scholarly journals Predicting Physical−Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

2006 ◽  
Vol 46 (5) ◽  
pp. 2030-2042 ◽  
Author(s):  
Luca Bernazzani ◽  
Celia Duce ◽  
Alessio Micheli ◽  
Vincenzo Mollica ◽  
Alessandro Sperduti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Physical Chemical ◽  
Recursive Neural Networks

scholarly journals Physical-chemical properties studying of molecular structures via topological index calculating

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 261-269
Author(s):  
Jianzhang Wu ◽  
Mohammad Reza Farahani ◽  
Xiao Yu ◽  
Wei Gao
Keyword(s):  
Topological Index ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Connectivity Index ◽  
Chemical Characteristics ◽  
Eccentric Connectivity Index ◽  
Distance Computation ◽  
Physical Chemical ◽  
Mathematical Derivation ◽  
Mathematical Standpoint

AbstractIt’s revealed from the earlier researches that many physical-chemical properties depend heavily on the structure of corresponding moleculars. This fact provides us an approach to measure the physical-chemical characteristics of substances and materials. In our article, we report the eccentricity related indices of certain important molecular structures from mathematical standpoint. The eccentricity version indices of nanostar dendrimers are determined and the reverse eccentric connectivity index for V-phenylenic nanotorus is discussed. The conclusions we obtained mainly use the trick of distance computation and mathematical derivation, and the results can be applied in physics engineering.

scholarly journals Electron Energy Studying of Molecular Structures via Forgotten Topological Index Computation

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wei Gao ◽  
Weifan Wang ◽  
Muhammad Kamran Jamil ◽  
Mohammad Reza Farahani
Keyword(s):  
Carbon Atom ◽  
Electron Energy ◽  
Topological Index ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Sum Of Squares ◽  
Molecular Structures ◽  
Physical Chemical ◽  
Vertex Degrees ◽  
Chemical Material

It is found from the earlier studies that the structure-dependency of totalπ-electron energyEπheavily relies on the sum of squares of the vertex degrees of the molecular graph. Hence, it provides a measure of the branching of the carbon-atom skeleton. In recent years, the sum of squares of the vertex degrees of the molecular graph has been defined as forgotten topological index which reflects the structure-dependency of totalπ-electron energyEπand measures the physical-chemical properties of molecular structures. In this paper, in order to research the structure-dependency of totalπ-electron energyEπ, we present the forgotten topological index of some important molecular structures from mathematical standpoint. The formulations we obtained here use the approach of edge set dividing, and the conclusions can be applied in physics, chemical, material, and pharmaceutical engineering.

scholarly journals Study of Vanadium Carbide Structures Based on Ve and Ev-Degree Topological Indices

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Abdul Rauf ◽  
Saba Maqbool ◽  
Muhammad Naeem ◽  
Adnan Aslam ◽  
Hamideh Aram ◽  
...  
Keyword(s):  
Fossil Fuels ◽  
Vanadium Carbide ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Biologically Active ◽  
Topological Descriptors ◽  
Biological Applications ◽  
Chemical Behavior ◽  
Physical Chemical ◽  
Atom Bond

Vanadium is a biologically active product with significant industrial and biological applications. Vanadium is found in a variety of minerals and fossil fuels, the most common of which are sandstones, crude oil, and coal. Topological descriptors are numerical numbers assigned to the molecular structures and have the ability to predict certain of their physical/chemical properties. In this paper, we have studied topological descriptors of vanadium carbide structure based on ev and ve degrees. In particular, we have computed the closed forms of Zagreb, Randic, geometric-arithmetic, and atom-bond connectivity (ABC) indices of vanadium carbide structure based on ev and ve degrees. This kind of study may be useful for understanding the biological and chemical behavior of the structure.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Chlorinated Organics in Nearshore Waters and Tributaries of the St. Clair River

1986 ◽  
Vol 21 (3) ◽  
pp. 344-350 ◽  
Author(s):  
Barry G. Oliver ◽  
Klaus L.E. Kaiser
Keyword(s):  
United States ◽  
Suspended Sediment ◽  
Waste Disposal ◽  
Large Volume ◽  
Water Samples ◽  
Chemical Properties ◽  
Low Level ◽  
Chemical Company ◽  
Physical Chemical ◽  
The Individual

Abstract The concent rat ions of hexachloroethane (HCE), hexachlorobutadiene (HCBD), pentachlorobenzene (QCB), hexachlorobenzene (HCB) and octachlorostyrene (OCS) in large volume water samples show that the major sources of these chemicals to the St. Clair River are Dow Chemical Company effluents and, to a lesser degree, Sarnia’s Township ditch which drains one of Dow’s waste disposal sites. Tributaries entering the river on both sides of the Canada/United States border contain measurable concentrations of these chemicals indicating low level contamination throughout the area. The degree of water/suspended sediment partitioning of the chemicals (Kp) was studied. Kp values for the individual chemicals changed in a manner consistent with changes in their physical-chemical properties.

