scholarly journals VDB Entropy Measures and Irregularity-Based Indices for the Rectangular Kekulene System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Weidong Zhao ◽  
K. Julietraja ◽  
P. Venugopal ◽  
Xiujun Zhang
Keyword(s):  
Coal Gasification ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Mathematical Expression ◽  
Cost Effective ◽  
Molecular Structures ◽  
Specific Chemical ◽  
Chemical Structures ◽  
Broad Array ◽  
Polycyclic Aromatic

Theoretical chemists are fascinated by polycyclic aromatic hydrocarbons (PAHs) because of their unique electromagnetic and other significant properties, such as superaromaticity. The study of PAHs has been steadily increasing because of their wide-ranging applications in several fields, like steel manufacturing, shale oil extraction, coal gasification, production of coke, tar distillation, and nanosciences. Topological indices (TIs) are numerical quantities that give a mathematical expression for the chemical structures. They are useful and cost-effective tools for predicting the properties of chemical compounds theoretically. Entropic network measures are a type of TIs with a broad array of applications, involving quantitative characterization of molecular structures and the investigation of some specific chemical properties of molecular graphs. Irregularity indices are numerical parameters that quantify the irregularity of a molecular graph and are used to predict some of the chemical properties, including boiling points, resistance, enthalpy of vaporization, entropy, melting points, and toxicity. This study aims to determine analytical expressions for the VDB entropy and irregularity-based indices in the rectangular Kekulene system.

scholarly journals Identify Similarities in Diverse Polycyclic Aromatic Hydrocarbons of Asphaltenes and Heavy Oils Revealed by Noncontact Atomic Force Microscopy: Aromaticity, Bonding, and Implications in Reactivity

2019 ◽  
Author(s):  
Yunlong Zhang
Keyword(s):  
Atomic Force Microscopy ◽  
Aromatic Hydrocarbons ◽  
Molecular Structures ◽  
Polynuclear Aromatic Hydrocarbons ◽  
Heavy Oils ◽  
Diverse Range ◽  
Force Microscopy ◽  
Chemical Structures ◽  
Atomic Force ◽  
Polycyclic Aromatic

Heavy oils are enriched with polycyclic (or polynuclear) aromatic hydrocarbons (PAH or PNA), but characterization of their chemical structures has been a great challenge due to their tremendous diversity. Recently, with the advent of molecular imaging with noncontact Atomic Force Microscopy (nc-AFM), molecular structures of petroleum has been imaged and a diverse range of novel PAH structures was revealed. Understanding these structures will help to understand their chemical reactivities and the mechanisms of their formation or conversion. Studies on aromaticity and bonding provide means to recognize their intrinsic structural patterns which is crucial to reconcile a small number of structures from AFM and to predict infinite number of diverse molecules in bulk. Four types of PAH structures can be categorized according to their relative stability and reactivity, and it was found that the most and least stable types are rarely observed in AFM, with most molecules as intermediate types in a subtle balance of kinetic reactivity and thermodynamic stability. Local aromaticity was found maximized when possible for both alternant and nonalternant PAHs revealed by the aromaticity index NICS (Nucleus-Independent Chemical Shift) values. The unique role of five-membered rings in disrupting the electron distribution was recognized. Especially, the presence of partial double bonds in most petroleum PAHs was identified and their implications in the structure and reactivity of petroleum are discussed.

A Deep Molecular Generative Model Based on Multi-Resolution Graph Variational Autoencoders

2021 ◽  
Author(s):  
zhenxiang gao ◽  
xinyu wang ◽  
Blake Blumenfeld Gaines ◽  
Jinbo Bi ◽  
minghu song
Keyword(s):  
Molecular Graph ◽  
Generative Models ◽  
Generation Process ◽  
Structure Generation ◽  
Specific Chemical ◽  
Chemical Structures ◽  
Structural Environment ◽  
Resolution Graph ◽  
As Graph ◽  
Graph Nodes

