jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vladimir Shalgunov | Sara Lopes van den Broek | Ida Vang Andersen | Rocío García‐Vázquez | Nakul Ravi Raval | Mikael Palner | Yuki Mori | Gabriela Schäfer | Barbara Herrmann | Hannes Mikula | Natalie Beschörner | Maiken Nedergaard | Stina Syvänen | Matthias Barz | Gitte M. Knudsen | Umberto Maria Battisti | Matthias M. Herth
Source: RSC Medicinal Chemistry (article, 2022-12-02)
Field: Chemistry — Organic Chemistry

Pretargeted imaging beyond the blood–brain barrier Pretargeting is a powerful nuclear imaging strategy to achieve enhanced imaging contrast for nanomedicines and reduce the radiation burden to healthy tissue. Pretargeting is based on bioorthogonal chemistry. The most attractive reaction for this purpose is currently the tetrazine ligation, which occurs between <i>trans</i>-cyclooctene (TCO) tags and tetrazines (Tzs). Pretargeted imaging beyond the blood-brain barrier (BBB) is challenging and has not been reported thus far. In this study, we developed Tz imaging agents that are capable of ligating <i>in vivo</i> to targets beyond the BBB. We chose to develop <sup>18</sup>F-labeled Tzs as they can be applied to positron emission tomography (PET) - the most powerful molecular imaging technology. Fluorine-18 is an ideal radionuclide for PET due to its almost ideal decay properties. As a non-metal radionuclide, fluorine-18 also allows for development of Tzs with physicochemical properties enabling passive brain diffusion. To develop these imaging agents, we applied a rational drug design approach. This approach was based on estimated and experimentally determined parameters such as the BBB score, pretargeted autoradiography contrast, <i>in vivo</i> brain influx and washout as well as on peripheral metabolism profiles. From 18 initially developed structures, five Tzs were selected to be tested for their <i>in vivo</i> click performance. Whereas all selected structures clicked <i>in vivo</i> to TCO-polymer deposited into the brain, [<sup>18</sup>F]18 displayed the most favorable characteristics with respect to brain pretargeting. [<sup>18</sup>F]18 is our lead compound for future pretargeted neuroimaging studies based on BBB-penetrant monoclonal antibodies. Pretargeting beyond the BBB will allow us to image targets in the brain that are currently not imageable, such as soluble oligomers of neurodegeneration biomarker proteins. Imaging of such currently non-imageable targets will allow early diagnosis and personalized treatment monitoring. This in turn will accelerate drug development and greatly benefit patient care. Pretargeting ; Bioorthogonal chemistry ; Positron emission tomography ; In vivo ; Nuclear imaging ; Tetrazine ; Chemistry ; Blood–brain barrier ; Neuroimaging ; Molecular imaging ; Preclinical imaging ; Biomedical engineering ; Nuclear medicine ; Medicine ; Radioimmunotherapy ; Combinatorial chemistry ; Click chemistry ; Monoclonal antibody Click Chemistry and Applications ; Radiopharmaceutical Chemistry and Applications ; Monoclonal and Polyclonal Antibodies Research
Authors: Vivian R. Feig | Eva Remlova | Benjamin Muller | Johannes Kuosmanen | Nikhil B. Lal | Anna I. Ginzburg | Kewang Nan | Ashka Patel | Ahmad Mujtaba Jebran | Meghana Prabhu Bantwal | Niora Fabian | Keiko Ishida | J. A. Jenkins | Jan‐Georg Rosenboom | Sang Hyun Park | Wiam Abdalla Mohammed Madani | Alison Hayward | Giovanni Traverso
Source: Advanced Materials (article, 2022-11-02)
Field: Materials Science — Biomaterials

