Structure-property Relationship Analysis Research Articles

Precision Molecular Engineering of Compact Near-Infrared Fluorophores.

Organic fluorophores with near-infrared (NIR) emission and reduced molecular size are crucial for advancing bioimaging and biosensing technologies. Traditional methods, such as conjugation expansion and heteroatom engineering, often fail to reduce fluorophore size without sacrificing NIR emission properties. Addressing this challenge, our study utilized quantum chemical calculations and structure-property relationship analysis to establish an iterative design approach and enable precision engineering for compact, single-benzene-based NIR fluorophores. These newly developed fluorophores exhibit emissions up to 759 nm and maintain molecular weights as low as 192 g/mol, approximately 50% of that of Cy7. Additionally, they display unique environmental sensitivity─nonemissive in aqueous solutions but highly emissive in lipid environments. This property significantly enhances their utility in wash-free imaging of live cells. Our findings mark a substantial breakthrough in fluorophore engineering, paving the way for more efficient and adaptable imaging methodologies.

Vertex-Edge-Weighted Molecular Graphs: A Study on Topological Indices and Their Relevance to Physicochemical Properties of Drugs Used in Cancer Treatment.

Quantitative structure-property relationship (QSPR) analysis plays a crucial role in predicting physicochemical properties and biological activities of pharmaceutical compounds, aiding in drug design and optimization. This study focuses on leveraging QSPR within the framework of vertex and edge-weighted (VEW) molecular graphs, exploring their significance in drug research. By examining 48 drugs used in the treatment of various cancers and their physicochemical properties, previous studies serve as a foundation for our research. Introducing a novel methodology for computing vertex and edge weights, we highlight the importance of considering atomic properties and interbond dynamics. Statistical analysis, employing linear regression models, reveals enhanced correlations between topological indices and the physicochemical properties of drugs. Comparison with previous studies on unweighted molecular graphs highlights the enhancements achieved with our approach.

A paradigmatic approach to the topological measure of babesiosis drugs and estimating physical properties via QSPR analysis.

New developments in the field of chemical graph theory have made it easier to comprehend how chemical structures relate to the graphs that underlie them on a more profound level using the ideas of classical graph theory. Chemical graphs can be effectively probed with the help of quantitative structure-property relationship (QSPR) analysis. In order to statistically correlate physical attributes. Earlier research on medications motivated us to explore the quantitative structure-property relationships (QSPR) of babesiosis. We examined the babesiosis drugs data and used topological indices to do this. A measurement that reflects the theoretical characteristics of drugs is a topological index. The chemical structures of drugs that relieve pain are studied using well-known degree-based topological indices. Mepron, azithromycin, clindamycin, imidocarb, triclosan and other medications are among them. These drugs are administered to minimize or eliminate the discomfort that is felt in the affected location. The chemical structure of a molecule is represented as a graph. Further research into topological indices' QSPR analysis revealed so as a substantial link with the physical characteristics of drugs utilized in the production of drugs to halt the disease. The analysis of topological indices served as evidence for this. The results demonstrate how well the QSPR experiments on applying regression technique are useful in development of novel drugs for babesiosis and predict the characteristics of babesiosis drugs. The usefulness of applying TIs in this situation is demonstrated by the linear regression model's minimum error and correct prediction of these attributes.

Topological characterization of [n]-triangulenes through degree-based molecular descriptors with the prediction to π-electron energy

Abstract Triangulene and its π-extended homologues are a family of polycyclic aromatic hydrocarbons
with a peculiar chemical structure. They are recognized for their intricate structural configurations
and electrical properties, which make them a promising material for potential uses in spintronics.
They are built of benzenoid rings fused in a triangular manner. Topological indices are widely
utilized as graph theoretical measures for evaluating the physicochemical properties of polycyclic
aromatic hydrocarbons by analyzing their molecular structures, makes them hold a significant position
in the domain of mathematical and computational chemistry. In this study, a mathematical
exploration of topological indices of [n]-triangulenes has been done to establish a comprehensive
understanding of their applications and significance. Generalized expressions for topological indices
have been computed, and their predictive power for various physicochemical properties has been
studied using statistical methods. Also, a quantitative structure-property relationship analysis of
[n]-triangulene’s energetic characteristics has been performed. Moreover, a generalized algebraic
expression to predict the π-electron energy of [n]-triangulene structure has been derived.

