jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zoë Goddard | María J. Marín | David A. Russell | Mark Searcey
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Biomaterials

Active targeting of gold nanoparticles as cancer therapeutics Gold nanoparticles (AuNPs) are of increasing interest for their unique properties and their biocompatability, minimal toxicity, multivalency and size tunability make them exciting drug carriers. The functionalisaton of AuNPs with targeting moieties allows for their selective delivery to cancers, with antibodies, proteins, peptides, aptamers, carbohydrates and small molecules all exploited. Here, we review the recent advances in targeted-AuNPs for the treatment of cancer, with a particular focus on these classes of targeting ligands. We highlight the benefits and potential drawbacks of each ligand class and propose directions in which the field could grow. Colloidal gold ; Nanotechnology ; Nanoparticle ; Chemistry ; Materials science Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; RNA Interference and Gene Delivery
Authors: Patrick Rowe | Volker L. Deringer | Piero Gasparotto | Gábor Csányi | Angelos Michaelides
Source: The Journal of Chemical Physics (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

An accurate and transferable machine learning potential for carbon We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the many-body smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [V. L. Deringer and G. Csányi, Phys. Rev. B 95, 094203 (2017)] with the high numerical accuracy necessary for crystalline graphene [Rowe et al., Phys. Rev. B 97, 054303 (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon. Graphene ; Amorphous solid ; Interatomic potential ; Phonon ; Ab initio ; Density functional theory ; Carbon fibers ; Gaussian ; Crystal structure prediction ; Dispersion (optics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Dongdong Wang | Huihui Wu | Changlai Wang | Long Gu | Hongzhong Chen | Deblin Jana | Lili Feng | Jiawei Liu | Xueying Wang | Pengping Xu | Zhen Guo | Qianwang Chen | Yanli Zhao
Source: Angewandte Chemie International Edition (article, 2020-10-22)
Field: Materials Science — Materials Chemistry

Self‐Assembled Single‐Site Nanozyme for Tumor‐Specific Amplified Cascade Enzymatic Therapy Nanomaterials with enzyme-mimicking activity (nanozymes) show potential for therapeutic interventions. However, it remains a formidable challenge to selectively kill tumor cells through enzymatic reactions, while leaving normal cells unharmed. Herein, we present a new strategy based on a single-site cascade enzymatic reaction for tumor-specific therapy that avoids off-target toxicity to normal tissues. A copper hexacyanoferrate (Cu-HCF) nanozyme with active single-site copper exhibited cascade enzymatic activity within the tumor microenvironment: Tumor-specific glutathione oxidase activity by the Cu-HCF single-site nanozymes (SSNEs) led to the depletion of intracellular glutathione and the conversion of single-site Cu<sup>II</sup> species into Cu<sup>I</sup> for subsequent amplified peroxidase activity through a Fenton-type Harber-Weiss reaction. In this way, abundant highly toxic hydroxyl radicals were generated for tumor cell apoptosis. The results show that SSNEs could amplify the tumor-killing efficacy of reactive oxygen species and suppress tumor growth in vivo. Cascade ; Enzyme ; Chemistry ; Cancer research ; Medicine ; Biochemistry ; Chromatography Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanoparticle-Based Drug Delivery
Authors: Mitchell D. Nothling | Qiang Fu | Amin Reyhani | Stephanie Allison‐Logan | Kenward Jung | Jian Zhu | Masami Kamigaito | Cyrille Boyer | Greg G. Qiao
Source: Advanced Science (review, 2020-08-26)
Field: Chemistry — Organic Chemistry

