jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rahul Sheshanarayana | Fengqi You
Source: Advanced Science (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis Knowledge distillation (KD) is a powerful model compression technique that transfers knowledge from complex teacher models to compact student models, reducing computational costs while preserving predictive accuracy. This study investigated KD's efficacy in molecular property prediction across domain-specific and cross-domain tasks, leveraging state-of-the-art graph neural networks (SchNet, DimeNet++, and TensorNet). In the domain-specific setting, KD improved regression performance across diverse quantum mechanical properties in the QM9 dataset, with DimeNet++ student models achieving up to an 90% improvement in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn></mml:msup> <mml:annotation>$R^{2}$</mml:annotation></mml:semantics> </mml:math> compared to non-KD baselines. Notably, in certain cases, smaller student models achieved comparable or even superior <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msup><mml:mi>R</mml:mi> <mml:mn>2</mml:mn></mml:msup> <mml:annotation>$R^{2}$</mml:annotation></mml:semantics> </mml:math> improvements while being 2× smaller, highlighting KD's ability to enhance efficiency without sacrificing predictive performance. Cross-domain evaluations further demonstrated KD's adaptability, where embeddings from QM9-trained teacher models enhanced predictions for ESOL (logS) and FreeSolv (ΔG<sub>hyd</sub>), with SchNet exhibiting the highest gains of ≈65% in logS predictions. Embedding analysis revealed substantial student-teacher alignment gains, with the relative shift in cosine similarity distribution peaks reaching up to 1.0 across student models. These findings highlighted KD as a robust strategy for enhancing molecular representation learning, with implications for cheminformatics, materials science, and drug discovery. Computer science ; Scalability ; Embedding ; Cheminformatics ; Artificial intelligence ; Machine learning ; Graph ; Domain knowledge ; Property (philosophy) ; Domain (mathematical analysis) ; Similarity (geometry) ; Theoretical computer science ; Chemistry ; Mathematics ; Database ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Muhammad Waleed Iqbal | Muhammad Shahab | Zakir Ullah | Guojun Zheng | İrfan Anjum | Gamal A. Shazly | Atrsaw Asrat Mengistie | Xinxiao Sun | Qipeng Yuan
Source: Scientific Reports (article, 2025-01-13)
Field: Chemistry — Organic Chemistry

Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. The performance evaluation of these models demonstrated SVM as the best model, which was further utilized to shortlist 51 highly potent compounds by screening an FDA-approved library of 1040 drugs. Molecular docking and molecular dynamic simulation were subsequently employed to evaluate the binding affinity and stability of the proposed compounds. Orlistat, acarbose and afatinib were identified as the potent leads, demonstrating stable conformations and stronger interactions, validated by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bond analyses. Molecular Mechanics/Generalized Born Surface Area (MMGBSA) analysis validated their binding affinities by providing comparably lower binding free energies for orlistat (- 33.4743 ± 3.8908), acarbose (- 19.5455 ± 5.4702), and afatinib (- 36.4944 ± 5.4929) than the control, dasatinib (- 13.7785 ± 5.8058). Finally, toxicity analysis revealed orlistat and acarbose as the possible safer therapeutics by eliminating afatinib as it showed significant toxicity concerns. Our investigation supports the advance computational methods utilization in the field of drug discovery and suggest further experimental validation of proposed inhibitors of Src kinase for their safer use against inflammatory diseases. The ultimate aim of this study is to advance the development of effective treatments for inflammatory diseases, linked with Src overexpression. Dasatinib ; Proto-oncogene tyrosine-protein kinase Src ; Docking (animal) ; Tyrosine kinase ; Drug repositioning ; Virtual screening ; Computational biology ; Chemistry ; Support vector machine ; Drug discovery ; Pharmacology ; Machine learning ; Biochemistry ; Computer science ; Medicine ; Kinase ; Drug ; Biology ; Signal transduction Synthesis and biological activity ; Quinazolinone synthesis and applications ; Computational Drug Discovery Methods
Authors: Tomáš Prošek | Patrick Keil | Kateryna Popova
Source: Corrosion and Materials Degradation (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Corrosion Protection and Sustainability: Why Are the Two Concepts Inherently Intertwined Corrosion has a significant impact on the economic and environmental sustainability of metal-based infrastructure and products. This position paper explores the intrinsic relationship between corrosion protection and sustainability, examining the economic costs, environmental impacts and technological strategies involved. While corrosion results in resource waste, energy loss, and increased CO2 emissions, effective corrosion management can extend the service life of metallic components, thus preserving resources and minimizing environmental burden. The approaches such as Total Cost of Ownership (TCO) and Life Cycle Analysis (LCA) can provide a framework for selecting the most cost-efficient and environmentally friendly corrosion protection method in view of the required lifetime. The paper emphasises the crucial role of material selection, design optimization, recyclability and environmentally friendly coatings. Regulatory pressures and new trends such as machine learning are also discussed. Achieving sustainability goals requires greater awareness, education, interdisciplinary collaboration, and continued innovation in corrosion protection strategies. Sustainability ; Corrosion ; Risk analysis (engineering) ; Business ; Materials science ; Metallurgy ; Biology ; Ecology Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Marine Biology and Environmental Chemistry
Authors: S.I. Woo | Nack J. Kim | Jung-Wook Wee
Source: Polymer Degradation and Stability (article, 2025-06-16)
Field: Materials Science — Biomaterials

