jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: FNU Srinidhi | Deepak Patel | Vasantha Kumara S A
Source: ChemRxiv (preprint, 2020-03-12)
Field: Chemistry — Spectroscopy

Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed. Solubility ; Supercritical carbon dioxide ; Mean squared error ; Supercritical fluid ; Supercritical fluid extraction ; Approximation error ; Artificial neural network ; Backpropagation ; Solvent ; Chemistry ; Carbon dioxide ; Mathematics ; Thermodynamics ; Materials science ; Computer science ; Algorithm ; Organic chemistry ; Artificial intelligence ; Statistics ; Physics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Xiaorui Wang | Jiezhong Qiu | Yuquan Li | Guangyong Chen | Huanxiang Liu | Ben Liao | Chang‐Yu Hsieh | Xiaojun Yao
Source: ChemRxiv (preprint, 2020-11-26)
Field: Materials Science — Materials Chemistry

RetroPrime: A Chemistry-Inspired and Transformer-based Method for Retrosynthesis Predictions Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. These two steps are accomplished with versatile Transformer models, respectively. While RetroPrime performs competitively against all state-of-the art models on the standard USPTO-50K dataset, it manifests remarkable generalizability and outperforms the only published result by a non-trivial margin of 4.8% for the Top-1 accuracy on the large-scale USPTO-full dataset. It is known that outputs of Transformer-based retrosynthesis model tend to suffer from insufficient diversity and high invalidity. These problems may limit the potential of Transformer-based methods in real practice, yet no prior works address both issues simultaneously. RetroPrime is designed to tackle these challenges. Finally, we provide convincing results to support the claim that RetromPrime can more effectively generalize across chemical space. Retrosynthetic analysis ; Transformer ; Computer science ; Generalizability theory ; Artificial intelligence ; Machine learning ; Engineering ; Chemistry ; Mathematics ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Elzbieta Rembeza | Martin K. M. Engqvist
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-12-19)
Field: Chemistry — Spectroscopy

Experimental investigation of enzyme functional annotations reveals extensive annotation error Only a small fraction of genes deposited to databases has been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy of existing annotations are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation. Annotation ; Class (philosophy) ; Similarity (geometry) ; Computer science ; Function (biology) ; Enzyme ; Computational biology ; Protein function ; Relevance (law) ; Information retrieval ; Biology ; Gene ; Artificial intelligence ; Genetics ; Biochemistry Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Enzyme Structure and Function
Authors: Alexander Ryabov | Iskander Akhatov | Petr Zhilyaev
Source: Scientific Reports (preprint, 2020-05-14)
Field: Chemistry — Spectroscopy

Neural network interpolation of exchange-correlation functional Density functional theory (DFT) is one of the most widely used tools to solve the many-body Schrodinger equation. The core uncertainty inside DFT theory is the exchange-correlation (XC) functional, the exact form of which is still unknown. Therefore, the essential part of DFT success is based on the progress in the development of XC approximations. Traditionally, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo numerical calculations. However, there is no consistent and general scheme of XC interpolation and functional representation. Many different developed parametrizations mainly utilize a number of phenomenological rules to construct a specific XC functional. In contrast, the neural network (NN) approach can provide a general way to parametrize an XC functional without any a priori knowledge of its functional form. In this work, we develop NN XC functionals and prove their applicability to 3-dimensional physical systems. We show that both the local density approximation (LDA) and generalized gradient approximation (GGA) are well reproduced by the NN approach. It is demonstrated that the local environment can be easily considered by changing only the number of neurons in the first layer of the NN. The developed NN XC functionals show good results when applied to systems that are not presented in the training/test data. The generalizability of the formulated NN XC framework leads us to believe that it could give superior results in comparison with traditional XC schemes provided training data from high-level theories such as the quantum Monte Carlo and post-Hartree-Fock methods. Interpolation (computer graphics) ; Extrapolation ; A priori and a posteriori ; Applied mathematics ; Monte Carlo method ; Artificial neural network ; Statistical physics ; Density functional theory ; Generalizability theory ; Hybrid functional ; Time-dependent density functional theory ; Mathematics ; Computer science ; Physics ; Artificial intelligence ; Quantum mechanics ; Mathematical analysis Advanced NMR Techniques and Applications ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Sergi Vela | Alan Scheidegger | Raimón Fabregat | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2020-08-26)
Field: Materials Science — Materials Chemistry