Quantitative structure properties relationship ([formula omitted]) analysis for physicochemical properties of nonsteroidal anti-inflammatory drugs (NSAIDs) usingVe degree-based reducible topological indices

Nonsteroidal Anti-Inflammatory Drugs (NSAIDs) are a class of medications that are used for different therapeutic uses. They effectively alleviate pain, reduce inflammation, and manage fever. These drugs are available in various forms. NSAIDs are prescribed by healthcare professionals to address a wide range of symptoms, from headaches and dental pain to conditions like arthritis and muscle stiffness. In this work, we use ve-degree-based reducible topological descriptors in quantitative structure-property relationship (QSPR) analysis to estimate the physicochemical properties of NSAIDs. In the first step, we have developed a MAPLE-based code to compute the reducible ve-degree-based topological descriptors of NSAIDs. Then, a linear regression model was used to estimate four physicochemical properties of seventy NSAIDs. It has been observed that two physicochemical properties, namely Molecular Weight and Complexity show a very strong correlation with the reducible ve-degree-based topological descriptors. For both cases, the value of correlation coefficient is greater than 0.9. Finally, quadratic and cubic regression models were constructed, and a comparative analysis with these models is presented. These results may help enhance the understanding of NSAIDs medication structures and aid in predicting their pharmacological activity.

Steiner 3-Wiener Index of Zigzag Polyhex Nanotubes.

Let G be a connected graph and S be a k element subset of the vertex set V(G) of G. Steiner distance is a natural generalization of the usual graph distance. The Steiner-k distance dG(S) between the vertices of S is the minimum size among all connected subgraphs whose vertex set contains S. The generalized indices based on Steiner distances have several applications in the real world. It is a well-known fact that "Steiner Problem" is NP-complete and hence any parameter based on Steiner distance is also an NP problem. The objective of this work is to determine the Steiner 3-Wiener index for an important chemical structure called the zigzag polyhex nanotube. In this paper, we present an algorithm for computing the Steiner 3-Wiener index (SW3) for zigzag polyhex nanotubes. The developed algorithm addresses the complexities associated with exponential increments in the number of vertices as the nanotube's circumference or length expands. The obtained SW3 values for zigzag polyhex nanotubes can be used for Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) analyses. We have presented an algorithm and listed the numerical values of SW3 for various parameters of circumference and length of a zigzag polyhex nanotube to facilitate their utilization in QSAR/ QSPR analyses. We have obtained the time complexity for the algorithm, which shows that the SW3 values are computationally intensive. To explain this complex nature, we have used multiple linear regression to fit log SW3 values corresponding to log p and log q, the radius and length of a nanotube. The algorithm addresses the complexities associated with the exponential increase in vertices as the nanotube's circumference or length expands. Furthermore, we provide SW3 values for various parameter combinations up to circumference or length 17, and a general relation to determine the value of SW3, facilitating its utilization in real-world applications. These values serve as crucial descriptors for understanding the structural nuances of zigzag polyhex nanotubes, that find applications in material science, drug design, drug discovery, etc.

Computational analysis of diameter eccentricity based and hyper diameter eccentricity based indices for linear saturated monocarboxylic acids

Chemical graph theory deals with chemical graphs, which are used to represent chemical systems. Topological indices of molecular graphs are an important area of research in chemical graph theory. These indices are numerical values associated with compounds that aim to establish connections between chemical structures and physical attributes, chemical reactivity, and biological activity. Many graph polynomials have been proposed to compute various topological indices. Quantitative structure-property relationship (QSPR) analysis of chemical compounds involves several regression methods that rely on these topological indices. In this article, we utilize the newly introduced DƐ-polynomial to calculate the eccentricitybased indices such as the diameter eccentricity based and hyper diameter eccentricity based index values for nineteen linear saturated monocarboxylic acids. Quantitative structureproperty relationship (QSPR) analysis is also performed for the seven thermodynamic properties of monocarboxylic acids. The relationship between thermodynamic properties and topological indices is explored using linear, quadratic and cubic regression models. The best fit curvilinear regression models were determined based on the R2 and RMSE values of the models. To further investigate the statistical significance of our models, we also conducted chi-square goodness-of-fit tests to identify the best fit models.