In vitro temperature-dependent degradation of polylactic acid: Experimental characterization and neural network-based prediction Polylactic acid ; Degradation (telecommunications) ; Characterization (materials science) ; Artificial neural network ; In vitro ; Materials science ; Biological system ; Composite material ; Chemical engineering ; Chemistry ; Computer science ; Polymer ; Nanotechnology ; Engineering ; Biochemistry ; Biology ; Artificial intelligence ; Telecommunications biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Electrospun Nanofibers in Biomedical Applications
Authors: Haifa Qiu | Ming Yang | Haitao Huang
Source: ACS Applied Materials & Interfaces (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

DFT-Machine Learning Joint Exploration of Transition Metal-Doped Ferroelectric BaTiO<sub>3</sub> for Electrocatalytic Hydrogen Evolution Doping regulation holds promise to modulate electrocatalytic performance, yet it remains largely unexplored for ferroelectric (FE) BaTiO<sub>3</sub> (BTO). By jointly employing first-principles calculations and machine learning (ML) analysis, we examine the effect of a broad range of transition metal (TM) doping in FE BTO on the electrocatalytic hydrogen evolution reaction (HER) activity and screen out the optimal TM dopants. We unveil that some early-to-middle group TM (V, Cr, Mo, Ta, Ru)-doped BTO surfaces feature higher synthesizability, which also exhibit noticeable HER activity with |Δ<i>G</i><sub>H*</sub>| < 0.2 eV owing to intermediate hydrogen adsorption strength. Among all doped surfaces, the Mo-doped one shows optimal HER activity under both out-of-plane and in-plane polarization states. We reveal an intense interplay between the hydrogen adsorption configuration and the corresponding hydrogen bonding interaction, which relies more on the TM group than the polarization state. Most importantly, we propose a physically informed descriptor of the surface oxygen <i>p</i> band, which better describes HER activity trends of TM-doped surfaces than conventional band descriptors.This indicates the significance of the fractional filling and bandwidth of occupied oxygen <i>p</i>-band states. Moreover, we establish a robust ML model that can well predict HER activity with surface-independent input parameters alone with <i>R</i><sup>2</sup> value above 0.93. From these parameters, we identify the inherent outer electron number of the TM dopant as the dominant feature, while the second ionization energy of the TM dopant and the initial polarization state show non-negligible feature importance. These findings could enlighten understanding, rational design, and accelerated discovery of element doping of FE materials for catalysis and other implications. Materials science ; Ferroelectricity ; Doping ; Joint (building) ; Transition metal ; Hydrogen ; Nanotechnology ; Electrocatalyst ; Optoelectronics ; Physical chemistry ; Catalysis ; Electrochemistry ; Electrode ; Dielectric ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electronic and Structural Properties of Oxides
Authors: Milan Dhara | Sanjoy Banerjee | Hemanta Naskar | Barnali Ghatak | Sk Babar Ali | Nityananda Das | Amit Kumar Chakraborty | Rajib Bandyopadhyay | Bipan Tudu
Source: IEEE Sensors Journal (article, 2025-03-18)
Field: Chemistry — Organic Chemistry