Tuning the Thermal Stability and Photoisomerization of Azoheteroarenes through Macrocycle Strain Azobenzene and its derivatives are one of the most-widespread molecular scaffolds in a range of modern applications, as well as in fundamental research. After photoexcitation, azo-based photoswitches revert back to the most stable isomer in a timescale (t1/2) that determines the range of potential applications. Attempts to bring t1/2 to extreme values prompted to the development of azobenzene and azoheteroarene derivatives that either rebalance the E- and Z- isomer stabilities, or exploit unconventional thermal isomerization mechanisms. In the former case, one successful strategy has been the creation of macrocycle strain, which tends to impact the E/Z stability asymmetrically, and thus significantly modifies t1/2. On the bright side, bridged derivatives have shown an improved optical switching owing to the higher quantum yields and absence of degradation. However, in most (if not all) cases, bridged derivatives display a reversed thermal stability (more stable Z-isomer), and smaller t1/2 than the acyclic counterparts, which restricts their potential interest to applications requiring a fast forward and backwards switch. In this paper, we investigate the impact of alkyl bridges to the thermal stability of phenyl-azoheteroarenes using computational methods, and we reveal that is indeed possible to combine such improved photo-switching characteristics while preserving the regular thermal stability (more stable E-isomer), and increased t1/2 values under the appropriate connectivity and bridge length. Photoisomerization ; Azobenzene ; Thermal stability ; Isomerization ; Photoexcitation ; Stability (learning theory) ; Thermal ; Alkyl ; Chemistry ; Materials science ; Strain (injury) ; Molecular switch ; Chemical physics ; Molecule ; Physics ; Excitation ; Computer science ; Thermodynamics ; Organic chemistry ; Quantum mechanics ; Catalysis Photochromic and Fluorescence Chemistry ; Molecular Junctions and Nanostructures ; Organic Electronics and Photovoltaics
Authors: Matthew Waas | Jack Littrell | Rebekah L. Gundry
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-01-23)
Field: Chemistry — Spectroscopy

CIRFESS: An interactive resource for querying the set of theoretically detectable peptides for cell surface and extracellular enrichment proteomic studies Abstract Cell surface transmembrane, extracellular, and secreted proteins are high value targets for immunophenotyping, drug development, and studies related to intercellular communication in health and disease. As the number of specific and validated affinity reagents that target this subproteome are limited, mass spectrometry (MS)-based approaches will continue to play a critical role in enabling discovery and quantitation of these molecules. Given the technical considerations that make MS-based cell surface proteome studies uniquely challenging, it can be difficult to select an appropriate experimental approach. To this end, we have integrated multiple prediction strategies and annotations into a single online resource, C ompiled I nteractive R esource f or E xtracellular and S urface S tudies (CIRFESS). CIRFESS enables rapid interrogation of the human proteome to reveal the cell surface proteome theoretically detectable by current approaches and highlights where current prediction strategies provide concordant and discordant information. We applied CIRFESS to identify the percentage of various subsets of the proteome which are expected to be captured by targeted enrichment strategies, including two established methods and one that is possible but not yet demonstrated. These results will inform the selection of available proteomic strategies and development of new strategies to enhance coverage of the cell surface and extracellular proteome. CIRFESS is available at www.cellsurfer.net/cirfess . Proteome ; Computational biology ; Human proteome project ; Extracellular ; Biology ; Proteomics ; Cell ; Set (abstract data type) ; Computer science ; Resource (disambiguation) ; Bioinformatics ; Cell biology ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; RNA and protein synthesis mechanisms
Authors: Javad Shabanpour | Sina Beyraghi | A. Cheldavi
Source: Scientific Reports (preprint, 2020-06-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrafast reprogrammable multifunctional vanadium-dioxide-assisted metasurface for dynamic THz wavefront engineering In this paper, for the first time, a new generation of ultrafast reprogrammable multi-mission bias encoded metasurface is proposed for dynamic terahertz wavefront engineering by employing VO2 reversible and fast monoclinic to tetragonal phase transition. The multi-functionality of our designed VO2 based coding metasurface (VBCM) was guaranteed by elaborately designed meta-atom comprising three-patterned VO2 thin films whose operational statuses can be dynamically tuned among four states of "00"-"11" by merely changing the biasing voltage controlled by an external Field-programmable gate array platform. Capitalizing on such meta-atom design and by driving VBCM with different spiral-like and spiral-parabola-like coding sequences, single vortex beam and focused vortex beam with interchangeable orbital angular momentum modes were satisfactorily generated respectively. Additionally, by adopting superposition theorem and convolution operation, symmetric/asymmetric multiple beams and arbitrarily-oriented multiple vortex beams in pre-demined directions with different topological charges are realized. Several illustrative examples successfully have clarified that the proposed VBCM is a promising candidate for solving crucial terahertz challenges such as high data rate wireless communication where ultrafast switching between several missions is required. Reconfigurability ; Ultrashort pulse ; Wavefront ; Biasing ; Superposition principle ; Optics ; Materials science ; Optoelectronics ; Physics ; Computer science ; Voltage ; Laser ; Telecommunications Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Lev Sarkisov | Rocío Bueno-Pérez | Mythili Sutharson | David Fairen‐Jiménez
Source: ChemRxiv (preprint, 2020-09-07)
Field: Materials Science — Materials Chemistry

