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Academic literature on the topic 'QSAR Model'

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Published: 18 May 2024

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Journal articles on the topic "QSAR Model"

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Li, Yan Kun, and Xiao Ying Ma. "QSAR/QSPR Model Research of Complicated Samples." Advanced Materials Research 740 (August 2013): 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.740.306.

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QSAR/QSPR study is a hot issue in present chemical informatics research, and is the very active research domain. In present, a large number of QSAR/QSPR (quantitative structure-activity/property relationships) models have been widely studied and applied in a lot of different areas. This paper overviews the developments, research methods and applications of QSAR/QSPR model.
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Okey, Robert W., and H. David Stensel. "A QSAR-based biodegradability model—A QSBR." Water Research 30, no. 9 (September 1996): 2206–14. http://dx.doi.org/10.1016/0043-1354(96)00098-x.

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Zhang, Xiujun, H. G. Govardhana Reddy, Arcot Usha, M. C. Shanmukha, Mohammad Reza Farahani, and Mehdi Alaeiyan. "A study on anti-malaria drugs using degree-based topological indices through QSPR analysis." Mathematical Biosciences and Engineering 20, no. 2 (2022): 3594–609. http://dx.doi.org/10.3934/mbe.2023167.

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<abstract> <p>The use of topological descriptors is the key method, regardless of great advances taking place in the field of drug design. Descriptors portray the chemical characteristic of a molecule in numerical form, that is used for QSAR/QSPR models. The numerical values related with chemical constitutions that correlates the chemical structure with the physical properties referto topological indices. The study of chemical structure with chemical reactivity or biological activity is termed as quantitative structure activity relationship, in which topological index play a significant role. Chemical graph theory is one such significant branches of science which play a key role in QSAR/QSPR/QSTR studies. This work is focused on computing various degree-based topological indices and regression model of nine anti-malaria drugs. Regression models are fitted for computed indices values with 6 physicochemical properties of the anti-malaria drugs are studied. Based on the results obtained, an analysis is carried out for various statistical parameters for which conclusions are drawn.</p> </abstract>
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Toropov, Andrey A., and Alla P. Toropova. "The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR." Current Computer-Aided Drug Design 16, no. 3 (June 2, 2020): 197–206. http://dx.doi.org/10.2174/1573409915666190328123112.

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Background: The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Methods: Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. Results: The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. Conclusion: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.
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Mudasir, Mudasir, Iqmal Tahir, and Ida Puji Astuti Maryono Putri. "QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP ANALYSIS OF 1,2,4-THIADIAZOLINE FUNGICIDES BASED ON MOLECULAR STRUCTURE CALCULATED BY AM1 METHOD." Indonesian Journal of Chemistry 3, no. 1 (June 7, 2010): 39–47. http://dx.doi.org/10.22146/ijc.21904.

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Quantitative structure-Activity relationship (QSAR) analysis of fungicides having 1,2,4-thiadiazoline structure based on theoretical molecular properties have been done. Calculation of the properties was conducted by semiempirical method AM1 and the activity of the compounds was taken from literature. Relationship analysis between fungicides activity (pEC50) and molecular properties was done using SPSS program. The QSAR analysis gave the best model as follows: pEC50 = 3.842 + (1.807x10-4) ET + (5.841x10-3) Eb - (5.689x10-2) DHf -0.770 log P + 1.144 a - 0.671 m + 9.568 GLOB - (5.54x10-2) MR. n=19 r=0.917 SE=0.216 Fcal/Ftable=2.459 PRESS=0.469. The best model obtained was then used to design and predict the fungicides activity of new compounds derived from 1,2,4-thiadiazoline. Keywords: QSAR, QSPR, fungicide, molecular structure, 1,2,4-thiadiazoline
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Sarkar, Bikash Kumar. "DFT Based QSAR Studies of Phenyl Triazolinones of Protoporphyrinogen Oxidase Inhibitors." Asian Journal of Organic & Medicinal Chemistry 5, no. 4 (December 31, 2020): 307–11. http://dx.doi.org/10.14233/ajomc.2020.ajomc-p280.