Synthesis of HNQ-Imprinted Poly(Methyl Acrylate) Using Hybrid Polymerization for the Detection of Lawsone in Henna Powder In this work, a hybrid polymerization technique, combining sol-gel polymerization with thermal polymerization, was prescribed for the preparation of graphite-supported lawsone-imprinted poly[methyl acrylate (MA)] (PMA@HNQ-G) as a sensing material. The imprinted polymer’s morphology, structure, and imprinting endorsement were thoroughly investigated using various analytical techniques, including field-emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), UV-visible (UV-Vis) spectroscopy, Brunauer-Emmett–Teller (BET) analysis, and energy-dispersive X-ray (EDX) spectroscopy. The pH 6.0 phosphate buffer solution (PBS) was selected for the electrochemical investigation of lawsone (HNQ). Under optimized experimental conditions, the calibration curve was linear over two concentration ranges of 0.05–1.0 and 1.0–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300~\mu $ </tex-math></inline-formula>M with a detection limit of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.001~\mu $ </tex-math></inline-formula>M and detection quantification of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.005~\mu $ </tex-math></inline-formula>M by using the differential pulse voltammetry (DPV) technique. To evaluate the practical applicability of the PMA@HNQ-GPE, it was employed for the quantification of HNQ in commercial henna made from the leaves of the henna plant (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lawsonia inermis</i>). The sensor’s response was validated by the partial least squares regression (PLSR) technique that helps to established a robust statistical model to correlate the sensor’s output with the actual lawsone concentrations measured by reversed-phase high performance liquid chromatography (RP-HPLC) technique. The model showed a high prediction accuracy of 95.65% for the detection of HNQ in the cosmetic samples. This approach offers a combination of analytical chemistry (RP-HPLC) and machine learning/statistics (PLSR) to create a validated, reliable method for lawsone detection in cosmetic products, offering potential benefits for quality control and safety assessment in the cosmetic industry. Lawsone ; Polymerization ; Acrylate ; Materials science ; Polymer chemistry ; Molecularly imprinted polymer ; Chemistry ; Polymer ; Organic chemistry ; Copolymer ; Composite material ; Catalysis ; Selectivity Synthesis and Biological Evaluation ; Analytical chemistry methods development ; Analytical Chemistry and Chromatography
Authors: Ivan Anishchenko | Yakov Kipnis | Indrek Kalvet | Guangfeng Zhou | Rohith Krishna | Samuel J. Pellock | Anna Lauko | Gyu Rie Lee | Linna An | Justas Dauparas | Frank DiMaio | David Baker
Source: Proceedings of the National Academy of Sciences (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Modeling protein–small molecule conformational ensembles with PLACER Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state, an entirely atomic-level description could have advantages in speed and generality. We developed a graph neural network called PLACER (protein-ligand atomistic conformational ensemble resolver) trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. PLACER accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding. When given a description of the larger protein context, it builds up structures of small molecules and protein side chains for protein-small molecule docking. Because PLACER is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using PLACER to assess the accuracy and preorganization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with a <i>k</i><sub>cat</sub>/<i>K</i><sub>M</sub> of 11,000 M<sup>-1</sup>min<sup>-1</sup>, considerably higher than any pre-deep learning design for this reaction. We anticipate that PLACER will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems and for designing higher activity preorganized enzymes. Small molecule ; Molecule ; Biomolecule ; Molecular dynamics ; Graph ; Atom (system on chip) Enzyme Structure and Function ; Protein Structure and Dynamics ; Peptidase Inhibition and Analysis
Authors: Li Guo | Qin Gao | Mengyi Zhang | Peng Cheng | Peng He | Lu‐Jun Li | Dong Ding | Changcheng Liu | Francis Collins Muga | Masroor Kamal | Jiangtao Qi
Source: Agriculture (article, 2025-06-19)
Field: Chemistry — Analytical Chemistry

Soil Organic Matter Content Prediction Using Multi-Input Convolutional Neural Network Based on Multi-Source Information Fusion Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R2 of 0.87 and an RMSE of 1.68, a 23% improvement in R2 compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R2 improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Computer science ; Dimensionality reduction ; Pattern recognition (psychology) ; Soil organic matter ; Random forest ; Correlation coefficient ; Artificial neural network ; Support vector machine ; Remote sensing ; Environmental science ; Soil science ; Machine learning ; Soil water Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Soil Geostatistics and Mapping
Authors: Xian Li | Tiantian Zhang | Ziwei Liu | Jiao Meng | Qian Li | Martin Gand | Kexin Zhu | Yanjun Qiao | Wushuang Bai | Zisheng Guo | Bin Li | Yiran Wang | Jing Dong | Binglin Li
Source: Food Chemistry X (article, 2025-02-01)
Field: Chemistry — Spectroscopy