Deep generative models have recently emerged as encouraging tools for the de novo molecular structure generation. Even though considerable advances have been achieved in recent years, the field of generative molecular design is still in its infancy. One potential solution may be to integrate domain knowledge of structural or medicinal chemistry into the data-driven machine learning process to address specific deep molecule generation goals. This manuscript proposes a new graph-based hierarchical variational autoencoder (VAE) model for molecular generation. Training molecules are first decomposed into small molecular fragments. Unlike other motif-based molecular graph generative models, we further group decomposed fragments into different interchangeable fragment clusters according to their local structural environment around the attachment points where the bond-breaking occurs. In this way, each chemical structure can be transformed into a three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer, respectively. We construct a hierarchical VAE model to learn such three-layer hierarchical graph representations of chemical structures in a fine-to-coarse order, in which atoms, decomposed fragments, and related fragment clusters act as graph nodes at each corresponding graph layer. The decoder component is designed to iteratively select a fragment out of a predicted fragment cluster vocabulary and then attach it to the preceding substructure. The newly introduced third graph layer will allow us to incorporate specific chemical structural knowledge, e.g., interchangeable fragments sharing similar local chemical environments or bioisosteres derived from matched molecular pair analysis information, into the molecular generation process. It will increase the odds of assembling new chemical moieties absent in the original training set and enhance structural diversity/novelty scores of generated structures. Our proposed approach demonstrates comparatively good performance in terms of model efficiency and other molecular evaluation metrics when compared with several other graph- and SMILES-based generative molecular models. We also analyze how our generative models' performance varies when choosing different fragment sampling techniques and radius parameters that determine the local structural environment of interchangeable fragment clusters. Hopefully, our multi-level hierarchical VAE prototyping model might promote more sophisticated works of knowledge-augmented deep molecular generation in the future.

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 Editorial: Topological investigations of chemical networks

2021 ◽  
Vol 44 (1) ◽  
pp. 267-269
Author(s):  
Muhammad Javaid ◽  
Muhammad Imran
Keyword(s):  
Information Science ◽  
Biological Activities ◽  
Chemical Properties ◽  
Vital Role ◽  
Topological Indices ◽  
Molecular Structures ◽  
Quantitative Structure ◽  
Structure Property ◽  
Chemical Structures ◽  
Interdisciplinary Field

Abstract The topic of computing the topological indices (TIs) being a graph-theoretic modeling of the networks or discrete structures has become an important area of research nowadays because of its immense applications in various branches of the applied sciences. TIs have played a vital role in mathematical chemistry since the pioneering work of famous chemist Harry Wiener in 1947. However, in recent years, their capability and popularity has increased significantly because of the findings of the different physical and chemical investigations in the various chemical networks and the structures arising from the drug designs. In additions, TIs are also frequently used to study the quantitative structure property relationships (QSPRs) and quantitative structure activity relationships (QSARs) models which correlate the chemical structures with their physio-chemical properties and biological activities in a dataset of chemicals. These models are very important and useful for the research community working in the wider area of cheminformatics which is an interdisciplinary field combining mathematics, chemistry, and information science. The aim of this editorial is to arrange new methods, techniques, models, and algorithms to study the various theoretical and computational aspects of the different types of these topological indices for the various molecular structures.

scholarly journals Topological Analysis of PAHs using Irregularity based Indices

2021 ◽  
Vol 12 (3) ◽  
pp. 2970-2987
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Topological Analysis ◽  
Chemical Properties ◽  
Topological Descriptors ◽  
Graph Invariants ◽  
Chemical Structures ◽  
Potential Applications ◽  
Polycyclic Aromatic ◽  
Analytical Expressions

Topological descriptors are non-empirical graph invariants that characterize the structures of chemical molecules. The structural descriptors are vital components of QSAR/QSPR studies which form the basis for theoretical chemists to design and investigate new chemical structures. Irregularity indices are a class of topological descriptors that have been employed to study certain chemical properties of compounds. This article aims to compute analytical expressions of irregularity indices for three important classes of polycyclic aromatic hydrocarbons. The intriguing properties of these classes of compounds have several potential applications in wide-raging fields, which warrant a study of their properties from a structural perspective. Additionally, the 3D graphical representations of a few indices are presented, which will aid in analyzing the similarity of behavior among the indices.

