jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ranjan Jana | Hasina Mamataj Begam | Enakshi Dinda
Source: Chemical Communications (review, 2021-01-01)
Field: Chemistry — Organic Chemistry

The emergence of the C–H functionalization strategy in medicinal chemistry and drug discovery Owing to the market competitiveness and urgent societal need, an optimum speed of drug discovery is an important criterion for successful implementation. Despite the rapid ascent of artificial intelligence and computational and bioanalytical techniques to accelerate drug discovery in big pharma, organic synthesis of privileged scaffolds predicted <i>in silico</i> for <i>in vitro</i> and <i>in vivo</i> studies is still considered as the rate-limiting step. C-H activation is the latest technology added into an organic chemist's toolbox for the rapid construction and late-stage modification of functional molecules to achieve the desired chemical and physical properties. Particularly, elimination of prefunctionalization steps, exceptional functional group tolerance, complexity-to-diversity oriented synthesis, and late-stage functionalization of privileged medicinal scaffolds expand the chemical space. It has immense potential for the rapid synthesis of a library of molecules, structural modification to achieve the required pharmacological properties such as absorption, distribution, metabolism, excretion, toxicology (ADMET) and attachment of chemical reporters for proteome profiling, metabolite synthesis, <i>etc.</i> for preclinical studies. Although heterocycle synthesis, late-stage drug modification, <sup>18</sup>F labelling, methylation, <i>etc. via</i> C-H functionalization have been reviewed from the synthetic standpoint, a general overview of these protocols from medicinal and drug discovery aspects has not been reviewed. In this feature article, we will discuss the recent trends of C-H activation methodologies such as synthesis of medicinal scaffolds through C-H activation/annulation cascade; C-H arylation for sp<sup>2</sup>-sp<sup>2</sup> and sp<sup>2</sup>-sp<sup>3</sup> cross-coupling; C-H borylation/silylation to introduce a functional linchpin for further manipulation; C-H amination for N-heterocycles and hydrogen bond acceptors; C-H fluorination/fluoroalkylation to tune polarity and lipophilicity; C-H methylation: methyl magic in drug discovery; peptide modification and macrocyclization for therapeutics and biologics; fluorescent labelling and radiolabelling for bioimaging; bioconjugation for chemical biology studies; drug-metabolite synthesis for biodistribution and excretion studies; late-stage diversification of drug-molecules to increase efficacy and safety; cutting-edge DNA encoded library synthesis and improved synthesis of drug molecules <i>via</i> C-H activation in medicinal chemistry and drug discovery. Chemical space ; Drug discovery ; Chemistry ; ADME ; Surface modification ; Combinatorial chemistry ; Nanotechnology ; Computational biology ; Computer science ; Biology ; In vitro ; Materials science ; Biochemistry Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Synthesis and Catalytic Reactions
Authors: Huachen Tao | Tianyi Wu | Sina Kheiri | Matteo Aldeghi | Alán Aspuru‐Guzik | Eugenia Kumacheva
Source: Advanced Functional Materials (article, 2021-09-15)
Field: Materials Science — Materials Chemistry

Self‐Driving Platform for Metal Nanoparticle Synthesis: Combining Microfluidics and Machine Learning Abstract Many applications of inorganic nanoparticles (NPs), including photocatalysis, photovoltaics, chemical and biochemical sensing, and theranostics, are governed by NP optical properties. Exploration and identification of reaction conditions for the synthesis of NPs with targeted spectroscopic characteristics is a time‐, labor‐, and resource‐intensive task, as it involves the optimization of multiple interdependent reaction conditions. Integration of machine learning (ML) and microfluidics (MF) offers accelerated identification and optimization of reaction conditions for NP synthesis. Here, an autonomous ML‐driven, oscillatory MF platform for the synthesis of NPs is reported. The platform utilized multiple recipes and reaction times for the synthesis of NPs with different dimensions, conducted spectroscopic NP characterization, and employed ML approaches to analyze multiple yet prioritized spectroscopic NP characteristics, and identified reaction conditions for the synthesis of NPs with targeted optical properties. The platform is also used to develop an understanding of the relationship between reaction conditions and NP properties. This study shows the strong potential of ML‐driven oscillatory MF platforms in materials science and paves the way for automated NP development. Microfluidics ; Materials science ; Nanotechnology ; Nanoparticle ; Characterization (materials science) ; Reaction conditions ; Photocatalysis ; Computer science ; Catalysis ; Chemistry Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Weikang Luo | Liang-Lin Zhang | Zhaoyu Yang | Xiaohang Guo | Yao Wu | Wei Zhang | Jiekun Luo | Tao Tang | Yang Wang
Source: Journal of Nanobiotechnology (review, 2021-10-13)
Field: Materials Science — Materials Chemistry

