Reaction Graphs Research Articles

Implementation of a soft grading system for chemistry in a Moodle plugin: reaction handling

Here, we present a new method for evaluating questions on chemical reactions in the context of remote education. This method can be used when binary grading is not sufficient as some tolerance may be acceptable. In order to determine a grade, the developed workflow uses the pairwise similarity assessment of two considered reactions, each encoded by a single molecular graph with the help of the Condensed Graph of Reaction (CGR) approach. This workflow is part of the ChemMoodle project and is implemented as a Moodle Plugin. It uses the Chemdoodle engine for reaction drawing and visualization and communicates with a REST server calculating the similarity score using ISIDA fragment descriptors. The plugin is open-source, accessible in GitHub (https://github.com/Laboratoire-de-Chemoinformatique/moodle-qtype_reacsimilarity) and on the Moodle plugin store (https://moodle.org/plugins/qtype_reacsimilarity?lang=en). Both similarity measures and fragmentation can be configured.Scientific contribution This work introduces an open-source method for evaluating chemical reaction questions within Moodle using the CGR approach. Our contribution provides a nuanced grading mechanism that accommodates acceptable tolerances in reaction assessments, enhancing the accuracy and flexibility of the grading process.

Analysis and Design of Cable Stayed Bridge

Cable Stayed Bridges are one of the most fascinating icons in the field of Engineering and are commonly used bridge typologies for spans between 200m and 1100mm due to their structural efficiency, cost and aesthetics. The basic structural form of a cable-stayed bridge is a series of overlapping triangles comprising the pylon, the cables, and the girder. All these members are under predominantly axial forces, with the cables under tension and both the pylon and the girder under compression. Axially loaded members are generally more efficient than flexural members. This contributes to the economy of the bridge. In this project, we have made an attempt to analyses a 200m span Cable Stayed Bridge using the STAAD Pro software based on the design parameters which includes the bending moment, shear force and displacement, Reaction Graph, Total Quantity Materail, etc. Key Words: Cable Stayed Bridges, Unknown Load Factor Optimization, Construction Stage Analysis, Influence Line Diagram (ILD), SFD BMD Diagram, Analysis Design.

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability.

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

A novel method for quantifying variations in NO formation pathways using a sub-mechanism accounting and reaction tracing algorithm

This work presents a new implementation of reaction path analysis that traces molar fluxes attributable to various sub-mechanisms from inception through to their final product. The post-processing procedure involves creating a network of these fluxes and quantifying the contributions made by the specified formation and destruction mechanisms through an iterative process starting from the individual initiation reactions. A key distinction of this approach is that the reaction fluxes are directly correlated to their initiation reaction, which is then followed throughout the entire reaction network. This allows for the distinction of contribution to the formation of a target species from an arbitrary number of sources. The effectiveness of this approach is demonstrated by examining the formation of NO in three different case studies: (1) the impact of equivalence ratio on methane/air premixed counterflow flames, (2) the effect of hydrogen addition on ultra-lean methane/air premixed counter flames, and (3) the influence of pressure elevation in partially premixed methane/air flames. The results are presented in emission indexes, NO production rates throughout the flame, and multidigraphs illustrating the allocated flows between the species. Compared to replicated analysis methods previously used for these case studies, this new approach provides a more comprehensive and detailed analysis for several reasons. Firstly, the method does not require duplicated simulations with modified kinetic mechanisms, but is applied purely post-processing. This avoids the previous problems of needing to re-simulate the flame multiple times and allows for the fully coupled chemistry set. Secondly, the method allows for an arbitrarily defined set of initiation pathways which propagate through all possible routes within the network. This allows for the identification of spatial/temporal variability in the sub-mechanisms and thereby yields more information than the integral values of previous methods.Novelty and significance statementUnderstanding reaction pathways that lead to the formation of pollutants such as NO is an essential aspect in the development of new sustainable and low-emission combustion concepts. However, evaluating the different underlying sub-mechanisms is difficult due to the high degree of interconnectedness and spatial/temporal overlap of the reaction pathways. This work, therefore, presents a new algorithm that can be used to investigate the contributions of an arbitrary number of sub-mechanisms to the formation or decomposition of a species. The results can be expressed not only as integral values, but also as spatially and/or temporally resolved production rates and reaction path figures and graphs, leading to a more comprehensive and detailed analysis. The approach is also applied as a purely post-processing technique, which does not require multiple simulations with different chemical kinetic mechanisms as has previously be applied.