Progress and Perspectives Beyond Traditional RAFT Polymerization The development of advanced materials based on well-defined polymeric architectures is proving to be a highly prosperous research direction across both industry and academia. Controlled radical polymerization techniques are receiving unprecedented attention, with reversible-deactivation chain growth procedures now routinely leveraged to prepare exquisitely precise polymer products. Reversible addition-fragmentation chain transfer (RAFT) polymerization is a powerful protocol within this domain, where the unique chemistry of thiocarbonylthio (TCT) compounds can be harnessed to control radical chain growth of vinyl polymers. With the intense recent focus on RAFT, new strategies for initiation and external control have emerged that are paving the way for preparing well-defined polymers for demanding applications. In this work, the cutting-edge innovations in RAFT that are opening up this technique to a broader suite of materials researchers are explored. Emerging strategies for activating TCTs are surveyed, which are providing access into traditionally challenging environments for reversible-deactivation radical polymerization. The latest advances and future perspectives in applying RAFT-derived polymers are also shared, with the goal to convey the rich potential of RAFT for an ever-expanding range of high-performance applications. Raft ; Reversible addition−fragmentation chain-transfer polymerization ; Chain transfer ; Polymerization ; Radical polymerization ; Nanotechnology ; Polymer ; Computer science ; Materials science ; Chemistry ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Covalent Organic Framework Applications ; Photopolymerization techniques and applications
Authors: Pavel Karpov | Guillaume Godin | Igor V. Tetko
Source: Journal of Cheminformatics (article, 2020-03-18)
Field: Materials Science — Materials Chemistry

Transformer-CNN: Swiss knife for QSAR modeling and interpretation We present SMILES-embeddings derived from the internal encoder state of a Transformer [1] model trained to canonize SMILES as a Seq2Seq problem. Using a CharNN [2] architecture upon the embeddings results in higher quality interpretable QSAR/QSPR models on diverse benchmark datasets including regression and classification tasks. The proposed Transformer-CNN method uses SMILES augmentation for training and inference, and thus the prognosis is based on an internal consensus. That both the augmentation and transfer learning are based on embeddings allows the method to provide good results for small datasets. We discuss the reasons for such effectiveness and draft future directions for the development of the method. The source code and the embeddings needed to train a QSAR model are available on https://github.com/bigchem/transformer-cnn. The repository also has a standalone program for QSAR prognosis which calculates individual atoms contributions, thus interpreting the model's result. OCHEM [3] environment (https://ochem.eu) hosts the on-line implementation of the method proposed. Quantitative structure–activity relationship ; Computer science ; Transformer ; Inference ; Artificial intelligence ; Benchmark (surveying) ; Source code ; Encoder ; Machine learning ; Code (set theory) ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Rita M. Pinto | Filipa A. Soares | Salette Reis | Cláudia Nunes | Patrick Van Dijck
Source: Frontiers in Microbiology (review, 2020-05-26)
Field: Materials Science — Materials Chemistry

Innovative Strategies Toward the Disassembly of the EPS Matrix in Bacterial Biofilms Bacterial biofilms represent a major concern at a worldwide level due to the high demand for implantable medical devices and the rising numbers of bacterial resistance. The complex structure of the extracellular polymeric substances (EPS) matrix plays a major role in this phenomenon, since it protects bacteria from antibiotics, avoiding drug penetration at bactericidal concentrations. Besides, this structure promotes bacterial cells to adopt a dormant lifestyle, becoming less susceptible to antibacterial agents. Currently, the available treatment for biofilm-related infections consists in the administration of conventional antibiotics at high doses for a long-term period. However, this treatment lacks efficiency against mature biofilms and for implant-associated biofilms it may be necessary to remove the medical device. Thus, biofilm-related infections represent an economical burden for the healthcare systems. New strategies focusing on the matrix are being highlighted as alternative therapies to eradicate biofilms. Here, we outline reported matrix disruptive agents, nanocarriers, and technologies, such as application of magnetic fields, photodynamic therapy, and ultrasounds, that have been under investigation to disrupt the EPS matrix of clinically relevant bacterial biofilms. In an ideal therapy, a synergistic effect between antibiotics and the explored innovated strategies is aimed to completely eradicate biofilms and avoid antimicrobial resistance phenomena. Biofilm ; Matrix (chemical analysis) ; Microbiology ; Chemistry ; Biochemical engineering ; Biology ; Bacteria ; Engineering ; Genetics Corrosion Behavior and Inhibition ; Bacterial biofilms and quorum sensing ; Marine Biology and Environmental Chemistry
Authors: Jiazhen Cai | Xuan Chu | Kun Xu | Hongbo Li | Jing Wei
Source: Nanoscale Advances (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven new material discovery New materials can bring about tremendous progress in technology and applications. However, the commonly used trial-and-error method cannot meet the current need for new materials. Now, a newly proposed idea of using machine learning to explore new materials is becoming popular. In this paper, we review this research paradigm of applying machine learning in material discovery, including data preprocessing, feature engineering, machine learning algorithms and cross-validation procedures. Furthermore, we propose to assist traditional DFT calculations with machine learning for material discovery. Many experiments and literature reports have shown the great effects and prospects of this idea. It is currently showing its potential and advantages in property prediction, material discovery, inverse design, corrosion detection and many other aspects of life. Workflow ; Computer science ; Discovery learning ; Scientific discovery ; Data science ; Artificial intelligence ; Machine learning ; Cognitive science ; Mathematics education ; Database ; Psychology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Tran Doan Huan | Chiho Kim | Lihua Chen | Anand Chandrasekaran | Rohit Batra | Shruti Venkatram | Deepak Kamal | Jordan P. Lightstone | Rishi Gurnani | Pranav Shetty | Manav Ramprasad | Julia Laws | Madeline Shelton | Rampi Ramprasad
Source: Journal of Applied Physics (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