Actively Triggerable Metals via Liquid Metal Embrittlement for Biomedical Applications Actively triggerable materials, which break down upon introduction of an exogenous stimulus, enable precise control over the lifetime of biomedical technologies, as well as adaptation to unforeseen circumstances, such as changes to an established treatment plan. Yet, most actively triggerable materials are low-strength polymers and hydrogels with limited long-term durability. By contrast, metals possess advantageous functional properties, including high mechanical strength and conductivity, that are desirable across several applications within biomedicine. To realize actively triggerable metals, a mechanism called liquid metal embrittlement is leveraged, in which certain liquid metals penetrate the grain boundaries of certain solid metals and cause them to dramatically weaken or disintegrate. In this work, it is demonstrated that eutectic gallium indium (EGaIn), a biocompatible alloy of gallium, can be formulated to reproducibly trigger the breakdown of aluminum within different physiologically relevant environments. The breakdown behavior of aluminum after triggering can further be readily controlled by manipulating its grain structure. Finally, three possible use cases of biomedical devices constructed from actively triggerable metals are demonstrated. Materials science ; Nanotechnology ; Eutectic system ; Gallium ; Liquid metal ; Alloy ; Metallurgy Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Max Nanao | Shibom Basu | U. Zander | Thierry Giraud | John Surr | Matias Guijarro | Mario Lentini | Franck Felisaz | Jérémy Sinoir | Christian Morawe | Amparo Vivo | Antonia Beteva | Marcus Oscarsson | Hugo Caserotto | Fabien Dobias | David Flot | Didier Nurizzo | Jonathan Gigmes | Nicolas Foos | R. Siebrecht | Thomas Roth | Pascal Théveneau | Olof Svensson | Gergely Papp | Bernard Lavault | F. Cipriani | R. Barrett | C. Clavel | Gordon A. Leonard
Source: Journal of Synchrotron Radiation (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

ID23-2: an automated and high-performance microfocus beamline for macromolecular crystallography at the ESRF ID23-2 is a fixed-energy (14.2 keV) microfocus beamline at the European Synchrotron Radiation Facility (ESRF) dedicated to macromolecular crystallography. The optics and sample environment have recently been redesigned and rebuilt to take full advantage of the upgrade of the ESRF to the fourth generation Extremely Brilliant Source (ESRF-EBS). The upgraded beamline now makes use of two sets of compound refractive lenses and multilayer mirrors to obtain a highly intense (>10<sup>13</sup> photons s<sup>-1</sup>) focused microbeam (minimum size 1.5 µm × 3 µm full width at half-maximum). The sample environment now includes a FLEX-HCD sample changer/storage system, as well as a state-of-the-art MD3Up high-precision multi-axis diffractometer. Automatic data reduction and analysis are also provided for more advanced protocols such as synchrotron serial crystallographic experiments. Beamline ; Microbeam ; Synchrotron radiation ; Synchrotron ; Optics ; Diffractometer ; Upgrade ; Sample (material) ; High-energy X-rays ; Materials science ; Data reduction ; Physics ; Computer science ; Scanning electron microscope Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Alexander Pomberger | Nicholas A. Jose | D. Walz | Jens Meissner | C. Holze | Matthaeus Kopczynski | Philipp Müller-Bischof | Alexei A. Lapkin
Source: Chemical Engineering Journal (article, 2022-09-08)
Field: Materials Science — Materials Chemistry