Physiologically based radiopharmacokinetic (PBRPK) modeling to simulate and analyze radiopharmaceutical therapies: studies of non-linearities, multi-bolus injections, and albumin binding

BackgroundWe aimed to develop a publicly shared computational physiologically based pharmacokinetic (PBPK) model to reliably simulate and analyze radiopharmaceutical therapies (RPTs), including probing of hot-cold ligand competitions as well as alternative injection scenarios and drug designs, towards optimal therapies.ResultsTo handle the complexity of PBPK models (over 150 differential equations), a scalable modeling notation called the “reaction graph” is introduced, enabling easy inclusion of various interactions. We refer to this as physiologically based radiopharmacokinetic (PBRPK) modeling, fine-tuned specifically for radiopharmaceuticals. As three important applications, we used our PBRPK model to (1) study the effect of competition between hot and cold species on delivered doses to tumors and organs at risk. In addition, (2) we evaluated an alternative paradigm of utilizing multi-bolus injections in RPTs instead of prevalent single injections. Finally, (3) we used PBRPK modeling to study the impact of varying albumin-binding affinities by ligands, and the implications for RPTs. We found that competition between labeled and unlabeled ligands can lead to non-linear relations between injected activity and the delivered dose to a particular organ, in the sense that doubling the injected activity does not necessarily result in a doubled dose delivered to a particular organ (a false intuition from external beam radiotherapy). In addition, we observed that fractionating injections can lead to a higher payload of dose delivery to organs, though not a differential dose delivery to the tumor. By contrast, we found out that increased albumin-binding affinities of the injected ligands can lead to such a differential effect in delivering more doses to tumors, and this can be attributed to several factors that PBRPK modeling allows us to probe.ConclusionsAdvanced computational PBRPK modeling enables simulation and analysis of a variety of intervention and drug design scenarios, towards more optimal delivery of RPTs.

A Kinetic Finite Volume Discretization of the Multidimensional PIDE Model for Gene Regulatory Networks

In this paper, a finite volume discretization scheme for partial integro-differential equations (PIDEs) describing the temporal evolution of protein distribution in gene regulatory networks is proposed. It is shown that the obtained set of ODEs can be formally represented as a compartmental kinetic system with a strongly connected reaction graph. This allows the application of the theory of nonnegative and compartmental systems for the qualitative analysis of the approximating dynamics. In this framework, it is straightforward to show the existence, uniqueness and stability of equilibria. Moreover, the computation of the stationary probability distribution can be traced back to the solution of linear equations. The discretization scheme is presented for one and multiple dimensional models separately. Illustrative computational examples show the precision of the approach, and good agreement with previous results in the literature.

Multi-Instance Learning Approach to the Modeling of Enantioselectivity of Conformationally Flexible Organic Catalysts.

Computational design of chiral organic catalysts for asymmetric synthesis is a promising technology that can significantly reduce the material and human resources required for the preparation of enantiopure compounds. Herein, for the modeling of catalysts' enantioselectivity, we propose to use the multi-instance learning approach accounting for multiple catalyst conformers and requiring neither conformer selection nor their spatial alignment. A catalyst was represented by an ensemble of conformers, each encoded by three-dimesinonal (3D) pmapper descriptors. A catalyzed reactant transformation was converted into a single molecular graph, a condensed graph of reaction, encoded by 2D fragment descriptors. A whole chemical reaction was finally encoded by concatenated 3D catalyst and 2D transformation descriptors. The performance of the proposed method was demonstrated in the modeling of the enantioselectivity of homogeneous and phase-transfer reactions and compared with the state-of-the-art approaches.

Comprehensive exploration of graphically defined reaction spaces

Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.

Exploring physical sciences learners’ graphical interpretation of rate and extent of reaction graphs

Graphs are very important in all areas of science, and they are an essential part of tertiary, high school, and primary school learning, worldwide. A solid understanding of graphical interpretation is essential for understanding today's world and becoming scientifically literate. The aim of this study was to explore grade 12 learners’ graphical interpretation of the rate and extent of reaction topic. A mixed-method methodology was adopted for this study. A purposive sampling technique was used to sample participants from the accessible population in King Cetshwayo District KwaZulu-Natal province in South Africa. One-hundred and forty-six (146) grade 12 Physical Sciences learners formed the sample. A validated two-tier diagnostic questionnaire and semi-structured interviews were used to collect data. Quantitative data were analysed using SPSS version 25 and inductive coding was used to qualitative data. The Johnstone triangle and the Peircean semiotic modes were employed as the theoretical framework. The findings indicated that learners rely on definitions to interpret graphs. Most of the learners failed to interpret the salient features of the graphs. The findings of this study are diagnostic, and they assist module designers and educators in determining challenges learners face when interpreting graphs in chemistry. Implications for instructional approaches particular to the rate of reaction graphs are discussed. Further studies are needed on instructional practices and their effect on students’ ability to interpret rate and extent of reaction graphs. Keywords: Graphical interpretation; rate and extent of reaction; Johnstone triangle