Material Informatics with PoreBlazer v4.0 and CSD MOF Database In this article, we present an updated version of the PoreBlazer code, an open access, open source Fortran 90 programme to calculate structural properties of porous materials. The article describes the properties calculated by the code, their physical meaning and their relationship to the properties that can be measured experimentally. We reflect on the progress of the code over the years and discuss features of the most recent version. The results of these calculations, along with the PoreBlazer code, documentation, and case studies are available online from https://github.com/SarkisovGroup/PoreBlazer. Documentation ; Code (set theory) ; Fortran ; Computer science ; Source code ; Programming language ; Meaning (existential) ; Informatics ; Database ; Open source ; Software ; Engineering X-ray Diffraction in Crystallography ; Magnesium Oxide Properties and Applications ; Machine Learning in Materials Science
Authors: Camilo Eduardo Hernández-Cuellar | Esteban Martinez | Juan Carlos Gallego-Gómez
Source: Revista Colombiana de Ciencias Químico Farmacéuticas (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Descubrimiento de fármacos basado en imagenología de células vivas El diseño eficiente de compuestos aprovechando las características estructurales de las moléculas y la búsqueda eficiente de dianas terapéuticas, ha proporcionado herramientas efectivas en la investigación de nuevos tratamientos cuando esta se enfoca en mecanismos celulares de la enfermedad. Los cambios fenotípicos producidos por la interacción in vitro entre molécula-diana, pueden controlarse cuantitativamente mediante imagenología de células vivas. Para garantizar una interacción adecuada, es necesario considerar diferentes elementos cruciales: 1. Las características estructurales y la dinámica molecular del compuesto a evaluar. 2. La relevancia del blanco para la fisiopatología de interés. Sin embargo, el desconocimiento del panorama general en el descubrimiento de fármacos, desde problemáticas estructurales y celulares, ha enlentecido la búsqueda de nuevos tratamientos. Esta revisión descriptiva de tema presenta algunos aspectos estructurales importantes para la caracterización de compuestos como candidatos terapéuticos, y aproximaciones experimentales para desarrollo de sistemas celulares. Los tópicos discutidos se enfocan en la monitorización por imagenología de células vivas y así mismo proporcionamos ejemplos relevantes. La monitorización de efectos fenotípicos producidos por interacciones entre candidato químico y blanco terapéutico en un sistema celular puede favorecer la búsqueda eficiente de moléculas potencialmente terapéuticas. Humanities ; Physics ; Molecular biology ; Philosophy ; Chemistry ; Biology Click Chemistry and Applications ; Computational Drug Discovery Methods ; Monoclonal and Polyclonal Antibodies Research
Authors: Zheng Wang | Xiaoming Shi | Xusheng Yang | Zhuhong Liu | San‐Qiang Shi | Xingqiao Ma
Source: Applied Sciences (article, 2020-12-15)
Field: Materials Science — Metals and Alloys

The Effects of Hydrogen Distribution on the Elastic Properties and Hydrogen-Induced Hardening and Softening of α-Fe In this work, we conducted a high-throughput atomistic simulation of the interstitial solid solutions of hydrogen in α-Fe. The elastic constants and moduli were calculated. Through statistical analysis of structures and results, the influences of the microscopic distribution of hydrogen on the elastic moduli, as well as hydrogen-induced hardening and softening, are discussed. We found that even though the uniformly distributed hydrogen caused slight softening in α-Fe, the distribution of hydrogen at different adjacent positions significantly affected the elastic moduli. For example, hydrogen increased the Young’s modulus and shear modulus at the 5th and 10th nearest neighbors, resulting in hardening, but decreased the bulk modulus at the 7th nearest neighbor, making the material easier to compress. These phenomena are related to the distribution densities of the positions that hydrogen atoms can occupy on the two major slip families, {110} and {112}, at different nearest neighbors distinguished by distances. Softening ; Moduli ; Hydrogen ; Materials science ; Shear modulus ; Elastic modulus ; Hardening (computing) ; Modulus ; k-nearest neighbors algorithm ; Thermodynamics ; Composite material ; Crystallography ; Chemistry ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Matthew R. Henry | Andrei G. Fedorov
Source: Computational Materials Science (article, 2020-08-13)
Field: Materials Science — Surfaces, Coatings and Films

