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Macromolecular Architecture and Molecular Modelling of Dendrimers
Published in Neelesh Kumar Mehra, Keerti Jain, Dendrimers in Nanomedicine, 2021
Rahul Gauro, Keerti Jain, Vineet Kumar Jain, Neelesh Kumar Mehra, Harvinder Popli
QSAR is a method used to measure the interaction between structural and biological properties. The most general form of QSAR can be expressed by the following equation: biological activity = f (physicochemical and/or structural parameters). Physicochemical descriptors include parameters that account for topology, electronic properties, hydrophobicity and steric effects, which are empirically calculated using analytical methods. The procedure used in QSAR includes chemical analyses and biological experiments. Researchers have been trying to create drugs based on QSAR for several years. The example of QSAR in modelling is the 1-(X-phenyl)-3,3-dialkyl triazene sequence. These compounds are of concern for their anti-tumour activity, but they are also mutagenic. QSAR is used to explain how the structure can be adjusted to minimise mutagenicity without drastically reducing the anti-tumour function. In a quantitative activity relationship analysis, the anti-leishmanial activity of the substituted pyrimidine and pyrazolo pyrimidine analogues was calculated using physicochemical and steric descriptions (hydrophobicity, molar refractivity, Supton resonance, Verloop steric parameters and van der Waals volumes of the substituent groups) of the different substituents. The study of pyrimidine analogues suggested the need for unsubstituted pyrimidine for anti-leishmanial action. Linear multiple regression analysis using the least squares method was used to establish a correlation (Ghasemi et al. 2018).
In Silico Toxicology Approach for Environment Safety
Published in Pankaj Chowdhary, Abhay Raj, Contaminants and Clean Technologies, 2020
Anil Kumar Singh, Pankaj Chowdhary, Abhay Raj
SAR and QSAR models for predicting toxicological effects of compounds or pollutants are widely used in various fields of science due to their physicochemical or structurally distinctive features (Devillers, 2013). However, the development of a model for toxicity prediction has many obstacles and difficulties in terms of complexity for new researchers of various disciplines. QSAR is the most common method and is designed to study the dependency of biological, toxicological, or distinct effects of chemical compounds or pollutants on their molecular properties (Rogers, Hopfinger, & Sciences, 1994). In the last two decades, QSAR modeling has been widely used in different areas of science. A number of efforts have been made toward accurate and authentic toxicity prediction, and advanced model systems have been developed to address the current flaws/issues.
Modeling biofiltration
Published in Joseph S. Devinny, Marc A. Deshusses, Todd S. Webster, Biofiltration for Air Pollution Control, 2017
Joseph S. Devinny, Marc A. Deshusses, Todd S. Webster
Recently, there has been a renewed interest in biofilter modeling with the development of quantitative structure activity relationships (QSARs) (Choi et al., 1996; Devinny et al., 1997; Govind et al., 1997; Johnson and Deshusses, 1997). QSAR models seek to describe the activity of particular chemicals based on their chemical structure. QSARs have been widely applied in toxicology and more recently in the field of wastewater treatment (Govind et al., 1991; Boethling et al., 1994; Okey and Stensel, 1996). The application to biological waste air treatment is new and shows promise for biofilter design. QSAR models are quite different and in some ways much more limited than the conceptual models. Because the only data used are those that describe the compound, QSAR models obviously cannot describe all aspects of biofiltration. What can be expected of QSAR models is that they will describe the relative treatability of various compounds for a set of conditions. Thus, where there is experience with a set of compounds in a biofilter, it should be possible to predict the performance of that biofilter or a similar biofilter on other compounds.