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The quantitative structure activity relationships (QSARs) have been investigated on a series of substituted phenyl triazolinones having protoporphyrinogen oxidase (PPO) inhibition activities. The density functional theory (DFT) method is applied to calculate the quantum chemical descriptors. The derived QSAR model is based on three molecular descriptors namely highest occupied molecular orbital (HOMO) energy, electrophilic group frontier electron density (Fg E) and nucleus independent chemical shift (NICS). The best QSAR model has a square correlation coefficient r2 =0.886 and cross-validated square correlation coefficient q2 = 0.837.
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Rybińska-Fryca, Anna, Anita Sosnowska, and Tomasz Puzyn. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids." Materials 13, no. 11 (May 30, 2020): 2500. http://dx.doi.org/10.3390/ma13112500.

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The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model’s quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors’ selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.
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Pokle, Maithili S., Rashmi D. Singh, and Madhura P. Vaidya. "2D QSAR MODEL BASED ON 1,2-DISUBSTITUTED BENZIMIDAZOLES IMPDH INHIBITORS." Indian Drugs 59, no. 04 (June 1, 2022): 18–23. http://dx.doi.org/10.53879/id.59.04.13117.

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Quantitative structure activity relationship (QSAR) analysis of 1, 2-disubstituted benzimidazoles IMPDH inhibitors was studied for their antibacterial activity. The 2D QSAR model was developed using molecular suite (VLife MDS 4.3.1) on a set of 38 molecules. Multiple Linear Regression (MLR) was implemented for building a robust 2D QSAR model with various variable selection methods. The generated QSAR model emphasized that electronic, spatial, lipophilic and structural parameters play an important role in binding of benzimidazole derivatives to the receptor and thus in turn facilitates the further optimization of novel IMPDH inhibitors before synthesizing.
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Bu, Qingwei, Qingshan Li, Yun Liu, and Chun Cai. "Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?" Journal of Chemistry 2021 (October 31, 2021): 1–8. http://dx.doi.org/10.1155/2021/5563066.

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Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.
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LIAO, SI YAN, LI QIAN, JIN CAN CHEN, YONG SHEN, and KANG CHENG ZHENG. "2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY." Journal of Theoretical and Computational Chemistry 07, no. 02 (April 2008): 287–301. http://dx.doi.org/10.1142/s0219633608003745.

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Two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 23 analogs of 2-Methoxyestradiol with anticancer activity (expressed as p GI50) against MCF-7 human breast cancer cells have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) for 3D. The established 2D-QSAR model in training set shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2= 0.779). The same model was further applied to predict p GI50values of the four compounds in the test set, and the resulting [Formula: see text] being as high as 0.827, further confirms that this 2D-QSAR model has high predictive ability for this kind of compound. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2(0.927) and q2(0.786) obtained from CoMFA model. The results that 2D- and 3D-QSAR analyses accord with each other, suggest that the electrostatic interaction plays a decisive role in determining the anticancer activity of the studied compounds, and that increasing the negative charge of substituent R2and the positive charge of substituents linking to C17as well as decreasing the size of substituent R1are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with anticancer activity.
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Dissertations / Theses on the topic "QSAR Model"

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Spreafico, Morena. "Mixed-model QSAR at the glucocorticoid and liver X receptors /." [S.l.] : [s.n.], 2009. http://edoc.unibas.ch/diss/DissB_8730.

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Bagchi, Bhaskar. "Quantum chemical calculation and structure activity relationship of bioactive terpenoids." Thesis, University of North Bengal, 2016. http://ir.nbu.ac.in/handle/123456789/2762.

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Raynaud, Isabelle. "Etude des relations structure-activité quantitatives (QSAR) des cytokinines : synthèse et activité biologique de nouvelles molécules actives." Angers, 1996. http://www.theses.fr/1996ANGE0022.