scholarly journals L-MolGAN: An improved implicit generative model for large molecular graphs

2021 ◽  
Author(s):  
Yutaka Tsujimoto ◽  
Satoru Hiwa ◽  
Yushi Nakamura ◽  
Yohei Oe ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Molecular Graph ◽  
Chemical Properties ◽  
Molecular Size ◽  
Material Design ◽  
Generative Models ◽  
Molecular Structures ◽  
Generative Adversarial Networks ◽  
Baseline Model ◽  
Molecular Graphs ◽  
Model Training

<p>Deep generative models are used to generate arbitrary molecular structures with the desired chemical properties. MolGAN is a renowned molecular generation models that uses generative adversarial networks (GANs) and reinforcement learning to generate molecular graphs in one shot. MolGAN can effectively generate a small molecular graph with nine or fewer heavy atoms. However, the graphs tend to become disconnected as the molecular size increase. This poses a challenge to drug discovery and material design, where large molecules are potentially inclusive. This study develops an improved MolGAN for large molecule generation (L-MolGAN). In this model, the connectivity of molecular graphs is evaluated by a depth-first search during the model training process. When a disconnected molecular graph is generated, L-MolGAN rewards the graph a zero score. This procedure decreases the number of disconnected graphs, and consequently increases the number of connected molecular graphs. The effectiveness of L-MolGAN is experimentally evaluated. The size and connectivity of the molecular graphs generated with data from the ZINC-250k molecular dataset are confirmed using MolGAN as the baseline model. The model is then optimized for a quantitative estimate of drug-likeness (QED) to generate drug-like molecules. The experimental results indicate that the connectivity measure of generated molecular graphs improved by 1.96 compared with the baseline model at a larger maximum molecular size of 20 atoms. The molecules generated by L-MolGAN are evaluated in terms of multiple chemical properties, QED, synthetic accessibility, and log octanol–water partition coefficient, which are important in drug design. This result confirms that L-MolGAN can generate various drug-like molecules despite being optimized for a single property, i.e., QED. This method will contribute to the efficient discovery of new molecules of larger sizes than those being generated with the existing method.<br></p>

scholarly journals Identify Similarities in Diverse Polycyclic Aromatic Hydrocarbons of Asphaltenes and Heavy Oils Revealed by Noncontact Atomic Force Microscopy: Aromaticity, Bonding, and Implications in Reactivity

2019 ◽  
Author(s):  
Yunlong Zhang
Keyword(s):  
Atomic Force Microscopy ◽  
Aromatic Hydrocarbons ◽  
Molecular Structures ◽  
Polynuclear Aromatic Hydrocarbons ◽  
Heavy Oils ◽  
Diverse Range ◽  
Force Microscopy ◽  
Chemical Structures ◽  
Atomic Force ◽  
Polycyclic Aromatic

Heavy oils are enriched with polycyclic (or polynuclear) aromatic hydrocarbons (PAH or PNA), but characterization of their chemical structures has been a great challenge due to their tremendous diversity. Recently, with the advent of molecular imaging with noncontact Atomic Force Microscopy (nc-AFM), molecular structures of petroleum has been imaged and a diverse range of novel PAH structures was revealed. Understanding these structures will help to understand their chemical reactivities and the mechanisms of their formation or conversion. Studies on aromaticity and bonding provide means to recognize their intrinsic structural patterns which is crucial to reconcile a small number of structures from AFM and to predict infinite number of diverse molecules in bulk. Four types of PAH structures can be categorized according to their relative stability and reactivity, and it was found that the most and least stable types are rarely observed in AFM, with most molecules as intermediate types in a subtle balance of kinetic reactivity and thermodynamic stability. Local aromaticity was found maximized when possible for both alternant and nonalternant PAHs revealed by the aromaticity index NICS (Nucleus-Independent Chemical Shift) values. The unique role of five-membered rings in disrupting the electron distribution was recognized. Especially, the presence of partial double bonds in most petroleum PAHs was identified and their implications in the structure and reactivity of petroleum are discussed.

