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Swarm Intelligence and Evolutionary Algorithms for Drug Design and Development
Published in Sandeep Kumar, Anand Nayyar, Anand Paul, Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development, 2019
Quantitative structure activity relationship (QSAR): the QSAR model as discussed earlier are basically the regression and the classification models that can be used in chemical and biological science and engineering. The QSAR regression models can be related to a group of “predictors” termed as variable “X” to the potency of the response variable “Y”, during the process of classifying QSAR model it relates the predictor variables to a categorical value of the response variable.
Alternative Methodologies to Animal Testing
Published in Nicola Loprieno, Alternative Methodologies for the Safety Evaluation of Chemicals in the Cosmetic Industry, 2019
Usually, this relates to partition coefficient for predicting percutaneous absorption, and possibly, chemical structure. Validated quantitative structure-activity relationship (QSAR) data also can be used at this stage.
A Practical Process for Assessing the Validity of Alternative Methods for Toxicity Testing
Published in Francis N. Marzulli, Howard I. Maibach, Dermatotoxicology Methods: The Laboratory Worker’s Vade Mecum, 2019
Leon H. Bruner, Gregory J. Carr, Mark Chamberlain, Rodger D. Curren
The concept of a training set, used for the development and optimization of alternative methods, and of a separate test set for validation is well known to quantitative structure-activity relationship (QSAR) researchers. Prior to the development of a new QSAR model, it is usual to divide the set of substances into two sets training and test on a random basis. The QSAR model is developed and optimized using relevant chemical parameters/descriptors associated with the materials in the training set. Then the robustness of the QSAR is established by evaluating the model using the test set. Although this concept has not made significant impact hitherto on the validation of alternative methods, the same logic should apply to the development and validation of an alternative method. However, experience has demonstrated that it is difficult to obtain sets of substances sufficiently large to apply this methodology widely (see Bagley et al., 1992b; Barratt, 1995a, 1995b). Illustrations of how QSAR may aid selection of an RSTS have been provided by Barratt et al. (1995a) and Chamberlain and Barratt (1995).
DFT based QSAR study on quinolone-triazole derivatives as antibacterial agents
Published in Journal of Receptors and Signal Transduction, 2022
Niloofar Ghasedi, Shahin Ahmadi, Sepideh Ketabi, Ali Almasirad
Computational methods have been applied for interpreting and predicting many biomedical phenomena such as ion channels [12], new drug delivery systems[13–15], and drug design [16,17]. Quantitative structure-activity relationship (QSAR) is one of the computational methods used to design new drugs and accurately predict the biological activity of ligands [18]. As an important area of chemometrics, this method has been the subject of several studies and widely used in toxicology and drug design in recent years [19–27]. Due to the expensive and time-consuming process of synthesis, the structure-activity relationship study via theoretical, computational chemistry methods provides a guide for researchers to focus on the synthesis of candidate compounds with promising higher activities [28,29].
Prediction of cytotoxic activity of a series of 1H-pyrrolo[2,3-b]pyridine derivatives as possible inhibitors of c-Met using molecular fingerprints
Published in Journal of Receptors and Signal Transduction, 2019
Tahereh Damghani, Korosh Mashayekh, Somayeh Pirhadi, Omidreza Firuzi, Shahrzad Sharifi, Najmeh Edraki, Mehdi Khoshneviszadeh, Mohammad Sadegh Avestan
Quantitative structure activity relationship (QSAR) is a computational method with the specific aim to predict biological activity via a mathematical model built from a set of experimentally tested compounds. For this purpose, a set of molecular descriptors are generated from molecular structures, which can be ascribed to the biological activity by a QSAR model. Unlike 3D-QSAR methods, a drawback of 2D-QSAR methods is that they barely can guide the synthesis process as they use uninterpretable descriptors not based on the molecular skeleton [15]. Molecular fingerprints are among molecular descriptors that encode structural information in a binary bit string. Dictionary-based fingerprints represent the presence or absence of particular fragments in the structure; hence, they not only can predict the biological activity, but they can also lead to the design of new molecules.
Deciphering the absorption profile and interaction of multi-components of Zhi-Zi-Da-Huang decoction based on in vitro–in silico–in vivo integrated strategy
Published in Xenobiotica, 2019
Qing Hu, Xixi Li, Qingshui Shi, Gongjun Yang, Fang Feng
Over the recent years, several ex vivo and in silico methods have been extensively applied to evaluate the intestinal absorption of TCMs, excluding compounds with poor absorption properties and unravelling the global chemome that possess the potential of being an active molecular entity (Liu et al., 2013). To our knowledge, rat everted gut sac technique, an efficient and comparatively controllable model, has been commonly used for in vitro investigation of intestinal absorption (Liu et al., 2017; Ma et al., 2012). Octanol–water partition experiment is said to conveniently provide essential information about drug permeability, which is the critical factor for drugs to transport across the intestinal barrier (Korinth et al., 2012). In addition, in silico screening based on the quantitative structure-activity relationship (QSAR) is also deemed as a valuable and fast avenue for characterization of drug absorption, possessing the advantages of resource- and time-saving (Han et al., 2014). However, both in vitro models and in silico predictions have their respective advantages and drawbacks. A bias prediction of intestinal absorption might be obtained sometimes when investigations were carried out with one experimental model solely (Luo et al., 2013). Therefore, the integration of various approaches could be taken into consideration to promote the reliability and accuracy of the analysis.