Herbal medicine derived carbon dots: synthesis and applications in therapeutics, bioimaging and sensing Since the number of raw material selections for the synthesis of carbon dots (CDs) has grown extensively, herbal medicine as a precursor receives an increasing amount of attention. Compared with other biomass precursors, CDs derived from herbal medicine (HM-CDs) have become the most recent incomer in the family of CDs. In recent ten years, a great many studies have revealed that HM-CDs tend to be good at theranostics without drug loading. However, the relevant development and research results are not systematically reviewed. Herein, the origin and history of HM-CDs are outlined, especially their functional performances in medical diagnosis and treatment. Besides, we sort out the herbal medicine precursors, and analyze the primary synthetic methods and the key characteristics. In terms of the applications of HM-CDs, medical therapeutics, ion and molecular detection, bioimaging, as well as pH sensing are summarized. Finally, we discuss the crucial challenges and future prospects. Nanotechnology ; Materials science ; Chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Nagaraj Basavegowda | Kwang-Hyun Baek
Source: Molecules (review, 2021-02-09)
Field: Materials Science — Materials Chemistry

Multimetallic Nanoparticles as Alternative Antimicrobial Agents: Challenges and Perspectives Recently, infectious diseases caused by bacterial pathogens have become a major cause of morbidity and mortality globally due to their resistance to multiple antibiotics. This has triggered initiatives to develop novel, alternative antimicrobial materials, which solve the issue of infection with multidrug-resistant bacteria. Nanotechnology using nanoscale materials, especially multimetallic nanoparticles (NPs), has attracted interest because of the favorable physicochemical properties of these materials, including antibacterial properties and excellent biocompatibility. Multimetallic NPs, particularly those formed by more than two metals, exhibit rich electronic, optical, and magnetic properties. Multimetallic NP properties, including size and shape, zeta potential, and large surface area, facilitate their efficient interaction with bacterial cell membranes, thereby inducing disruption, reactive oxygen species production, protein dysfunction, DNA damage, and killing potentiated by the host's immune system. In this review, we summarize research progress on the synergistic effect of multimetallic NPs as alternative antimicrobial agents for treating severe bacterial infections. We highlight recent promising innovations of multimetallic NPs that help overcome antimicrobial resistance. These include insights into their properties, mode of action, the development of synthetic methods, and combinatorial therapies using bi- and trimetallic NPs with other existing antimicrobial agents. Antimicrobial ; Nanotechnology ; Nanoparticle ; Chemistry ; Materials science ; Organic chemistry Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Simon Müller | Christina Sauter | Ramesh Shunmugasundaram | Nils Wenzler | Vincent De Andrade | Francesco De Carlo | Ender Konukoğlu | Vanessa Wood
Source: Nature Communications (article, 2021-10-27)
Field: Materials Science — Surfaces, Coatings and Films

Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation. Segmentation ; Electrode ; Battery (electricity) ; Deep learning ; Computer science ; Materials science ; Artificial intelligence ; Lithium-ion battery ; Lithium (medication) ; Graphite ; Nanotechnology ; Composite material ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Mounika Gayathri Tirumala | Pratibha Anchi | Susmitha Raja | Mahesh Rachamalla | Chandraiah Godugu
Source: Frontiers in Pharmacology (review, 2021-09-09)
Field: Materials Science — Materials Chemistry

Novel Methods and Approaches for Safety Evaluation of Nanoparticle Formulations: A Focus Towards In Vitro Models and Adverse Outcome Pathways Nanotoxicology is an emerging field employed in the assessment of unintentional hazardous effects produced by nanoparticles (NPs) impacting human health and the environment. The nanotoxicity affects the range between induction of cellular stress and cytotoxicity. The reasons so far reported for these toxicological effects are due to their variable sizes with high surface areas, shape, charge, and physicochemical properties, which upon interaction with the biological components may influence their functioning and result in adverse outcomes (AO). Thus, understanding the risk produced by these materials now is an important safety concern for the development of nanotechnology and nanomedicine. Since the time nanotoxicology has evolved, the methods employed have been majorly relied on <i>in vitro</i> cell-based evaluations, while these simple methods may not predict the complexity involved in preclinical and clinical conditions concerning pharmacokinetics, organ toxicity, and toxicities evidenced through multiple cellular levels. The safety profiles of nanoscale nanomaterials and nanoformulations in the delivery of drugs and therapeutic applications are of considerable concern. In addition, the safety assessment for new nanomedicine formulas lacks regulatory standards. Though the <i>in vivo</i> studies are greatly needed, the end parameters used for risk assessment are not predicting the possible toxic effects produced by various nanoformulations. On the other side, due to increased restrictions on animal usage and demand for the need for high-throughput assays, there is a need for developing and exploring novel methods to evaluate NPs safety concerns. The progress made in molecular biology and the availability of several modern techniques may offer novel and innovative methods to evaluate the toxicological behavior of different NPs by using single cells, cell population, and whole organisms. This review highlights the recent novel methods developed for the evaluation of the safety impacts of NPs and attempts to solve the problems that come with risk assessment. The relevance of investigating adverse outcome pathways (AOPs) in nanotoxicology has been stressed in particular. Nanotoxicology ; Nanomedicine ; Risk analysis (engineering) ; Nanotechnology ; Biochemical engineering ; Computer science ; Medicine ; Nanoparticle ; Materials science ; Engineering Nanoparticles: synthesis and applications ; 3D Printing in Biomedical Research
Authors: Miguel Albaladejo‐Siguan | Elizabeth C. Baird | David Becker‐Koch | Yanxiu Li | Andrey L. Rogach | Yana Vaynzof
Source: Advanced Energy Materials (article, 2021-02-12)
Field: Materials Science — Materials Chemistry