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Un modèle statistique de relations structure-activité de type quantitatif des cytokinines, hormones végétales, a été mis au point. Il a été construit à partir de 26 molécules appartenant aux deux principales classes de cytokinines : les pyridylphenylurées et les purines n#6-substituées. Apres l'analyse conformationnelle de ces 26 molécules, différents alignements ont été construits et testes dans COMFA. Finalement, deux modèles ont été sélectionnés. Ils ont été valides par des échantillons-tests de molécules connues. Le meilleur modèle, ORL1, a ensuite servi pour faire des prédictions d'activité sur des molécules hypothétiques. Ces molécules ont par la suite été synthétisées et leur activité biologique a été déterminée. Il s'agit d'une guanidine prédite active mais qui s'est révélée instable après des examens complémentaires. Deux pyridylphenylurées portant un groupement sulfinyl ou sulfonyl en 6 sur la pyridine se sont révélées actives, comme le prédisait le modèle. D'autre part, deux pyridylbenzylurées, analogues des deux molécules précédentes, prédites actives par le modèle, possèdent en fait un effet inhibiteur. Le modèle choisi (ORL1) semble fiable pour la prédiction de molécules rigides telles que les pyridylphenylurées ou les styrylpurines.
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Mazzatorta, Paolo. "Evaluation of pesticide toxicity : a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424819.

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The thesis aimed to extract information relevant to the hazard and risk assessment of pesticides. In particular, quantitative structure-activity relationship (QSAR) approaches have been used to build up a mathematical model able to predict the aquatic acute toxicity, Leso, and the avian oral toxicity, LDso, for pesticides. Ecotoxicological values were collected from several databases, and screened according to quality criteria. A hierarchical QSAR approach was applied for the prediction of acute aquatic toxicity. Chemical structures were encoded into molecular descriptors by an automated, seamless procedure available within the OpenMolGRID system. Different linear and non-linear regression techniques were used to obtain reliable and thoroughly validated QSARs. The final model was developed by a counter-propagation neural network coupled with genetic algorithms for variable selection. The proposed QSAR is consistent with McFarland's principle for biological activity and makes use of seven molecular descriptors. The model was assessed thoroughly in test (R2 = 0.8) and validation sets (R2 = 0.72), the y-scrambling test and a sensitivity/stability test. The second endpoint considered in this thesis was avian oral toxicity. As previously, the chemical description of chemicals was generated automatically by the OpenMolGRID system. The best classification model was chosen on the basis of the performances on a validation set of 19 data points, and was obtained from a support vector machine using 94 data points and nine variables selected by genetic algorithms (Error Ratetraining = 0.021, Error Ratevaiidalion = 0.158). The model allowed for a mechanistic estimation of the toxicological action. In fact, several descriptors selected for the final classification model encode for the interaction of the pesticides with other molecules. The presence of hetero-atoms, e.g. sulphur atoms, is correlated with the toxicity, and the pool of descriptor selected is generally dependent from the 3D conformation of the structures. These suggest that, in the case of avian oral toxicity, pesticides probably exert their toxic action through the interaction with some macromolecule and/or protein of the biological system.
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Malazizi, Ladan. "Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation." Thesis, University of Bradford, 2008. http://hdl.handle.net/10454/4262.

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Toxic compounds, such as pesticides, are routinely tested against a range of aquatic, avian and mammalian species as part of the registration process. The need for reducing dependence on animal testing has led to an increasing interest in alternative methods such as in silico modelling. The QSAR (Quantitative Structure Activity Relationship)-based models are already in use for predicting physicochemical properties, environmental fate, eco-toxicological effects, and specific biological endpoints for a wide range of chemicals. Data plays an important role in modelling QSARs and also in result analysis for toxicity testing processes. This research addresses number of issues in predictive toxicology. One issue is the problem of data quality. Although large amount of toxicity data is available from online sources, this data may contain some unreliable samples and may be defined as of low quality. Its presentation also might not be consistent throughout different sources and that makes the access, interpretation and comparison of the information difficult. To address this issue we started with detailed investigation and experimental work on DEMETRA data. The DEMETRA datasets have been produced by the EC-funded project DEMETRA. Based on the investigation, experiments and the results obtained, the author identified a number of data quality criteria in order to provide a solution for data evaluation in toxicology domain. An algorithm has also been proposed to assess data quality before modelling. Another issue considered in the thesis was the missing values in datasets for toxicology domain. Least Square Method for a paired dataset and Serial Correlation for single version dataset provided the solution for the problem in two different situations. A procedural algorithm using these two methods has been proposed in order to overcome the problem of missing values. Another issue we paid attention to in this thesis was modelling of multi-class data sets in which the severe imbalance class samples distribution exists. The imbalanced data affect the performance of classifiers during the classification process. We have shown that as long as we understand how class members are constructed in dimensional space in each cluster we can reform the distribution and provide more knowledge domain for the classifier.
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Moda, Tiago Luiz. "Desenvolvimento de modelos in silico de propriedades de ADME para a triagem de novos candidatos a fármacos." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-22032007-112055/.