ПОИСК ЭКСТРЕМАЛЬНОГО СОДЕРЖАНИЯ КОМПОНЕНТА В РЕАГИРУЮЩЕЙ СИСТЕМЕ С ПОМОЩЬЮ ГРАФА ХИМИЧЕСКИХ РЕАКЦИЙ

The article presents a technique for calculating a maximum amount of a substance in a closed system using a step-by-step graph of chemical reactions. A list of probable one- and two-particle reversible reactions is generated based on the substances that may be a part of the reacting system. The list is narrowed down according to the research objectives and conditions of the reacting system. The obtained list corresponds to the incidence matrix, which is suitable for analyzing the graph via a computer. The search for the maximum amount of the component in question is conducted through the comparison of weights of the graph’s edges at each stage of its intermediate reactions. The graph’s weights are calculated based on the kinetic coefficients of reactions. They also determine the dominant reaction. The amount is calculated under the premise that the dominant reaction is equilibrium. The maximum amount of hydroperoxyl radical HO2 in a hydrogen H and oxygen O reacting system is calculated.

Parametric Optimization of Material Removal Rate of EDM (AISI-4147) by using Taguchi Approach

Electric discharge machine is a non-conventional machining. In which there is no contact between tool and work piece. By EDM process machining of hard material components that are difficult to machine such as heat treated tool steels, ceramics, composites, carbides, heat resistance steel which are used in die and mould producing industries, aero space and nuclear industries. EDM is a non-contact type technique of material removal and in this spark is used to remove the material from the work piece. Since the EDM is an attractive means of making tool and dies with high accuracy in dimension and surface finish quality of micro scale parts. The present experimental research studies the process parameters that are influencing the machining concert and its productivity. A collective approach is used for the optimization in parameters and performance characteristics which are based on Taguchi method. The experiments are based on Taguchi‟s L9 orthogonal array. The reaction graph and reaction table of each near of machining constraints are took from Taguchi method to select the optimum levels of machining factors. In the work, the machining parameters for thesis are current, pulse on time and pulse off time, which are optimized for minimum tool wear rate (TWR), maximum material removal rate (MRR) and minimum surface roughness during electro discharge machining of AISI 4147. Analysis of Variance is also used to find out variable affecting the various responses mentioned above.

Construction of a Homogeneous Enzyme-Free Autocatalytic Nucleic Acid Machinery for High-Performance Intracellular Imaging of MicroRNA

Construction of a Homogeneous Enzyme-Free Autocatalytic Nucleic Acid Machinery for High-Performance Intracellular Imaging of MicroRNA

Pathway Driven Target Selection in Klebsiella pneumoniae: Insights Into Carbapenem Exposure.

Carbapenem-resistant Klebsiella pneumoniae (CR-KP) represents an emerging threat to public health. CR-KP infections result in elevated morbidity and mortality. This fact, coupled with their global dissemination and increasingly limited number of therapeutic options, highlights the urgency of novel antimicrobials. Innovative strategies linking genome-wide interrogation with multi-layered metabolic data integration can accelerate the early steps of drug development, particularly target selection. Using the BioCyc ontology, we generated and manually refined a metabolic network for a CR-KP, K. pneumoniae Kp13. Converted into a reaction graph, we conducted topological-based analyses in this network to prioritize pathways exhibiting druggable features and fragile metabolic points likely exploitable to develop novel antimicrobials. Our results point to the aptness of previously recognized pathways, such as lipopolysaccharide and peptidoglycan synthesis, and casts light on the possibility of targeting less explored cellular functions. These functions include the production of lipoate, trehalose, glycine betaine, and flavin, as well as the salvaging of methionine. Energy metabolism pathways emerged as attractive targets in the context of carbapenem exposure, targeted either alone or in conjunction with current therapeutic options. These results prompt further experimental investigation aimed at controlling this highly relevant pathogen.

Guest‐Dependent Isomer Convergence of a Permanently Fluxional Coordination Cage

AbstractA fluxional bis‐monodentate ligand, based on the archetypal shape‐shifting molecule bullvalene, self‐assembles with M2+ (M=Pd2+ or Pt2+) to produce a highly complex ensemble of permanently fluxional coordination cages. Metal‐mediated self‐assembly selects for an M2L4 architecture while maintaining shape‐shifting ligand complexity. A second level of simplification is achieved with guest‐exchange; the binding of halides within the M2L4 cage mixture results in a convergence to a cage species with all four ligands present as the “B isomer”. Within this confine, the reaction graph of the bullvalene is greatly restricted, but gives rise to a mixture of 38 possible diastereoisomers in rapid exchange. X‐ray crystallography reveals a preference for an achiral form consisting of both ligand enantiomers. Through a combination of NMR spectroscopy and DFT calculations, we elucidate the restricted isomerisation pathway of the permanently fluxional M2L4 assembly.