Stability of Quantum Dot Solar Cells: A Matter of (Life)Time Abstract Colloidal quantum dot solar cells (QDSCs) are promising candidates amongst third generation photovoltaics due to their bandgap tunability, facile low‐temperature ink processing, strong visible‐to‐infrared absorption, and potential for multiple‐exciton generation. An unprecedented increase in power conversion efficiency is reported for different types of QDSCs in the last few years, making them appealing for large‐scale fabrication. The stability of QDSCs, however, still remains inadequate for industrial application, especially when they are operated under a sun‐like illumination in an ambient atmosphere. This review focuses on three classes of QDs (lead chalcogenides, lead halide perovskites, and lead‐free QDs) and considers the current understanding of their degradation mechanisms. For each material class, strategies for stability improvement are discussed, both from materials science and device engineering perspectives. This paper concludes by suggesting a methodology for characterizing the QDSCs’ stability which would standardize the results obtained by researchers worldwide. Materials science ; Quantum dot ; Photovoltaics ; Fabrication ; Nanotechnology ; Lead (geology) ; Optoelectronics ; Degradation (telecommunications) ; Energy conversion efficiency ; Halide ; Absorption (acoustics) ; Photovoltaic system ; Engineering physics ; Computer science ; Telecommunications ; Electrical engineering ; Physics Quantum Dots Synthesis And Properties ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Yuying Jia | Xuan Hou | Zhongwei Wang | Xiangang Hu
Source: ACS Sustainable Chemistry & Engineering (article, 2021-04-27)
Field: Materials Science — Materials Chemistry

Machine Learning Boosts the Design and Discovery of Nanomaterials Nanomaterials (NMs) have developed quickly and cover various fields, but research on nanotechnology and NMs largely relies on costly experiments or complex calculations (e.g., density functional theory). In contrast, machine learning (ML) methods can address the large amount of time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of NMs. ML is a powerful tool for NM research; however, large knowledge gaps and critical issues should be promptly addressed to promote NMs from the laboratory to industry. With a focus on the primary NM aspects, enhancements to the design of NM structures, properties, adsorption, and catalysis by ML are reviewed and discussed. Given the emergent challenges in nanobiology, ML predictions of interactions between NMs and biology are also analyzed. Subsequently, this perspective discusses how to improve the interpretability of ML algorithms, which has been a bottleneck of ML in recent years. ML has led to innovations in the development of NMs, but some problems remain, such as imperfect databases and the accuracy of algorithm determination and nanopattern image recognition, which are herein addressed. Overall, this perspective provides insights for the development of ML in NM research. Bottleneck ; Interpretability ; Computer science ; Nanotechnology ; Throughput ; Boosting (machine learning) ; Nanomaterials ; Machine learning ; Artificial intelligence ; Data science ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Jingsong Peng | Antoni P. Tomsia | Lei Jiang | Ben Zhong Tang | Qunfeng Cheng
Source: Nature Communications (article, 2021-07-27)
Field: Materials Science — Biomaterials