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As ferramentas de modelagem molecular e de estudos das relações quantitativas entre a estrutura e atividade (QSAR) ou estrutura e propriedade (QSPR) estão integradas ao processo de planejamento de fármacos, sendo de extremo valor na busca por novas moléculas bioativas com propriedades farmacocinéticas e farmacodinâmicas otimizadas. O trabalho em Química Medicinal realizado nesta dissertação de mestrado teve como objetivo estudar as relações quantitativas entre a estrutura e as propriedades farmacocinéticas biodisponibilidade oral e ligação às proteínas plasmáticas. Para a realização deste trabalho, conjuntos padrões de dados foram organizados para as propriedades biodisponibilidade e ligação às proteínas plasmáticas contendo a informação qualificada sobre a estrutura química e a propriedade alvo correspondente. Os conjuntos de dados criados formaram as bases científicas para o desenvolvimento dos modelos preditivos empregando os métodos holograma QSAR e VolSurf. Os modelos finais de HQSAR e VolSurf gerados neste trabalho possuem elevada consistência interna e externa, apresentando bom poder de correlação e predição das propriedades alvo. Devido à simplicidade, robustez e consistência, estes modelos são guias úteis em Química Medicinal nos estágios iniciais do processo de descoberta e desenvolvimento de fármacos.
Molecular modeling tools and quantitative structure-activity relantionships (QSAR) or structure-property (QSPR) are integrated into the drug design process in the search for new bioactive molecules with good pharmacokinetic and pharmacodynamic properties. The Medicinal Chemistry work carried out in this Master’s dissertation concerned studies of the quantitative relationshisps between chemical structure and the pharmacokinetic properties oral bioavailability and plasma protein binding. In the present work, standard data sets for bioavailability and plasma protein binding were organized encompassing the structural information and corresponding pharmacokinetic data. The created data sets established the scientific basis for the development of predictive models using the hologram QSAR and VolSurf methods. The final HQSAR and VolSurf models posses high internal and external consistency with good correlative and predictive power. Due to the simplicity, robustness and effectivess, these models are useful guides in Medicinal Chemistry in the early stages of the drug discovery and development process.
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MANSOURI, KAMEL. "New molecular descriptors for estimating degradation and fate of organic pollutants by QSAR/QSPR models within reach." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/45611.

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Organic pollutants that resist degradation in the environment can accumulate in body tissues and cause unavoidable intoxications to organisms in wild life as well as humans. The possible effects, usually increasing with the cumulative exposure to such chemicals, are not always addressed adequately in risk assessment procedures evaluating long and short-term contact hazard. Thus, chemicals accumulation, degradation and environmental fate are of prime concern for REACH when defining side effects due to chronic exposure. Characteristics and behavior of organic pollutants have been investigated experimentally during the last decades by use of various methods of trace analysis. However, the available data still contains several gaps. In this aim, REACH promotes the use of alternative methods to reduce the number of animal tests and suggests in-silico methods such as Quantitative Structure-Activity Relationships (QSARs) to fill the lack of knowledge. The goal of this thesis, in the framework of the ECO-ITN project, was to build QSAR models with high reliability based on good experimental data for optimal estimation of environmental endpoints of interest for REACH. New molecular descriptors and feature selection techniques have been tested paying particular attention to the validation steps and applicability domain definition.
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Dimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.