Machine-learning predictions of polymer properties with Polymer Genome Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions. Polymer ; Computer science ; Variety (cybernetics) ; Genome ; Representation (politics) ; Computation ; Range (aeronautics) ; Machine learning ; Artificial intelligence ; Materials science ; Algorithm ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Soumil Y. Joshi | Sanket A. Deshmukh
Source: Molecular Simulation (review, 2020-10-11)
Field: Materials Science — Materials Chemistry

A review of advancements in coarse-grained molecular dynamics simulations Over the last few years, coarse-grained molecular dynamics has emerged as a way to model large and complex systems in an efficient and inexpensive manner due to its lowered resolution, faster dynamics, and larger time steps. However, developing coarse-grained models and subsequently, the accurate interaction potentials (force-field parameters) is a challenging task. Traditional parameterisation techniques, although tedious, have been used extensively to develop CG models for a variety of solvent, soft-matter and biological systems. With the advent of sophisticated optimisation methods, machine learning, and hybrid approaches for force-field parameterisation, models with a higher degree of transferability and accuracy can be developed in a shorter period of time. We review here, some of these traditional and advanced parameterisation techniques while also shedding light on several transferable CG models developed in our group over the years using such an advanced method developed by us. These models, including solvents, polymers and biomolecules have helped us study important solute-solvent interactions and complex polymer architectures, thus paving a way to make experimentally verifiable observations. Computer science ; Transferability ; Force field (fiction) ; Molecular dynamics ; Variety (cybernetics) ; Field (mathematics) ; Biochemical engineering ; Verifiable secret sharing ; Complex system ; Biological system ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Mathematics ; Engineering Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Shuyan Zhang | Chi Lok Wong | Shuwen Zeng | Renzhe Bi | Kolvyn Tai | Kishan Dholakia | Malini Olivo
Source: Nanophotonics (article, 2020-09-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurfaces for biomedical applications: imaging and sensing from a nanophotonics perspective Abstract Metasurface is a recently developed nanophotonics concept to manipulate the properties of light by replacing conventional bulky optical components with ultrathin (more than 10 4 times thinner) flat optical components. Since the first demonstration of metasurfaces in 2011, they have attracted tremendous interest in the consumer optics and electronics industries. Recently, metasurface‐empowered novel bioimaging and biosensing tools have emerged and been reported. Given the recent advances in metasurfaces in biomedical engineering, this review article covers the state of the art for this technology and provides a comprehensive interdisciplinary perspective on this field. The topics that we have covered include metasurfaces for chiral imaging, endoscopic optical coherence tomography, fluorescent imaging, super‐resolution imaging, magnetic resonance imaging, quantitative phase imaging, sensing of antibodies, proteins, DNAs, cells, and cancer biomarkers. Future directions are discussed in twofold: application‐specific biomedical metasurfaces and bioinspired metasurface devices. Perspectives on challenges and opportunities of metasurfaces, biophotonics, and translational biomedical devices are also provided. The objective of this review article is to inform and stimulate interdisciplinary research: firstly, by introducing the metasurface concept to the biomedical community; and secondly by assisting the metasurface community to understand the needs and realize the opportunities in the medical fields. In addition, this article provides two knowledge boxes describing the design process of a metasurface lens and the performance matrix of a biosensor, which serve as a “crash‐course” introduction to those new to both fields. Biophotonics ; Nanophotonics ; Nanotechnology ; Computer science ; Medical imaging ; Materials science ; Photonics ; Optoelectronics ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Random lasers and scattering media
Authors: Sabina Halappanavar | Sybille van den Brûle | Penny Nymark | Laurent Gaté | Carole Seidel | Sarah Valentino | Vadim Zhernovkov | Pernille Høgh Danielsen | Andrea De Vizcaya‐Ruíz | Henrik Wolff | Tobias Stöger | Andrey Boyadziev | Sarah Søs Poulsen | Jorid Birkelund Sørli | Ulla Vogel
Source: Particle and Fibre Toxicology (review, 2020-05-25)
Field: Materials Science — Materials Chemistry