Stiff and tough PDMS-MMT layered nanocomposites visualized by AIE luminogens Abstract Polydimethylsiloxane (PDMS) is a widely used soft material that exhibits excellent stability and transparency. But the difficulty of fine-tuning its Young’s modulus and its low toughness significantly hinder its application in fields such as tissue engineering and flexible devices. Inspired by nacre, here we report on the development of PDMS-montmorillonite layered (PDMS-MMT-L) nanocomposites via the ice-templating technique, resulting in 23 and 12 times improvement in Young’s modulus and toughness as compared with pure PDMS. Confocal fluorescence microscopy assisted by aggregation-induced emission (AIE) luminogens reveals three-dimensional reconstruction and in situ crack tracing of the nacre-inspired PDMS-MMT-L nanocomposite. The PDMS-MMT-L nanocomposite is toughened with mechanisms such as crack deflection and bridging. The AIE-assisted visualization of the crack propagation for nacre-inspired layered nanocomposites provides an advanced and universal characterization technique for organic-inorganic nanocomposites. Nanocomposite ; Materials science ; Polydimethylsiloxane ; Toughness ; Montmorillonite ; Composite material ; Modulus ; Characterization (materials science) ; Nanotechnology Calcium Carbonate Crystallization and Inhibition ; Cephalopods and Marine Biology ; Luminescence and Fluorescent Materials
Authors: Resat Aksakal | Chiel Mertens | Matthieu Soete | Nezha Badi | Filip Du Prez
Source: Advanced Science (review, 2021-01-25)
Field: Materials Science — Biomaterials

Applications of Discrete Synthetic Macromolecules in Life and Materials Science: Recent and Future Trends In the last decade, the field of sequence-defined polymers and related ultraprecise, monodisperse synthetic macromolecules has grown exponentially. In the early stage, mainly articles or reviews dedicated to the development of synthetic routes toward their preparation have been published. Nowadays, those synthetic methodologies, combined with the elucidation of the structure-property relationships, allow envisioning many promising applications. Consequently, in the past 3 years, application-oriented papers based on discrete synthetic macromolecules emerged. Hence, material science applications such as macromolecular data storage and encryption, self-assembly of discrete structures and foldamers have been the object of many fascinating studies. Moreover, in the area of life sciences, such structures have also been the focus of numerous research studies. Here, it is aimed to highlight these recent applications and to give the reader a critical overview of the future trends in this area of research. Nanotechnology ; Macromolecule ; Biochemical engineering ; Data science ; Polymer science ; Materials science ; Computer science ; Chemistry ; Engineering Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Advanced biosensing and bioanalysis techniques
Authors: Fabio Juliá | Qianzhen Shao | Meng Duan | Matthew B. Plutschack | Florian Berger | Javier Mateos | Chenxi Lu | Xiao‐Song Xue | K. N. Houk | Tobias Ritter
Source: Journal of the American Chemical Society (article, 2021-09-21)
Field: Chemistry — Organic Chemistry

High Site Selectivity in Electrophilic Aromatic Substitutions: Mechanism of C–H Thianthrenation The introduction of thianthrene as a linchpin has proven to be a versatile strategy for the C-H functionalization of aromatic compounds, featuring a broad scope and fast diversification. The synthesis of aryl thianthrenium salts has displayed an unusually high para regioselectivity, notably superior to those observed in halogenation or borylation reactions for various substrates. We report an experimental and computational study on the mechanism of aromatic C-H thianthrenation reactions, with an emphasis on the elucidation of the reactive species and the nature of the exquisite site selectivity. Mechanisms involving a direct attack of arene to the isolated O-trifluoracetylthianthrene S-oxide (TT + -TFA) or to the thianthrene dication (TT 2+ ) via electron transfer under acidic conditions are identified. A reversible interconversion of the different Wheland-type intermediates before a subsequent, irreversible deprotonation is proposed to be responsible for the exceptional para selectivity of the reaction. Chemistry ; Electrophile ; Selectivity ; Mechanism (biology) ; Stereochemistry ; Electrophilic aromatic substitution ; Electrophilic addition ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry ; Catalysis Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques ; Synthesis and Catalytic Reactions
Authors: Fynn M. Hansen | Maria C. Tanzer | Franziska Brüning | Isabell Bludau | Che A. Stafford | Brenda A. Schulman | María S. Robles | Özge Karayel | Matthias Mann
Source: Nature Communications (article, 2021-01-11)
Field: Chemistry — Spectroscopy

Data-independent acquisition method for ubiquitinome analysis reveals regulation of circadian biology Protein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. Here, we develop a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identifies 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells - double the number and quantitative accuracy of data dependent acquisition. Applied to TNF signaling, the workflow comprehensively captures known sites while adding many novel ones. An in-depth, systems-wide investigation of ubiquitination across the circadian cycle uncovers hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting new connections between metabolism and circadian regulation. Ubiquitin ; Computational biology ; Circadian clock ; Quantitative proteomics ; Orbitrap ; Workflow ; Cell biology ; Proteasome ; Biology ; Proteomics ; Circadian rhythm ; Chemistry ; Computer science ; Biochemistry ; Mass spectrometry ; Neuroscience ; Gene Advanced Proteomics Techniques and Applications ; Ubiquitin and proteasome pathways ; Mass Spectrometry Techniques and Applications