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With the recent advantages of machine learning in cheminformatics, the drug discovery process has been accelerated; providing a high impact in the field of medicine and public health. Molecular property and activity prediction are key elements in the early stages of drug discovery by helping prioritize the experiments and reduce the experimental work. In this thesis, a novel approach for multi-task regression using a text-based Transformer model is introduced and thoroughly explored for training on a number of properties or activities simultaneously. This multi-task regression with Transformer based model is inspired by the field of Natural Language Processing (NLP) which uses prefix tokens to distinguish between each task. In order to investigate our architecture two data categories are used; 133 biological activities from ExCAPE database and three physical chemistry properties from MoleculeNet benchmark datasets. The Transformer model consists of the embedding layer with positional encoding, a number of encoder layers, and a Feedforward Neural Network (FNN) to turn it into a regression problem. The molecules are represented as a string of characters using the Simplified Molecular-Input Line-Entry System (SMILES) which is a ’chemistry language’ with its own syntax. In addition, the effect of Transfer Learning is explored by experimenting with two pretrained Transformer models, pretrained on 1.5 million and on 100 million molecules. The text-base Transformer models are compared with a feature-based Support Vector Regression (SVR) with the Tanimoto kernel where the input molecules are encoded as Extended Connectivity Fingerprint (ECFP), which are calculated features. The results have shown that Transfer Learning is crucial for improving the performance on both property and activity predictions. On bioactivity tasks, the larger pretrained Transformer on 100 million molecules achieved comparable performance to the feature-based SVR model; however, overall SVR performed better on the majority of the bioactivity tasks. On the other hand, on physicochemistry property tasks, the larger pretrained Transformer outperformed SVR on all three tasks. Concluding, the multi-task regression architecture with the prefix token had comparable performance with the traditional feature-based approach on predicting different molecular properties or activities. Lastly, using the larger pretrained models trained on a wide chemical space can play a key role in improving the performance of Transformer models on these tasks.
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Sköld, Christian. "Computational Modeling of the AT2 Receptor and AT2 Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models." Doctoral thesis, Uppsala University, Organic Pharmaceutical Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7823.

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Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT2 receptor.

The bioactive conformation of a peptide can provide important guidance in peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan.

To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor.

By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.

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Sköld, Christian. "Computational Modeling of the AT2 Receptor and AT2 Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models." Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7823.

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Rational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT2 receptor. The bioactive conformation of a peptide can provide important guidance in peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan. To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor. By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.
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Books on the topic "QSAR Model"

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Romualdo, Benigni, ed. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, Fla: CRC Press, 2003.

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name, No. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, FL: CRC Press, 2002.

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González-Díaz, Humberto. Alignment-free models in plant genomics: Theoretical, experimental and legal issues. New York: Nova Science, 2010.

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Knaak, James B., Charles Timchalk, and Rogelio Tornero-Velez, eds. Parameters for Pesticide QSAR and PBPK/PD Models for Human Risk Assessment. Washington, DC: American Chemical Society, 2012. http://dx.doi.org/10.1021/bk-2012-1099.

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Knaak, James B., Charles Timchalk, and Rogelio Tornero-Velez. Parameters for pesticide QSAR and PBPK/PD models for human risk assessment. Edited by American Chemical Society and American Chemical Society. Division of Agrochemicals. Washington, DC: American Chemical Society, 2012.

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Prakash, Gupta Satya, and Bahal R, eds. QSAR and molecular modeling studies in heterocyclic drugs. Berlin: Springer-Verlag, 2006.

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author, Panaye Annick, ed. Three dimensional QSAR: Applications in pharmacology and toxicology. Boca Raton: CRC Press, 2010.

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1944-, Truhlar Donald G., ed. Rational drug design. New York: Springer, 1999.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Boca Raton, FL: Taylor & Francis, 2010.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Boca Raton: CRC Press/Taylor & Francis, 2010.

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Book chapters on the topic "QSAR Model"

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Johansson, Erik, Lennart Eriksson, Maria Sandberg, and Svante Wold. "QSAR Model Validation." In Molecular Modeling and Prediction of Bioactivity, 271–72. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_36.

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Grunewald, Gary L., Niels Skjaerbaek, and James A. Monn. "An active site model of phenylethanolamine N-methyltransferase using CoMFA." In Trends in QSAR and Molecular Modelling 92, 513–16. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_138.

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Sanz, F., E. López de Briñas, J. Rodríguez, and F. Manaut. "Theoretical model for the metabolism of caffeine and its inhibition." In Trends in QSAR and Molecular Modelling 92, 193–96. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_29.

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Vrontaki, Eleni, and Antonios Kolocouris. "Pharmacophore Generation and 3D-QSAR Model Development Using PHASE." In Methods in Molecular Biology, 387–401. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8630-9_23.

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Rothe, H., and S. Boudon. "An approach to knowledge-based QSAR predictions using the MASCA model." In Trends in QSAR and Molecular Modelling 92, 502–3. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_134.