Polymer-Graphene Nanoassemblies and their Applications in Cancer Theranostics

2020 ◽  
Vol 20 (11) ◽  
pp. 1340-1351 ◽  
Author(s):  
Ponnurengam M. Sivakumar ◽  
Matin Islami ◽  
Ali Zarrabi ◽  
Arezoo Khosravi ◽  
Shohreh Peimanfard
Keyword(s):  
Mechanical Stability ◽  
Chemical Properties ◽  
The Other ◽  
Hydrophilic Polymer ◽  
Two Dimensional ◽  
Normal Tissues ◽  
Physical Chemical ◽  
Anti Cancer ◽  
Good Biocompatibility ◽  
Cancer Theranostics

Background and objective: Graphene-based nanomaterials have received increasing attention due to their unique physical-chemical properties including two-dimensional planar structure, large surface area, chemical and mechanical stability, superconductivity and good biocompatibility. On the other hand, graphene-based nanomaterials have been explored as theranostics agents, the combination of therapeutics and diagnostics. In recent years, grafting hydrophilic polymer moieties have been introduced as an efficient approach to improve the properties of graphene-based nanomaterials and obtain new nanoassemblies for cancer therapy. Methods and results: This review would illustrate biodistribution, cellular uptake and toxicity of polymergraphene nanoassemblies and summarize part of successes achieved in cancer treatment using such nanoassemblies. Conclusion: The observations showed successful targeting functionality of the polymer-GO conjugations and demonstrated a reduction of the side effects of anti-cancer drugs for normal tissues.

scholarly journals Shellfish Chitosan Potential in Wine Clarification

2021 ◽  
Vol 11 (10) ◽  
pp. 4417
Author(s):  
Veronica Vendramin ◽  
Gaia Spinato ◽  
Simone Vincenzi
Keyword(s):  
Molecular Structure ◽  
Chemical Properties ◽  
Allergic Reactions ◽  
Chitin Content ◽  
Deacetylation Degree ◽  
Physical Chemical ◽  
Similar Chemical ◽  
Fungal Chitosan ◽  
Significant Difference ◽  
Fish Industry

Chitosan is a chitin-derived fiber, extracted from the shellfish shells, a by-product of the fish industry, or from fungi grown in bioreactors. In oenology, it is used for the control of Brettanomyces spp., for the prevention of ferric, copper, and protein casse and for clarification. The International Organisation of Vine and Wine established the exclusive utilization of fungal chitosan to avoid the eventuality of allergic reactions. This work focuses on the differences between two chitosan categories, fungal and animal chitosan, characterizing several samples in terms of chitin content and degree of deacetylation. In addition, different acids were used to dissolve chitosans, and their effect on viscosity and on the efficacy in wine clarification were observed. The results demonstrated that even if fungal and animal chitosans shared similar chemical properties (deacetylation degree and chitin content), they showed different viscosity depending on their molecular weight but also on the acid used to dissolve them. A significant difference was discovered on their fining properties, as animal chitosans showed a faster and greater sedimentation compared to the fungal ones, independently from the acid used for their dissolution. This suggests that physical–chemical differences in the molecular structure occur between the two chitosan categories and that this significantly affects their technologic (oenological) properties.

scholarly journals Electrospinning: A promising technique for drug delivery systems

2020 ◽  
Vol 59 (1) ◽  
pp. 441-454
Author(s):  
Carlos A. Martínez-Pérez
Keyword(s):  
Drug Delivery ◽  
Drug Delivery Systems ◽  
Chemical Properties ◽  
Delivery Systems ◽  
Extrinsic Factor ◽  
Promising Technique ◽  
Physical Chemical ◽  
Set Up ◽  
Final System ◽  
Intense Research

AbstractIn the last years, electrospinning has become a technique of intense research to design and fabricate drug delivery systems (DDS), during this time a vast variety of DDS with mainly electrospun polymers and many different active ingredient(s) have been developed, many intrinsic and extrinsic factor have influence in the final system, there are those that can be attributed to the equipment set up and that to the physical-chemical properties of the used materials in the fabrication of DDS. After all, this intense research has generated a great amount of DDS loaded with one or more drugs. In this manuscript a review with the highlights of different kind of systems for drug delivery systems is presented, it includes the basic concepts of electrospinning, types of equipment set up, polymer/drug systems, limitations and challenges that need to be overcome for clinical applications.

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