Structure–property relations of sodium iron phosphate nuclear waste glasses: Effects of iron redox ratio and glass composition

AbstractIron phosphate glasses, known for their exceptional chemical durability and potential applicability in nuclear waste management, have gained significant attention over the years. The structures of these glasses are complicated by the coexistence of Fe3+ and Fe2+, which plays a crucial role in determining their structures and properties. This work uses molecular dynamics simulations to study the structural changes in Na2O–Fe2O3–P2O5 glasses with varying glass composition and Fe2+/Fe3+ redox ratio. It was found that the redox ratio and modifier contents significantly affected the short‐range and medium‐range orders in the glasses. Significant changes in the local environments around P5+ and Fe3+ were observed, as reflected by the bond distances and coordination numbers. Na+ cations are found to preferentially associate with Fe3+ (rather than Fe2+), whereas Fe2+ has stronger association with P5+ than Na+, confirming the structural role of Fe2+ as a glass modifier. The disruptions in P–O–P linkages upon increasing FeO suggest that FeO causes glass depolymerization. These glasses achieved higher connectivity with increasing ratios, conerting phosphorous Q2 to Q3 units and iron Q5 units to Q4 units. The decrease of nonbridging oxygen fractions with increasing ratios, through creating P–O–Fe linkages, is the main reason of enhanced network connectivity. Quantitative structure–property relationship analyses with different structural descriptors were used to correlate with measured properties. The analyses provided valuable insights into structure–property relationships, emphasizing the importance of choosing relevant energy parameters and defining glass network connectivity, particularly in Fnet descriptors. It was found the Fe–O–P linkage density exhibits strong correlations to measured dissolution rates, supporting the importance of these linkages in improving the chemical durability in iron phosphate glasses.

Molecule Empowerment and Crystal Desensitization: A Multilevel Structure-Property Analysis toward Designing High-Energy Low-Sensitivity Layered Energetic Materials.

Layered energetic materials (LEMs) can effectively balance energy and mechanical sensitivity, making them a current research focus in the field of energetic materials. However, the influence of the layered stacking pattern on impact sensitivity is still unclear, leading to the lack of advanced design strategies for high-energy low-sensitivity LEMs. Herein, we first utilize novel indicators such as maximum plane separation and hydrogen bond dimension to perform high-throughput screening on over 106 candidate structures, resulting in 17 target crystals. A systematic analysis was then conducted on the relationships between the bond dissociation energy (BDE) of the weakest energy-storing bond at the molecular level, the intralayer hydrogen bond energy (HBE), and the sliding energy barrier (SEB) at the crystal level with impact sensitivity. The findings suggest that a material can have low sensitivity only if at least two of the three properties perform well, and the interlayer sliding resistance can be reduced by enhancing the intermolecular hydrogen bond interactions, which reasonably explains the experimental phenomena. More importantly, we developed a prediction model for the impact sensitivity of LEMs with a coefficient of determination of 0.88. Additionally, factors affecting HBE and SEB were identified, and a linear model was established based on molecular-level feature variables. Finally, a new strategy for designing high-energy low-sensitivity LEMs was proposed, namely, empowerment at the molecular scale and desensitization at the crystal scale. This study integrates high-throughput screening, multilevel structure-property relationship analysis, and mathematical model construction, offering new perspectives for the development of novel high-energy and low-sensitivity energetic materials.

Pharmacophore Identification and Structure-Activity Relationship Analysis of a Series of Substituted Azaindoles as Inhibitors of Trypanosoma brucei.