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Opreaa, Tudor Ionel, Ludovic Kurunczi, and Eduard Eli Moret. "Role of the dipole moment during ligand receptor interaction: A hypothetic static model." In Trends in QSAR and Molecular Modelling 92, 398–99. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_92.

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Herbette, Leo G. "A structural model for drug interactions with biological membranes: Hydrophobicity, hydrophilicity and amphiphilicity in drug structures." In Trends in QSAR and Molecular Modelling 92, 76–85. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_10.

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Blankley, C. John, and Andrew D. White. "Lipophilic and electronic factors influencing the activity of a series of urea ACAT inhibitors: Approaches to model specification." In Trends in QSAR and Molecular Modelling 92, 349–51. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_73.

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Kemmritz, Kerstin, and Hans-Dieter Höltje. "Theoretical investigations on the interaction of non-steroidal antiphlogistics with a model of the active site of the human prostaglandin endoperoxide synthase (‘cyclooxygenase’)." In Trends in QSAR and Molecular Modelling 92, 476–77. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_124.

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Golani, Mati, and Idit I. Golani. "Neural Network Ensemble Based QSAR Model for the BBB Challenge: A Review." In Transactions on Engineering Technologies, 55–68. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7236-5_5.

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Conference papers on the topic "QSAR Model"

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Boboriko, Natalia, He Liying, and Yaraslau Dzichenka. "THE EXPLORATION OF CYP17A1 LIGAND SPACE BY THE QSAR MODEL." In 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.439b.

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Cytochrome P450 17A1 (CYP17A1) is a critically important enzyme in humans that catalyzes the formation of all endogenous androgens. This enzyme is often considered a molecular target for the development of novel high efficient drugs against prostate cancer. In the present work, the random forest algorithm was used to conduct a QSAR study on 370 CYP17A1 ligands with different structures that were collected from the literature and databases, and a QSAR model was created based on the five important descriptors screened out – 2D adjacency and distance matrix descriptors, 2D atom counts and bond counts and 3D surface area, volume and shape descriptors. The model was verified by the test set (accuracy, specificity, sensitivity, F-measure, MCC, and AUC were calculated). It was revealed that the hydrophobic properties of the vdW surface of the ligand have a significant contribution to the activity prediction. The hydrophobic effect of the molecules may be aroused by the presence of the hydrophobic groups or aromatic rings in the molecules. The created QSAR model shows that the molecules with more aromatic rings have better activity. The accuracy of the model on the test set was 84%, precision – 81%, sensitivity – 93%, specificity – 72%, F-measure – 0.87, MCC – 0.67, AUC – 0.88. The model has good robustness and predictive ability and can be used to screen and discover new highly active CYP17A1 inhibitors.
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Munjal, Nupur S., Narendra Kumar, Manu Sharma, and Chittaranjan Rout. "QSAR model development for solubility prediction of Paclitaxel." In 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552139.

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Douali, Latifa. "QSAR model of phenols generated by deep neural network." In 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2020. http://dx.doi.org/10.1109/iraset48871.2020.9092000.

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Djokovic, Nemanja, Ana Postolovic, and Katarina Nikolic. "MOLECULAR MODELING OF 5‐[(AMIDOBENZYL)OXY]‐ NICOTINAMIDES AS SIRTUIN 2 INHIBITORS USING ALIGNMENT- (IN)DEPENDENT 3D-QSAR ANALYSIS AND MOLECULAR DOCKING." In 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.410dj.

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The group of 5‐[(amidobenzyl)oxy]‐nicotinamides represents promising group of sirtuin 2 (SIRT2) inhibitors. Despite structural similarity, representatives of this group of inhibitors displayed versatile mechanisms of inhibition which hamper rational drug design. The aim of this research was to form a 3D-QSAR (3D-Quantitative Structure-Activity Relationship) model, define the pharmacophore of this subgroup of SIRT2 inhibitors, define the mode of protein-ligand interactions and design new compounds with improved predicted activity and pharmacokinetics. For the 3D-QSAR study, data set was generated using structures and activities of 166 5‐[(amidobenzyl)oxy]‐nicotinamides. 3D-conformations of compounds were optimized, alignment-independent GRIND2 descriptors were calculated and 3D-QSAR PLS models were generated using 70% of data set. To investigate bioactive conformations of inhibitors, molecular docking was used. Molecular docking analysis identified two clusters of predicted bioactive conformations which is in alignment with experimental observations. The defined pharmacophoric features were used to design novel inhibitors with improved predicted potency and ADMET profiles.
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Ragno, Rino, and Alessio Ragno. "db.3d-qsar.com. The first 3D QSAR models database." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.051r.