Machine learning analysis of pre-culture effects on rate-limiting steps in volatile compound dynamics of Mead A novel two-step fermentation process was developed to enhance mead flavor quality. Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) with three columns was used to analyze the volatile profiles of meads, along with sensory evaluation and machine learning. Compared to traditional mead (TM), our novel mead (NM) reduced off-flavor compounds by 37.6 %, with isoamyl alcohol decreasing 1.26-fold and ethyl laurate 2.09-fold. Meanwhile, aromatic compounds increased by 39.41 %, with isoamyl acetate rising 3.31-fold, ethyl caproate 2.79-fold, and phenylethyl alcohol 1.69-fold. Sensory evaluation revealed a significant reduction in bitterness (41.1 %) and irritation (42.5 %), while fruity, sweet, and pleasantly sour flavors increased by 27.4 %, 36.9 %, and 45.5 % for NM. Key aroma compounds (benzaldehyde, 2,3-butanediol, cedrol) were identified via recombination and omission experiments. Dynamic monitoring and machine learning identified key rate-limiting steps, including the oxidation of benzeneacetaldehyde (phenylethyl alcohol synthesis), isovaleraldehyde (isoamyl alcohol synthesis), and the conversion of octanoic acid to decanoic acid. Limiting ; Dynamics (music) ; Psychology ; Chemistry ; Computer science ; Engineering ; Pedagogy Analytical Chemistry and Chromatography ; Plant biochemistry and biosynthesis ; Fermentation and Sensory Analysis
Authors: Shicheng Dong | Wenhao Wang | Jun Zhu
Source: Inorganic Chemistry (article, 2025-09-03)
Field: Chemistry — Organic Chemistry

Design of Borole- and Silylene-Based Frustrated Lewis Pairs for Dinitrogen Activation Promoted by Aromaticity: A Combined DFT and Machine Learning Study In recent years, while main-group elements from the <i>s</i>- and <i>p</i>-block have emerged in the field of N<sub>2</sub> activation, silylenes─despite their remarkable successes in the activation of diverse small molecules─remain unreported for N<sub>2</sub> activation. Herein, we design "silylene-borole" frustrated Lewis pairs (FLPs) by combining silylene moieties with boron components and conduct comprehensive density functional theory (DFT) calculations to thoroughly investigate their potential for N<sub>2</sub> activation. Screening silylene moieties via machine learning suggests that a smaller Δ<i>E</i><sub>ST</sub> value will benefit N<sub>2</sub> activation thermodynamically. Interestingly, all these designed novel FLPs were found to be kinetically and thermodynamically favorable for N<sub>2</sub> activation, exhibiting low reaction barriers (ranging from 16.9 to 22.5 kcal/mol) and significant exergonicity, with Δ<i>G</i> values ranging from -32.3 to -36.7 kcal/mol. The notable thermodynamic stability of the products, enhanced by aromaticity, coupled with low activation barriers, represents a pivotal advancement in the development of FLP-chemistry for metal-free N<sub>2</sub> activation. Chemistry ; Silylene ; Aromaticity ; Computational chemistry ; Lewis acids and bases ; Frustrated Lewis pair ; Density functional theory ; Organic chemistry ; Silicon ; Molecule ; Catalysis Organoboron and organosilicon chemistry ; Inorganic Chemistry and Materials ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Juanxia Wang | Xu Shao | Chao Wang | Shu-Min Wu | Mengyao Bai | Pengtao Zhang | Selene Tang | Nan Zhang | X Wang | Zhen Yang | Xiao Hu | Ping Guan | Zhaoyuan Lyu | Shichao Ding
Source: Biosensors and Bioelectronics (review, 2025-06-25)
Field: Chemistry — Spectroscopy

Small-molecule fluorescence probes for biomarkers of Alzheimer's disease Nanotechnology ; Disease ; Neuroscience ; Biomarker ; Chemistry ; Computational biology ; Medicine ; Pathology ; Biology ; Materials science ; Biochemistry Molecular Sensors and Ion Detection ; Cholinesterase and Neurodegenerative Diseases ; Alzheimer's disease research and treatments