Human African trypanosomiasis is among the World Health Organization's designated neglected tropical diseases. Repurposing strategies are often employed in academic drug discovery programs due to financial limitations, and in this instance, we used human kinase inhibitor chemotypes to identify substituted 4-aminoazaindoles, exemplified by 1. Structure-activity and structure-property relationship analysis, informed by cheminformatics, identified 4s as a potent inhibitor of Trypanosoma brucei growth. While 4s appeared to be fast acting and cidal in the in vitro assays, it failed to cure a murine model of infection. Preliminary efforts to identify the potential mechanism of action of the series pointed to arginine kinase, though, as we demonstrate, this does not appear to be the sole target of our compounds. This comprehensive approach to drug discovery, encompassing cheminformatics, structure-potency and structure-property analysis, and pharmacophore identification, highlights our multipronged efforts to identify novel lead compounds for this deadly disease.

Statistical Evaluation of Cancer Drugs by QSPR Modeling

The prospect of discovering a cure for cancer has been present for the past two to three decades. Annually, this illness affects approximately 10 million individuals worldwide. Anticancer medications are pivotal in treating cancer and other malignant diseases. In this paper, the physical properties and chemical reactions associated with the anticancer medications of various topological indices (TIs) have been established. Additionally, we have discussed the degree-based TIs and their Quantitative Structure-Property Relationship (QSPR) analysis. In mathematical chemistry, molecular descriptors are essential, particularly for researchers investigating Quantitative Structure-Activity Relationship (QSAR) and QSPR models. The goal of the QSPR study is to establish a mathematical relationship between the properties under investigation (such as boiling point and flash point) and various descriptors related to the molecular structure of the drugs. Furthermore, we show the correlation with the physicochemical properties of anticancer medications.

Effect of Li2O on the structure and properties of low‐boron aluminosilicate fiber glasses from molecular dynamics simulations and quantitative structure–property relationship analysis

AbstractMixed alkaline earth (MgO, CaO) aluminosilicate glass fibers (MCAS) with and without boron are commonly used as reinforcements in plastic composites, and the fundamental understanding of the thermal and mechanical properties is essential to the design of new glass compositions to satisfy the growing demands in applications from renewable energy to lightweight structural components. In this work, a series of Li2O containing low‐boron MCAS fiber glasses have been studied by using molecular dynamic (MD) simulations with recently developed effective partial charge composition dependent boron potentials. Structural characteristics, such as pair distribution function, bond angle distribution, and neutron/X‐ray diffraction structure factors, were calculated, as well as properties such as elastic moduli and vibrational density of states. The addition of Li2O was found to improve the elastic moduli of the fiber glasses in excellent agreement with experimental results we reported earlier. The simulation results showed that the weakened network connectivity and decrease of tri‐/bridging oxygen have positively affected the lowering of liquid temperature, owing to the transformation to more boron Q2 and silicon/aluminon Q3. It is found that higher oxygen packing density, coordinated aluminum/boron species such as [AlO5] and [BO4] units, larger‐membered oxide rings, and intensified connections of [AlOx] and [SiO4] are the main reasons that lead to improved mechanical properties. MD‐based quantitative structure–property relationship analyses were performed and showed excellent correlations to measured properties, indicating that it is a promising approach to understand glass properties and design new glass compositions for functional applications.

Reverse Zagreb Indices and Their Application in the Evaluation of Physiochemical Properties of Anticancer/Antibacterial Drugs.

Recent advancements in chemical graph theory have facilitated a deeper understanding of the relationship between chemical structures and their underlying graphs based upon classical graph theory principles. Quantitative structure-property relationship (QSPR) analysis stands out as a powerful tool for probing chemical graphs. Topological indices, graph invariants assigning numerical values to graphs, play a pivotal role in statistically correlating physical properties, chemical reactivity, and biological activity across diverse chemical structures. The cactus graph (CG) represents a noteworthy family of connected graphs distinguished by the absence of common vertices between cycles. In this study, we focus on establishing the expressions of Zagreb and reverse Zagreb indices in terms of known parameters for cactus graphs. Subsequently, we leverage these indices to conduct QSPR analysis, employing linear and multilinear regression models to investigate the relationship between the properties of chemical structures adorned with cactus-like graphs and the corresponding Zagreb and reverse Zagreb indices.