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Field-Based Three-Dimensiona Quantitative Strucutere-Activity Relationships (FB 3D QSAR) comprise computational approaches used in drug design and molecular modeling to analyze the relationship between the three-dimensional structure of a list of molecules (described by molecular interaction fields) and their associated biological activities (BAs). It aims to understand how different structural features of the molecules contribute to enhancing or lowering the biological potency. The process of FB 3D QSAR involves several steps. First, a dataset of structurally diverse molecules with known BAs is selected. Then, their three-dimensional structures are generated using computational methods. Next, in the classical form of Cramer [1], sterical and electrostatic molecular interaction fields (MIFs) are calculated and as a final step a mathematical model is built through the correlation of BAs with MIFs by means of projection of latent structures (PLS) algorithm. With our interest in making 3D QSAR accessible to all as done with the www.3d-qsar.com [2] db.3d-qsar.com, the first publicly available database of 3D QSAR models, is presented in which the user can insert or draw a molecule and predict its potency against an available target. All the models available on db.3d-qsar.com have been heavily optimized in prediction power through a semi-systematic pretreatment and parameter selection procedure by initially dividing the datasets into training (80%) and prediction (20%) sets. Each published model was and will be prepared by a selection among thousands of alignment trials. The selected models were finally characterized using a validation set compiled with molecules taken from the ChEMBL database. At the time of writing more than 40 models associated to more than 30 different pharmacological targets have been prepared and are ready to be used. At the time of the presentation db.3d-qsar.com will be accessible to the public and during the presentation its features will be shown.
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"Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties." In Sustainable Processes and Clean Energy Transition. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902516-27.

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Abstract. Owing to the rapid growth in IL synthesis due to feasible cation–anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico principal properties (PPs) as descriptors. Deploying this properties aid in considering both the effect of cations and anions on Vibrio fischeri toxicity prediction. Among the models trained, the Random Forest model proved to be the most precise nevertheless, decision tree model was the most accurate and consistent. Considering the importance of the descriptors to Vibrio fischeri toxicity selection techniques and model optimization.
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Concu, Riccardo, and Maria Natalia Dias Soeiro Cordeiro. "A novel QSAR model to predict epidermial growth factor inhibitors." In MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/mol2net-04-05261.

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Ulfa, Adawiyah, Alhadi Bustamam, Arry Yanuar, Rizka Amalia, and Prasnurzaki Anki. "Model QSAR Classification Using Conv1D-LSTM of Dipeptidyl Peptidase-4 Inhibitors." In 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2021. http://dx.doi.org/10.1109/aims52415.2021.9466083.

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Wang, Dan, Junjie Wang, Chaochao Yang, and Yongqiang Ren. "Simulating QSAR Model of ERa Bioactivity by Statistics and Machine Learning." In ACM ICEA '21: 2021 ACM International Conference on Intelligent Computing and its Emerging Applications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3491396.3506514.

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Levovnik, Bojan D., Aleksa P. Alargić, Miloš M. Svirčev, and Goran I. Benedeković. "Building a 3D QSAR model with isopropylidene analogs of cytotoxic styryl-lactones." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.559l.

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Styryl-lactones are a class of natural products and related analogs that exhibit diverse biological activities, including anticancer, anti-inflammatory, and antimicrobial properties. Since these compounds are routinely obtained in our laboratory from their O-isopropylidene precursors, we envisioned the project to examine and compare their respective structures and in-vitro activities. This paper presents a basic 3D-QSAR steric model built on a small set of 11 selected O-isopropylidene and styryl-lactone (particularly [3.3.0]furofuranone) ligands and their in vitro activities against an MCF-7 cell line. It is part of a larger ongoing study that encompasses synthesis as well as in vitro and in silico testing of a growing library of natural products and their analogs, with the aim to determine their pharmacophores, molecular targets, and cellular mechanisms of action.
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