Guest-Dependent Isomer Convergence of a Permanently Fluxional Coordination Cage.

A fluxional bis‐monodentate ligand, based on the archetypal shape‐shifting molecule bullvalene, self‐assembles with M2+ (M=Pd2+ or Pt2+) to produce a highly complex ensemble of permanently fluxional coordination cages. Metal‐mediated self‐assembly selects for an M2L4 architecture while maintaining shape‐shifting ligand complexity. A second level of simplification is achieved with guest‐exchange; the binding of halides within the M2L4 cage mixture results in a convergence to a cage species with all four ligands present as the “B isomer”. Within this confine, the reaction graph of the bullvalene is greatly restricted, but gives rise to a mixture of 38 possible diastereoisomers in rapid exchange. X‐ray crystallography reveals a preference for an achiral form consisting of both ligand enantiomers. Through a combination of NMR spectroscopy and DFT calculations, we elucidate the restricted isomerisation pathway of the permanently fluxional M2L4 assembly.

Product control and a study of the structural change process during the recycling of lithium-ion batteries based on the carbothermic reduction method

Carbothermal reduction to recover lithium-ion batteries is an environmentally friendly recycling method. This work provides a theoretical analysis of the thermodynamics and an experimental verification process for the carbon thermal reduction recovery of lithium cobaltate (LiCoO2), as well as an explanation of the microstructural changes. The reaction conditions are controlled to obtain the ideal recovery product. A thermodynamic graph of the possible reaction between LiCoO2 and graphite (C) and the reduction of cobalt oxide (CoO, Co2O3, Co3O4) by carbon monoxide (CO) is obtained by thermodynamic analysis. The feasibility of the carbothermic reduction reaction of LiCoO2 at high temperature is studied under standard atmospheric pressure. By controlling the reaction temperature (800 °C) and the ratio of the reactants (LiCoO2/C = 4:5), the reduction products cobalt monoxide (CoO) and lithium carbonate (Li2CO3) are obtained, and the recovery rates are 89% and 84%, respectively. From the perspective of the crystal structure, the reduction process of LiCoO2 is analyzed. The Li-O bond in the LiCoO2 crystal structure is destroyed by CO, which promotes destruction of the Li-O octahedral structure and the formation of a Co-O octahedron. The Li-O octahedron is transformed into a tetrahedral structure in Li2O, and Li2O then reacts with CO2 to form Li2CO3; the Co-O octahedrons combine to form a CoO crystal structure. When the temperature continues to rise, CoO is reduced to Co. The carbothermic reduction recovery method does not require any additional hazardous chemicals and can avoid secondary pollution during the recovery process. This research provides theoretical support for the industrial recycling of lithium-ion batteries.

Kinetic discretization of one-dimensional nonlocal flow models

We show that one-dimensional nonlocal flow models in PDE form with Lighthill-Whitham-Richards flux supplemented with appropriate in- and out-flow terms can be spatially discretized with a finite volume scheme to obtain formally kinetic models with physically meaningful reaction graph structure. This allows the utilization of the theory of chemical reaction networks, as demonstrated here via the stability analysis of a flow model with circular topology. We further propose an explicit time discretization and a Courant-Friedrichs-Lewy condition ensuring many advantageous properties of the scheme. Additional characteristics, including monotonicity and the total variation diminishing property are also discussed.

Structural classification of continuous time Markov chains with applications

This paper is motivated by examples from stochastic reaction network theory. The Q-matrix of a stochastic reaction network can be derived from the reaction graph, an edge-labelled directed graph encoding the jump vectors of an associated continuous time Markov chain on the invariant space . An open question is how to decompose the space into neutral, trapping, and escaping states, and open and closed communicating classes, and whether this can be done from the reaction graph alone. Such general continuous time Markov chains can be understood as natural generalizations of birth-death processes, incorporating multiple different birth and death mechanisms. We characterize the structure of imposed by a general Q-matrix generating continuous time Markov chains with values in , in terms of the set of jump vectors and their corresponding transition rate functions. Thus the setting is not limited to stochastic reaction networks. Furthermore, we define structural equivalence of two Q-matrices, and provide sufficient conditions for structural equivalence. Examples are abundant in applications. We apply the results to stochastic reaction networks, a Lotka-Volterra model in ecology, the EnvZ-OmpR system in systems biology, and a class of extended branching processes, none of which are birth-death processes.

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction.

The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust, and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task-dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions, and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields, or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark data sets for future work. All data sets and the developed reaction GCNN model are available online, free of charge, and open source.