Adaptive simulations enable computational design of electron beam processing of nanomaterials with supersonic micro-jet precursor Supersonic speed ; Jet (fluid) ; Materials science ; Nanotechnology ; Context (archaeology) ; Knudsen number ; Nanomaterials ; Mechanics ; Physics ; Computational physics Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices ; Plasma Diagnostics and Applications
Authors: Shani L. Levit
Source: VCU Scholars Compass (Virginia Commonwealth University) (article, 2020-01-01)
Field: Materials Science — Biomaterials

Formulation and Validation of Nanoparticle Controlled Delivery for Chemotherapeutic Drug Products Taxol, a formulation of paclitaxel (PTX), is one of the most widely used anticancer drugs, particularly for treating recurring ovarian carcinomas following surgery. Clinically, PTX is used in combination with other drugs such as lapatinib (LAP) to increase treatment efficacy. Delivering drug combinations with nanoparticles has the potential to improve chemotherapy outcomes. In this study, we use Flash NanoPrecipitation, a rapid, scalable process to encapsulate weakly hydrophobic drugs (logP in vitro. Encapsulating either PTX or LAP into nanoparticles increases drug potency. When PTX and LAP are co-loaded in the same nanoparticle, they have a synergistic effect that is greater than treating with two single-drug loaded nanoparticles. Furthermore, we examined in vitro sequential delivery of PTX and LAP encapsulated into polymer nanoparticles on ovarian cancer cells. We observed a sequence-dependent cytotoxic effect. These results are promising for establishing sequential drug delivery with nanoparticles as a method for treating ovarian cancer. Building on this work, we demonstrated the potential of encapsulating a hydrophobic PTX prodrug into pH-responsive nanoparticles as a method to enhance drug efficacy and control drug release. Overall, these findings provide the foundation for developing co-encapsulated nanoparticles with controlled drug release as well as methods for improving the drug efficacy of PTX. Drug delivery ; Nanoparticle ; Drug ; Computer science ; Pharmacology ; Medicine ; Risk analysis (engineering) ; Nanotechnology ; Medical physics ; Materials science Nanoparticle-Based Drug Delivery ; Drug Solubulity and Delivery Systems ; Advanced Drug Delivery Systems
Authors: Manuel Carrer | Tatjana Škrbić | Sigbjørn Løland Bore | Giuseppe Milano | Michele Cascella | Achille Giacometti
Source: ChemRxiv (preprint, 2020-06-02)
Field: Chemistry — Organic Chemistry

Can Polarity-Inverted Surfactants Self-Assemble in Nonpolar Solvents? We investigate the self-assembly process of a surfactant with inverted polarity in water and cyclohexane using both all-atom and coarse grained hybrid particle-field moleculardynamics simulations. Unlike conventional surfactants, the molecule under study proposed in a recent experiment (M. Facchin et al., RSC Adv. 2017, 7, 15337–15341) isformed by a rigid and compact hydrophobic adamantane moiety, and a long and floppy triethylene glycol tail. In water, we report the formation of stable inverted micelles with the adamantane heads grouping together into a hydrophobic core, and the tails forming hydrogen bonds with water. By contrast, multi-microsecond simulations do not provide evidence of stable micelle formation in cyclohexane. Validating the computational results by comparison with experimental diffusion constant and small-angle neutron scattering intensity, we show that at laboratory thermodynamic conditions the mixture resides in the supercritical region of the phase diagram, where aggregated and free surfactant states co-exist in solution. Our simulations also provide indications about how to escape this region, to produce thermodynamically stable micellar forms. Adamantane ; Micelle ; Molecular dynamics ; Pulmonary surfactant ; Cyclohexane ; Chemical physics ; Chemistry ; Hydrogen bond ; Supercritical fluid ; Hydrophobe ; Molecule ; Small-angle neutron scattering ; Neutron scattering ; Materials science ; Chemical engineering ; Crystallography ; Physical chemistry ; Organic chemistry ; Computational chemistry ; Scattering ; Aqueous solution Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Material Dynamics and Properties