative potential in drug discovery, materials design, and process optimization but underscores persistent risks, including errors (e.g., "hallucinations") and ethical concerns.In the experimental phase, the chatbot answered 27 of 28 chemistry questions (96.43%) correctly; the error involved an incorrect chemical structure.Conclusion: While AI tools offer significant utility in chemistry, human oversight remains critical to mitigate risks.Accuracy varies by model version and prompt specificity, necessitating cautious adoption in education and research. Applications of artificial intelligence ; Expert system ; Key (lock) ; Risk assessment Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Artificial Intelligence in Healthcare and Education
Authors: Filipe Menezes | Tony Froehlich | Lucy C. Schofield | Julian Cremer | Jon Agirre | Robbie P. Joosten | Raphael Bourgeas | Sihyun Sung | José Antonio Marquez | Garib N. Murshudov | Oliver Plettenburg | Michael Sattler | Grzegorz M. Popowicz
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-27)
Field: Materials Science — Materials Chemistry

It’s Not Always Crystal Clear: In-Pocket Analysis for Understanding Binding Site Microenvironment Properties ABSTRACT Structural biology enabled key advances in biochemistry, drug discovery, and AI-driven three-dimensional structure modelling. Despite its importance, reliable and general computational tools for chemical validation of structural models remain limited, especially when small-molecule ligands are involved. This challenge is exacerbated when experimental resolution is insufficient to define interaction chemistry, leaving critical interpretations ambiguous. We introduce In-Pocket, a quantum mechanical optimisation method biased by experimental data that evaluates the chemical nature of protein-ligand binding interfaces. In-Pocket improves model quality, corrects chemically inconsistent structures, and identifies the energetically most favourable ligand-conformers within biomacromolecular complexes. The approach is generally applicable to a wide range of systems, including those containing transition metals. Benchmarking demonstrates that In-Pocket enhances the reliability of legacy and newly solved structures. With automated, user-friendly routine chemical validation, In-Pocket offers unique opportunities to improve the integrity of structural databases and impact diverse domains: from molecular biology to rational drug design and application of generative AI. Quantum chemical ; Reliability (semiconductor) ; Key (lock) ; Benchmarking ; Structural biology ; Rational design ; Chemical space ; Binding site ; Range (aeronautics) Machine Learning in Materials Science
Authors: Jiří Navrátil | Jarret Ross | Payel Das | Youssef Mroueh | Samuel C. Hoffman | Vijil Chenthamarakshan | Brian Belgodere
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

GP-MoLFormer-Sim: Test Time Molecular Optimization through Contextual Similarity Guidance The ability to design molecules while preserving similarity to a target molecule and/or property is crucial for various applications in drug discovery, chemical design, and biology. We introduce in this paper an efficient training-free method for navigating and sampling from the molecular space with a generative Chemical Language Model (CLM), while using the molecular similarity to the target as a guide. Our method leverages the contextual representations learned from the CLM itself to estimate the molecular similarity, which is then used to adjust the autoregressive sampling strategy of the CLM. At each step of the decoding process, the method tracks the distance of the current generations from the target and updates the logits to encourage the preservation of similarity in generations. We implement the method using a recently proposed $\sim$47M parameter SMILES-based CLM, GP-MoLFormer, and therefore refer to the method as GP-MoLFormer-Sim, which enables a test-time update of the deep generative policy to reflect the contextual similarity to a set of guide molecules. The method is further integrated into a genetic algorithm (GA) and tested on a set of standard molecular optimization benchmarks involving property optimization, molecular rediscovery, and structure-based drug design. Results show that, GP-MoLFormer-Sim, combined with GA (GP-MoLFormer-Sim+GA) outperforms existing training-free baseline methods, when the oracle remains black-box. The findings in this work are a step forward in understanding and guiding the generative mechanisms of CLMs. Similarity (geometry) ; Oracle ; Set (abstract data type) ; Property (philosophy) ; Sampling (signal processing) ; Generative model ; Generative grammar ; Test set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Revista, Zen | CHEMISTRY, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-28)
Field: Chemistry — Organic Chemistry

Strategic Frontiers: Unlocking the Total Synthesis of Architecturally Complex Natural Products The total synthesis of architecturally complex natural products stands as a pinnacle of organic chemistry. These molecules, vital for drug discovery and materials science, possess profound biological activities and intricate structural features. Characterized by multiple contiguous stereocenters, congested polycyclic frameworks, and diverse functional groups, they pose significant synthetic challenges. This paper explores the strategic frontiers redefining complex natural product total synthesis. We delve into advancements such as AI-powered retrosynthetic analysis, cutting-edge catalytic methods including C-H activation and visible-light photoredox catalysis, and the integration of biocatalysis for enhanced selectivity and sustainability. Furthermore, we examine the emergence of automated and flow chemistry platforms, along with the principles of divergent and fragment-based synthesis to access molecular diversity efficiently. By embracing these interdisciplinary strategies, synthetic chemists overcome long-standing challenges, paving the way for more efficient, sustainable, and scalable routes. This synergistic combination accelerates drug discovery, materials science, and minimizes environmental impact. Total synthesis ; Natural product ; Natural (archaeology) ; Synthetic biology ; Retrosynthetic analysis ; Drug discovery ; Biocatalysis Radical Photochemical Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation ; Cyclopropane Reaction Mechanisms
Authors: M Esmail Ehab | Menna S. Hany | Karen Tamer | Peter Emil | Marwa S. Hany | Tamer M. Ibrahim | Amal Aboulhassan
Source:  (article, 2025-05-10)
Field: Chemistry — Organic Chemistry

A Deep Learning Pipeline for Generating New PTR1 Inhibitors Targeting Leishmania Disease Drug discovery and material design aim at finding new molecules with properties of interest. However the process is expensive and time consuming. To address these limitations, Artificial Intelligence (AI) has been introduced recently in literature. In this paper, AI tools were utilized to generate new compounds that target the Leishmania disease for the first time. A combination of Variational Auto encoders and Reinforcement learning were trained on newly collected dataset as well as Zinc dataset. The validity and quality of generated compounds were evaluated using two methods: a collection of computed metrics and docking scores. The results achieved high scores with respect to validity and uniqueness which demonstrates the potential of this framework to operate on larger datasets relevant to our research. Pipeline (software) ; Computer science ; Leishmania ; Medicine ; Operating system ; World Wide Web Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Cytokine Signaling Pathways and Interactions