Quantitative Structure–Property Relationship Analysis in Molecular Graphs of Some Anticancer Drugs with Temperature Indices Approach

The most important application of anticancer drugs in various forms (alkylating agents, hormones agents, and antimetabolites) is the treatment of malignant diseases. Topological indices are widely used in the field of chemical and medical sciences, especially in studying the chemical, biological, clinical, and therapeutic aspects of drugs. In this article, the temperature indices in anticancer drugs molecular graphs such as Carmustine, Convolutamine F, Raloxifene, Tambjamine K, and Pterocellin B were calculated and then analyzed based on physical and chemical properties. The analysis was performed by identifying the best regression models based on temperature indices for six physical and chemical features of anticancer drugs. The results indicated that temperature indices were essential topological indices that predict the properties of anticancer drugs, such as boiling point, flash point, enthalpy, molar refractivity, molar volume, and polarizability. It was also observed that the r value of the regression model was more than 0.6, and the p value was less than 0.05.

Analyzing polycyclic aromatic hydrocarbons using topological indices and QSPR analysis to reveal molecular complexity

Polycyclic aromatic hydrocarbons (PAHs) have distinctive chemical structures and are well known for their wide range of uses and environmental relevance. This work explores the impact of these structural characteristics on eccentricity-based topological indices offering information about the arrangement of atoms within the molecules. This study uses quantitative structure-property relationship (QSPR) analysis to construct prediction models for understanding and forecasting specific PAHs including Dibenzo[e,l]Pyrene, Heptacence, Heptaphene, Naphthacene, Naphthalene, Naphto[1,2a]pyrene, Naphtho[2,3a]pyrene, perylene, Perylene, Picene, Phenanthrene, Pyrene, Tetraphene, Tribenzo[b,n,pqr]perylene, Tribenzo[a,fg,op]tetracene and Triphenylene. Furthermore, regression analysis applies to clarify the quantitative correlations between the factors under study and improves the interpretability of the data produced. The combined use of these diverse approaches advances a thorough comprehension of the mutual influence of chemical structure, topological indices and predictive modeling about PAHs.

Structure-property modeling of coumarins and coumarin-related compounds in pharmacotherapy of cancer by employing graphical topological indices.

Coumarins, a subgroup of colorless and crystalline oxygenated heterocyclic compounds originally discovered in the plant Dipteryx odorata, were the subject of a recent study investigating their quantitative structure-activity relationship (QSAR) in cancer pharmacotherapy. This study utilized graph theoretical molecular descriptors, also known as topological indices, as a numerical representation method for the chemical structures embedded in molecular graphs. These descriptors, derived from molecular graphs, play a pivotal role in quantitative structure-property relationship (QSPR) analysis. In this paper, intercorrelation between the Balban index, connective eccentric index, eccentricity connectivity index, harmonic index, hyper Zagreb index, first path Zagreb index, second path Zagreb index, Randic index, sum connectivity index, graph energy and Laplacian energy is studied on the set of molecular graphs of coumarins. It is found that the pairs of degree-based indices are highly intercorrelated. The use of these molecular descriptors in structure-boiling point modeling was analyzed. Finally, the curve-linear regression between considered molecular descriptors with physicochemical properties of coumarins and coumarin-related compounds is obtained.

Exploring physico-chemical properties of HIV/AIDS drugs using neighborhood topological indices of molecular graphs

In this study, we investigate the efficacy of neighborhood-degree-based topological indices in the modeling of drug properties pertinent to HIV/AIDS. By representing molecular structures as graphs, we delve deep into atom-level environments, uncovering intricate relationships between local topological attributes and theoretical characteristics. Through meticulous quantitative structure–property relationship analysis, we establish robust correlations between these indices and drug properties. This breakthrough augurs predictive insights in the realm of pharmaceutical research, reducing the need for exhaustive experimentation. Our research underscores the pivotal role played by neighborhood-degree-based topological indices in advancing drug discovery, offering a powerful tool that resonates with chemists and industry professionals. It marks a transformative step in the trajectory of pharmaceutical development, promising to redefine and enhance the future of drug design and innovation.