Adverse outcome pathways as a tool for the design of testing strategies to support the safety assessment of emerging advanced materials at the nanoscale Toxicity testing and regulation of advanced materials at the nanoscale, i.e. nanosafety, is challenged by the growing number of nanomaterials and their property variants requiring assessment for potential human health impacts. The existing animal-reliant toxicity testing tools are onerous in terms of time and resources and are less and less in line with the international effort to reduce animal experiments. Thus, there is a need for faster, cheaper, sensitive and effective animal alternatives that are supported by mechanistic evidence. More importantly, there is an urgency for developing alternative testing strategies that help justify the strategic prioritization of testing or targeting the most apparent adverse outcomes, selection of specific endpoints and assays and identifying nanomaterials of high concern. The Adverse Outcome Pathway (AOP) framework is a systematic process that uses the available mechanistic information concerning a toxicological response and describes causal or mechanistic linkages between a molecular initiating event, a series of intermediate key events and the adverse outcome. The AOP framework provides pragmatic insights to promote the development of alternative testing strategies. This review will detail a brief overview of the AOP framework and its application to nanotoxicology, tools for developing AOPs and the role of toxicogenomics, and summarize various AOPs of relevance to inhalation toxicity of nanomaterials that are currently under various stages of development. The review also presents a network of AOPs derived from connecting all AOPs, which shows that several adverse outcomes induced by nanomaterials originate from a molecular initiating event that describes the interaction of nanomaterials with lung cells and involve similar intermediate key events. Finally, using the example of an established AOP for lung fibrosis, the review will discuss various in vitro tests available for assessing lung fibrosis and how the information can be used to support a tiered testing strategy for lung fibrosis. The AOPs and AOP network enable deeper understanding of mechanisms involved in inhalation toxicity of nanomaterials and provide a strategy for the development of alternative test methods for hazard and risk assessment of nanomaterials. Adverse Outcome Pathway ; Outcome (game theory) ; Materials science ; Nanotechnology ; Computer science ; Medicine ; Computational biology Nanoparticles: synthesis and applications ; Integrated Circuits and Semiconductor Failure Analysis ; Nanotechnology research and applications
Authors: Kai Gao | Rick Oerlemans | Matthew R. Groves
Source: Biophysical Reviews (review, 2020-01-31)
Field: Chemistry — Spectroscopy

Theory and applications of differential scanning fluorimetry in early-stage drug discovery Differential scanning fluorimetry (DSF) is an accessible, rapid, and economical biophysical technique that has seen many applications over the years, ranging from protein folding state detection to the identification of ligands that bind to the target protein. In this review, we discuss the theory, applications, and limitations of DSF, including the latest applications of DSF by ourselves and other researchers. We show that DSF is a powerful high-throughput tool in early drug discovery efforts. We place DSF in the context of other biophysical methods frequently used in drug discovery and highlight their benefits and downsides. We illustrate the uses of DSF in protein buffer optimization for stability, refolding, and crystallization purposes and provide several examples of each. We also show the use of DSF in a more downstream application, where it is used as an in vivo validation tool of ligand-target interaction in cell assays. Although DSF is a potent tool in buffer optimization and large chemical library screens when it comes to ligand-binding validation and optimization, orthogonal techniques are recommended as DSF is prone to false positives and negatives. Drug discovery ; Context (archaeology) ; Computational biology ; False positive paradox ; Computer science ; Folding (DSP implementation) ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Biology ; Engineering ; Artificial intelligence ; Biochemistry ; Materials science Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Enzyme Structure and Function