Automated pH Adjustment Driven by Robotic Workflows and Active Machine Learning Buffer solutions have tremendous importance in biological systems and in formulated products. Whilst the pH response upon acid/base addition to a mixture containing a single buffer can be described by the Henderson-Hasselbalch equation, modelling the pH response for multi-buffered poly-protic systems after acid/base addition, a common task in all chemical laboratories and many industrial plants, is a challenge. Combining predictive modelling and experimental pH adjustment, we present an active machine learning (ML)-driven closed-loop optimization strategy for automating small scale batch pH adjustment relevant for complex samples (e.g., formulated products in the chemical industry). Several ML models were compared on a generated dataset of binary-buffered poly-protic systems and it was found that Gaussian processes (GP) served as the best performing models. Moreover, the implementation of transfer learning into the optimization protocol proved to be a successful strategy in making the process even more efficient. Finally, practical usability of the developed algorithm was demonstrated experimentally with a liquid handling robot where the pH of different buffered systems was adjusted, offering a versatile and efficient strategy for a pH adjustment processes. Computer science ; Usability ; Workflow ; Process (computing) ; Chemistry ; Process engineering ; Artificial intelligence ; Database ; Engineering Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Chenxu Wang | Sergey Akbulatov | Qihan Chen | Yancong Tian | Cai‐Li Sun | Marc Couty | Roman Boulatov
Source: Nature Communications (article, 2022-06-07)
Field: Chemistry — Organic Chemistry

The molecular mechanism of constructive remodeling of a mechanically-loaded polymer Large or repeated mechanical loads usually degrade polymers by accelerating fragmentation of their backbones but rarely, they can cause new backbone bonds to form. When these new bonds form faster than the original bonds break, mechanical degradation may be arrested or reversed in real time. Exploiting such constructive remodeling has proven challenging because we lack an understanding of the competition between bond-forming and bond-breaking reactions in mechanically-stressed polymers. Here we report the molecular mechanism and analysis of constructive remodeling driven by the macroradical products of mechanochemical fragmentation of a hydrocarbon backbone. By studying the changing compositions of a random copolymer of styrene and butadiene sheared at 10 °C in the presence of different additives we developed an approach to characterizing this growth/fracture competition, which is generalizable to other underlying chemistries. Our results demonstrate that constructive remodeling is achievable under practically relevant conditions, requires neither complex chemistries, elaborate macromolecular architectures or free monomers, and is amenable to detailed mechanistic interrogation and simulation. These findings constitute a quantitative framework for systematic studies of polymers capable of autonomously counteracting mechanical degradation at the molecular level. Polymer ; Fragmentation (computing) ; Constructive ; Mechanism (biology) ; Copolymer ; Chemistry ; Monomer ; Macromolecule ; Materials science ; Nanotechnology ; Chemical physics ; Computer science ; Process (computing) ; Organic chemistry ; Physics Advanced Polymer Synthesis and Characterization ; Polymer crystallization and properties ; Force Microscopy Techniques and Applications
Authors: Ahmad I. Ayesh
Source: Physica E Low-dimensional Systems and Nanostructures (article, 2022-02-04)
Field: Materials Science — Materials Chemistry

Investigation of NH3 adsorption on noble metal modified MoSe2 Noble metal ; Adsorption ; Materials science ; Metal ; Chemical engineering ; Chemistry ; Metallurgy ; Engineering ; Physical chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Thermoelectric Materials and Devices
Authors: Nehmedo G. Fawazy | Siva S. Panda | Ahmed Mostafa | Benson M. Kariuki | Mohamed S. Bekheit | Yassmin Moatasim | Omnia Kutkat | Walid Fayad | May A. El‐Manawaty | Ahmed A. F. Soliman | Riham A. El‐Shiekh | Aladdin M. Srour | Reham F. Barghash | Adel S. Girgis
Source: Scientific Reports (article, 2022-08-16)
Field: Chemistry — Organic Chemistry