Current applications and future impact of machine learning in emerging contaminants: A review
Published in Critical Reviews in Environmental Science and Technology, 2023
Lang Lei, Ruirui Pang, Zhibang Han, Dong Wu, Bing Xie, Yinglong Su
Undertaking risk assessments presents a daunting challenge, given the intricate and multifaceted nature of the available data on nanoparticle toxicity. Meta-analysis was capable of amalgamating findings from multiple studies on a given topic, thereby unveiling concealed information (Delgado-Rodríguez & Sillero-Arenas, 2018). It is expected that meta-analyses aided by ML will resolve the complexity of heterogeneous data. In fact, a RF model based on random permutations was developed to accurately assess the impact of macroscopic properties on toxicity of graphene (Ma et al., 2021). Similarly, Ban et al. linked numerous physicochemical properties of nanoparticles and different experimental conditions, quantifying protein corona components such as hydrophilicity and functionality, as well as the biological effects of NPs through the application of a meta-analysis based on the RF model (Ban et al., 2020). ML methods provide guidance for NPs synthesis, such as the proper design of active targeting nano-carriers, and prevent unexpected biological outcomes, such as cellular uptake and immune response. Limited data volume and high heterogeneity are significant limitations hindering the accurate prediction of traditional models, such as QSAR. However, ML models, such as RF, have achieved outstanding prediction accuracy and robustness with respect to heterogeneous big data containing both quantitative and qualitative factors (Ban et al., 2020). Furthermore, RF can determine the importance of characteristic variables and provide transparent recommendations for decision-making.
Genetic functional algorithm model, docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis
Published in Egyptian Journal of Basic and Applied Sciences, 2020
The first stage for the design and synthesis of novel hypothetical compounds with enhanced anti-tubercular activity and less toxicity/side effect as to with the approaches and methods that will consider the rate of experimental runs and time factor. Reference to the design of novel drug candidate, computer-aided drug design has demonstrated a crucial part for the discovery of new molecules in pharmaceutical design, drug metabolism, and medicinal chemistry [13]. This approach had facilitated the improvement in the course of optimization of chemical structures with well-defined purposes [14]. Quantitative structure-activity relationship study (QSAR) and molecular docking are one of the computer-aided drug design approaches which had been broadly utilized in the design, improvement and synthesis of first-hand drug [2]. QSAR investigation had shown to be an expedient technique for forecasting biological/inhibition activities, properties of any chemical compound by making use of an experimental data and molecular descriptors. This idea is based on the correlation between the information derived from any chemical space or structural molecule illustrated by the descriptor and well-defined experimental data provided. Meanwhile, molecular docking technique help to foresee the binding location and affinity of the existing interaction between the molecule (ligand) and the target. Thereby providing an idea to design prospective drug with better activity against the target [2]. Therefore, the study aimed to build a Genetic Functional Algorithm model, carry out molecular docking studies and in silico design of novel proposed compounds against Mycobacterium tuberculosis
Computation of some important degree-based topological indices for γ- graphyne and Zigzag graphyne nanoribbon
Published in Molecular Physics, 2023
Abdul Hakeem, Asad Ullah, Shahid Zaman
Moreover, it is utilised to investigate the characteristics of molecules in biology, physics, and chemistry. A proper description of molecular graphs is the first step in the process, which concludes with a prediction about the Nature of chemical compounds in the studied biological and physicochemical systems. This approach is known as quantitative structure–activity relationship (QSAR). A chemical compound's physicochemical parameters or physical activities, such as strain energy, boiling point, and heat of formation, are closely related to the chemical structure's molecular graph corresponding to that compound. Chemical graph theory is a branch of mathematical chemistry that connects mathematics, chemistry, and graph theory and solves mathematical problems in chemistry [1–6]. Topological indices are a type of mathematical tool used in chemistry to analyse the structure of molecules. Polynomials and integers are key in mathematics chemistry to regulate the properties of elements without using quantum physics. Together, these methods provide information that molecular graph symmetry conceals. Among these invariants, degree-based topological indices are recognised activities [7–9], and their numeric values are linked with the structure of different physical attributes, chemical reactivity, and biological characteristics [10]. The topological indices can be applied to drug design, molecular QSPR/QSAR analysis, thermodynamics, and other fields. For instance, it has been demonstrated that there is a significant degree of adjustment between the boiling point, the heat of production, and the atom and bond connectivity index classes of isomeric octanes [10,11].