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

AbstractTopological indices (TIs) can be used to forecast molecules' biological activity, physicochemical features, and toxicity. The use of topological indices and regression analysis can help us better understand drug behavior and contribute to the development of tailored drugs. This study investigates the predictive potential and efficacy of K‐Banhatti and Zagreb type degree‐based topological indices in quantitative structure–property relationship (QSPR) analysis of a comprehensive set of medications used for diabetes type‐I and type‐II disease. The K‐Banhatti and Zagreb type degree‐based topological indices are computed for 14 anti‐diabetes drug molecules using edge/vertex partitioning techniques. By leveraging these topological indices, QSPR regression models are developed to predict the physicochemical properties of the understudy drugs. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the anti‐diabetes drugs. Furthermore, the comparative analysis revealed that, for all the considered properties except enthalpy of vaporization, Zagreb type indices outperform K‐Banhatti indices with high predictive ability. Hence, it can be concluded that the Zagreb type indices are the best alternatives to theoretically predict the properties of anti‐diabetes drugs. This theoretical analysis can help chemists in their right choice of the topological indices to theoretically predict the properties of anti‐diabetes drugs without going into laborious experimentation.

Decolourization and degradation of azo-dyes by the NaClO/NiOOH catalytic oxidation system

ABSTRACT Azo dyes are highly toxic and difficult to degrade. Decolourization of acid red 18 (AR18) and reactive yellow 4 (RY4) dye wastewater by the NaClO/NiO x (OH) y catalytic system was studied. The influence of reaction conditions was investigated. Their degradation mechanism was discussed by free radical scavenging experiment, and the degradation pathways were proposed. The continuous flow experiment was conducted. The results showed that the catalytic oxidation has a good decolourization effect on AR18, but it is not ideal for RY4. The continuous flow experiment of 3000 min showed that the decolourization effect was stable. The concentration of Ni2+ in the effluent from the reactor was always up to the standard, and the catalyst was not deactivated. The decolourization mechanism of the two dyes is different: AR18 is mainly decolourized by atomic oxygen/singlet oxygen, while RY4 is mainly decolourized by NaClO. The catalysts were characterized by XPS and XRD. The surface contains the largest proportion of chemisorbed oxygen. With the extension of the catalyst service time, chemisorbed oxygen decreased slightly and was converted into atomic oxygen/singlet oxygen, which was consumed in the decolourization process. Compared with the used catalysts, the β-NiOOH crystallinity of the fresh catalyst is better. The quantitative structure–property relationship analysis based on the decolourization data shows that there is a good linear correlation between the decolourization rate of azo dyes and their molecular structure descriptors. The inorganic–organic properties balance (IOB) value was determined as the main molecular descriptor that affected the decolourization of dyes.

Design of Efficient Oxygen Reduction Reaction Catalysts with Single Transition Metal Atom on N-Doped Graphdiyne.

The revelation of the underlying structure-property relationship of single-atom catalysts (SACs) is a fundamental issue in the oxygen reduction reaction (ORR). Here we present systematic theoretical and experimental investigations of various N-doped graphdiyne (NGDY) supported transition metals (TMs) to shed light on this relationship. Calculation results indicate that the TMs' comprehensive activities follow the order of Pd@NGDY > Ni@NGDY > Co@NGDY > Fe@NGDY, which fits well with our experimental conclusion. Moreover, detailed structure-property relationship (194 in total) analysis suggests that the key-species binding stability (ΔG*OH), the d-orbital center (εd/εd-a) and charge transfer (ΔQTM/ΔQTM-a) of the active metal before/after reactants adsorption and the bond length of TM-O (LTM-O) as descriptors can well reflect the intermediate binding stability or ORR activity on different TM-SACs. Specifically, the change trend of catalytic activity is opposite to that of intermediate binding stability, meaning that too strongly bonded *OOH, *O, and *OH intermediates are unfavorable for ORR.