Development of spiro-3-indolin-2-one containing compounds of antiproliferative and anti-SARS-CoV-2 properties A series of 1″-(alkylsulfonyl)-dispiro[indoline-3,2'-pyrrolidine-3',3″-piperidine]-2,4″-diones 6a‒o has been synthesized through regioselective multi-component azomethine dipolar cycloaddition reaction of 1-(alkylsulfonyl)-3,5-bis(ylidene)-piperidin-4-ones 3a‒h. X-ray diffraction studies (6b‒d,h) confirmed the structures. The majority of the synthesized analogs reveal promising antiproliferation properties against a variety of human cancer cell lines (MCF7, HCT116, A431 and PaCa2) with good selectivity index towards normal cell (RPE1). Some of the synthesized agents exhibit potent inhibitory properties against the tested cell lines with higher efficacies than the standard references (sunitinib and 5-fluorouracil). Compound 6m is the most potent. Multi-targeted inhibitory properties against EGFR and VEGFR-2 have been observed for the synthesized agents. Flow cytometry supports the antiproliferation properties and shows the tested agents as apoptosis and necrosis forming. Vero cell viral infection model demonstrates the anti-SARS-CoV-2 properties of the synthesized agents. Compound 6f is the most promising (about 3.3 and 4.8 times the potency of the standard references, chloroquine and hydroxychloroquine). QSAR models explain and support the observed biological properties. Cell culture ; Stereochemistry ; Chemistry ; Phenanthridine ; Piperidine ; Vero cell ; Pyrrolidine ; Flow cytometry ; Combinatorial chemistry ; In vitro ; Biochemistry ; Biology ; Molecular biology Synthesis and biological activity ; Bioactive Compounds and Antitumor Agents ; Synthesis and Catalytic Reactions
Authors: Haisong Feng | Hu Ding | Si Wang | Yujie Liang | Yuan Deng | Yusen Yang | Min Wei | Xin Zhang
Source: ACS Applied Materials & Interfaces (article, 2022-05-27)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Catalytic Performance Predictions of Single-Atom Alloys for Acetylene Semihydrogenation Selective semihydrogenation of acetylene for the production of polymer-grade ethylene is a significant chemical industrial process. Facile activization of acetylene and weak adsorption of ethylene are critical requirements for high-performance catalysis. Single-atom alloys (SAAs) have strong binding effect on acetylene and weak effect on ethylene, which have been regarded as the superior catalysts for acetylene semihydrogenation. Herein, we established a pioneering machine learning (ML) assisted approach to investigate the reaction activity and selectivity of 70 SAA catalysts for acetylene semihydrogenation. As the most desirable ML model, the gradient boosting regression (GBR) algorithm has been extended to predict the energy barrier of *C<sub>2</sub>H<sub><i>n</i></sub> (<i>n</i> = 2-4) hydrogenation with a root-mean-square error (RMSE) of only 0.02 eV. Notably, five candidate SAAs with excellent activity and selectivity for acetylene semihydrogenation are screened out via accessible descriptors. These data of ML prediction have been verified by DFT calculation with a high-accuracy (error less than 0.07 eV). This work demonstrates the potential of ML-assisted approach for predicting the energy barrier of transition state and simultaneously provides a convenient approach for the rational design of efficient catalysts. Acetylene ; Materials science ; Catalysis ; Selectivity ; Ethylene ; Atom (system on chip) ; Chemical engineering ; Organic chemistry ; Computer science ; Chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: Abdulelah S. Alshehri | Fengqi You
Source: Chemical Engineering Journal (article, 2022-04-28)
Field: Materials Science — Materials Chemistry

Deep learning to catalyze inverse molecular design Computer science ; Generative grammar ; Artificial intelligence ; Perspective (graphical) ; Deep learning ; Inverse ; Machine learning ; Management science ; Data science ; Engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Ajnabiul Hoque | Raghavan B. Sunoj
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions Molecular descriptors from a mechanistically important metal–ligand–substrate complex are used for the first time to build a Deep Neural Network (DNN) model to predict % ee. Accurate and chemically meaningful predictions could be obtained. Catalysis ; Substrate (aquarium) ; Ligand (biochemistry) ; Artificial neural network ; Deep neural networks ; Deep learning ; Chemistry ; Artificial intelligence ; Computer science ; Combinatorial chemistry ; Organic chemistry ; Biology ; Receptor Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Ke Chen | Christian Künkel | Karsten Reuter | Johannes T. Margraf
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening Reorganization energies of flexible molecules pose a challenge for machine learning (ML) models. Combining them with semiempirical electronic structure methods helps, but the benefit for virtual screening is surprisingly small. Virtual screening ; Organic molecules ; Computer science ; Molecule ; Artificial intelligence ; Virtual machine ; Machine learning ; Human–computer interaction ; Nanotechnology ; Materials science ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: Youngchun Kwon | Sun Kim | Youn-Suk Choi | Seokho Kang
Source: Journal of Chemical Information and Modeling (article, 2022-11-22)
Field: Materials Science — Materials Chemistry

Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions In synthesis planning, it is important to determine suitable reaction conditions such that a chemical reaction proceeds as intended. Recent research attempts based on machine learning have proven to be effective in recommending reaction elements for specific categories regarding critical chemical context and operating conditions. However, existing methods can only make a single prediction per reaction and do not directly provide a complete specification of the reaction elements as the prediction. Therefore, their achievable performance is limited. In this study, we propose a generative modeling approach to predict multiple different reaction conditions for a chemical reaction, each of which fully specifies critical reaction elements such that these elements can be directly used as a feasible reaction condition. We formulate the problem of predicting reaction conditions as sampling from a generative distribution. We model the distribution by introducing a variational autoencoder augmented with a graph neural network and learn it from a reaction dataset. For a query reaction, multiple predictions can be obtained by repeated sampling from the distribution. Through experimental investigation on the reaction datasets of four major types of cross-coupling reactions, we demonstrate that the proposed method significantly outperforms existing methods in retrieving ground-truth reaction conditions. Computer science ; Autoencoder ; Generative model ; Graph ; Context (archaeology) ; Generative grammar ; Chemical reaction ; Artificial neural network ; Artificial intelligence ; Machine learning ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: C. Huy Pham | Rebecca Lindsey | Laurence E. Fried | Nir Goldman
Source: The Journal of Physical Chemistry Letters (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches. Computer science ; Transferability ; Quantum ; Stability (learning theory) ; Set (abstract data type) ; Cluster (spacecraft) ; Chebyshev filter ; Range (aeronautics) ; Coupled cluster ; Theoretical computer science ; Computational science ; Machine learning ; Materials science ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Zhi‐Wen Zhao | Yun Geng | Alessandro Troisi | Haibo Ma
Source: Advanced Intelligent Systems (article, 2022-03-19)
Field: Materials Science — Materials Chemistry

Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics The improvements of organic photovoltaics (OPVs) are mainly implemented by the design of novel materials and optimizations of experimental conditions through extensive trial‐and‐error experiments based on chemical intuition, which may be tedious and inefficient for exploring a larger chemical space. In the recent five years, data‐driven methods using machine learning (ML) algorithms and the knowledge of known materials/experimental parameters are introduced to OPV studies to help build a quantitative structure‐property relationship model and accelerate the molecular design and parameter optimization. Here, these recent promising progresses based on experimental OPV datasets are summarized. This review introduces the general workflow (e.g., dataset collection, feature engineering, ML model generation, and evaluation) of ML‐OPV projects and discusses the applications of this framework for predicting OPV performance and experimental optimizations in OPVs. Finally, an outlook of future work directions in this exciting and quickly developing field is presented. Workflow ; Computer science ; Intuition ; Organic solar cell ; Chemical space ; Experimental data ; Machine learning ; Artificial intelligence ; Photovoltaics ; Photovoltaic system ; Engineering ; Drug discovery Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Fuel Cells and Related Materials
Authors: Jun Yin | Jiali Li | Iftekhar A. Karimi | Xiaonan Wang
Source: Chemical Engineering Journal (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Generalized reactor neural ODE for dynamic reaction process modeling with physical interpretability Interpretability ; Ode ; Computer science ; Process (computing) ; Machine learning ; Artificial intelligence ; Control engineering ; Engineering Machine Learning in Materials Science ; Fault Detection and Control Systems ; Fuel Cells and Related Materials