scholarly journals L-MolGAN: An improved implicit generative model for large molecular graphs

2021 ◽  
Author(s):  
Yutaka Tsujimoto ◽  
Satoru Hiwa ◽  
Yushi Nakamura ◽  
Yohei Oe ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Molecular Graph ◽  
Chemical Properties ◽  
Molecular Size ◽  
Material Design ◽  
Generative Models ◽  
Molecular Structures ◽  
Generative Adversarial Networks ◽  
Baseline Model ◽  
Molecular Graphs ◽  
Model Training

<p>Deep generative models are used to generate arbitrary molecular structures with the desired chemical properties. MolGAN is a renowned molecular generation models that uses generative adversarial networks (GANs) and reinforcement learning to generate molecular graphs in one shot. MolGAN can effectively generate a small molecular graph with nine or fewer heavy atoms. However, the graphs tend to become disconnected as the molecular size increase. This poses a challenge to drug discovery and material design, where large molecules are potentially inclusive. This study develops an improved MolGAN for large molecule generation (L-MolGAN). In this model, the connectivity of molecular graphs is evaluated by a depth-first search during the model training process. When a disconnected molecular graph is generated, L-MolGAN rewards the graph a zero score. This procedure decreases the number of disconnected graphs, and consequently increases the number of connected molecular graphs. The effectiveness of L-MolGAN is experimentally evaluated. The size and connectivity of the molecular graphs generated with data from the ZINC-250k molecular dataset are confirmed using MolGAN as the baseline model. The model is then optimized for a quantitative estimate of drug-likeness (QED) to generate drug-like molecules. The experimental results indicate that the connectivity measure of generated molecular graphs improved by 1.96 compared with the baseline model at a larger maximum molecular size of 20 atoms. The molecules generated by L-MolGAN are evaluated in terms of multiple chemical properties, QED, synthetic accessibility, and log octanol–water partition coefficient, which are important in drug design. This result confirms that L-MolGAN can generate various drug-like molecules despite being optimized for a single property, i.e., QED. This method will contribute to the efficient discovery of new molecules of larger sizes than those being generated with the existing method.<br></p>

scholarly journals Bringing Chemical Structures to Life with Augmented Reality, Machine Learning and Quantum Chemistry

2022 ◽  
Author(s):  
Sukolsak Sakshuwong ◽  
Hayley Weir ◽  
Umberto Raucci ◽  
Todd Martinez
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Augmented Reality ◽  
Quantum Chemistry ◽  
Mobile Application ◽  
Scientific Community ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Chemical Behavior ◽  
Chemical Structures

Abstract Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, a mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or PDB ID, and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

scholarly journals On Degree-Based Topological Indices of Symmetric Chemical Structures

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 619 ◽  
Author(s):  
Jia-Bao Liu ◽  
Haidar Ali ◽  
Muhammad Shafiq ◽  
Usman Munir
Keyword(s):  
Molecular Descriptor ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Chemical Compounds ◽  
Topological Indices ◽  
Chemical Structures ◽  
Physico Chemical ◽  
First Time ◽  
Ga Index ◽  
Atom Bond

A Topological index also known as connectivity index is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are numerical parameters of a graph which characterize its topology and are usually graph invariant. In QSAR/QSPR study, physico-chemical properties and topological indices such as Randić, atom-bond connectivity (ABC) and geometric-arithmetic (GA) index are used to predict the bioactivity of chemical compounds. Graph theory has found a considerable use in this area of research. In this paper, we study HDCN1(m,n) and HDCN2(m,n) of dimension m , n and derive analytical closed results of general Randić index R α ( G ) for different values of α . We also compute the general first Zagreb, ABC, GA, A B C 4 and G A 5 indices for these Hex derived cage networks for the first time and give closed formulas of these degree-based indices.

Sign in / Sign up

Export Citation Format

Share Document