Relevant bibliographies by topics / QSAR

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  2. Dissertations / Theses
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Academic literature on the topic 'QSAR'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "QSAR"

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Toropov, Andrey A., and Alla P. Toropova. "QSPR/QSAR: State-of-Art, Weirdness, the Future." Molecules 25, no. 6 (March 12, 2020): 1292. http://dx.doi.org/10.3390/molecules25061292.

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.
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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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Costa, Paulo C. S., Joel S. Evangelista, Igor Leal, and Paulo C. M. L. Miranda. "Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR." Mathematics 9, no. 1 (December 29, 2020): 60. http://dx.doi.org/10.3390/math9010060.

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Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.
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Chinen, Kazue, and Timothy Malloy. "Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency." International Journal of Environmental Research and Public Health 19, no. 7 (April 4, 2022): 4338. http://dx.doi.org/10.3390/ijerph19074338.

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Under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) analysis of alternatives (AoA) process, quantitative structure–activity relationship (QSAR) models play an important role in expanding information gathering and organizing frameworks. Increasingly recognized as an alternative to testing under registration. QSARs have become a relevant tool in bridging data gaps and supporting weight of evidence (WoE) when assessing alternative substances. Additionally, QSARs are growing in importance in integrated testing strategies (ITS). For example, the REACH ITS framework for specific endpoints directs registrants to consider non-testing results, including QSAR predictions, when deciding if further animal testing is needed. Despite the raised profile of QSARs in these frameworks, a gap exists in the evaluation of QSAR use and QSAR documentation under authorization. An assessment of the different uses (e.g., WoE and ITS) in which QSAR predictions play a role in evidence gathering and organizing remains unaddressed for AoA. This study approached the disparity in information for QSAR predictions by conducting a substantive review of 24 AoA through May 2017, which contained higher-tier endpoints under REACH. Understanding the manner in which applicants manage QSAR prediction information in AoA and assessing their potential within ITS will be valuable in promoting regulatory use of QSARs and building out future platforms in the face of rapidly evolving technology while advancing information transparency.
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Worth, Andrew P. "ECVAM's Activities on Computer Modelling and Integrated Testing." Alternatives to Laboratory Animals 30, no. 2_suppl (December 2002): 133–37. http://dx.doi.org/10.1177/026119290203002s22.

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This paper introduces the basic concepts of quantitative structure–activity relationship (QSAR), expert system and integrated testing strategy, and explains how the analogy between QSARs and prediction models leads naturally to criteria for the validation of QSARs. ECVAM's in-house research programme on QSAR modelling and integrated testing is summarised, along with plans for the validation of QSAR models and expert system rulebases at the European Union level.
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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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Sizochenko, Natalia, and Jerzy Leszczynski. "Review of Current and Emerging Approaches for Quantitative Nanostructure-Activity Relationship Modeling." Journal of Nanotoxicology and Nanomedicine 1, no. 1 (January 2016): 1–16. http://dx.doi.org/10.4018/jnn.2016010101.

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Quantitative structure-activity/property relationships (QSAR/QSPR) approaches that have been applied with success in a number of studies are currently used as indispensable tools in the computational analysis of nanomaterials. Evolution of nano-QSAR methodology to the ranks of novel field of knowledge has resulted in the development of new so-called “nano-descriptors” and extension of the statistical approaches domain. This brief review focuses on the critical analysis of advantages and disadvantages of existing methods of nanoparticles' representation and their analysis in framework of structure-activity relationships. It summarizes recent QSAR/QSPR studies on inorganic nanomaterials. Here the authors describe practices for the QSAR modeling of inorganic nanoparticles, existing datasets, and discuss applicable descriptors and future perspectives of this field. About 50 different (Q)SAR/SPR models for inorganic nanomaterials have been developed during the past 5 years. An analysis of these peer reviewed publications shows that the most popular property of nanoparticles modeled via QSAR is their toxicity towards different bacteria, cell lines, and microorganisms. It has been clearly shown how nano-QSAR can contribute to the elucidation of toxicity mechanisms and different physical properties of inorganic nanomaterials.
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Rasulev, Bakhtiyor, and Gerardo Casanola-Martin. "QSAR/QSPR in Polymers." International Journal of Quantitative Structure-Property Relationships 5, no. 1 (January 2020): 80–88. http://dx.doi.org/10.4018/ijqspr.2020010105.

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Predictive modeling of the properties of polymers and polymeric materials is getting more attention, while it is still very complicated due to complexity of these materials. In this review, we discuss main applications of quantitative structure-property/activity relationships (QSPR/QSAR) methods for polymers published recently. The most relevant publications are discussed covering this field highlighting the main advantages and drawbacks of the obtained predictive models. Examples dealing with refractive index, glass transition temperatures, intrinsic viscosity, thermal decomposition and flammability properties are shown, together with a fouling-release activity study. Finally, some considerations are discussed in order to give some clues that could lead to the improvement in the efficient computational design and/or optimization of new polymers with enhanced properties/activities.
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Karelson, Mati, Uko Maran, Yilin Wang, and Alan R. Katritzky. "QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications." Collection of Czechoslovak Chemical Communications 64, no. 10 (1999): 1551–71. http://dx.doi.org/10.1135/cccc19991551.

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An overview on the development of QSPR/QSAR equations using various descriptor-mining techniques and multilinear regression analysis in the framework of the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular activities and properties. The properties cover single molecular species, interactions between different molecular species, properties of surfactants, complex properties and properties of polymers. A review with 54 references.
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Li, Yi-Xia, Abdul Rauf, Muhammad Naeem, Muhammad Ahsan Binyamin, and Adnan Aslam. "Valency-Based Topological Properties of Linear Hexagonal Chain and Hammer-Like Benzenoid." Complexity 2021 (April 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/9939469.

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Topological indices are quantitative measurements that describe a molecule’s topology and are quantified from the molecule’s graphical representation. The significance of topological indices is linked to their use in QSPR/QSAR modelling as descriptors. Mathematical associations between a particular molecular or biological activity and one or several biochemical and/or molecular structural features are QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships). In this paper, we give explicit expressions of two recently defined novel ev-degree- and ve-degree-based topological indices of two classes of benzenoid, namely, linear hexagonal chain and hammer-like benzenoid.
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Dissertations / Theses on the topic "QSAR"

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Espinosa, Porragas Gabriela. "Modelos QSPR/QSAR/QSTR basados en sistemas neuronales cognitivos." Doctoral thesis, Universitat Rovira i Virgili, 2002. http://hdl.handle.net/10803/8505.

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Un área sumamente interesante dentro del modelado molecular es el diseño de nuevos compuestos. Con sus propiedades definidas antes de ser sintetizados. Los métodos QSPR/QSAR han demostrado que las relaciones entre la estructura molecular y las propiedades físico químicas o actividades biológicas de los compuestos se pueden cuantificar matemáticamente a partir de parámetros estructurales simples.
Las redes neuronales (ANN) constituyen una alternativa para el desarrollo de algoritmos predictivos aplicados en diversos campos como: análisis masivo de bases de datos, para subsanar los obstáculos derivados de la selección o la multicolinealidad de variables, así como la sensibilidad de los modelos a la presencia de ruido en los datos de entrada al sistema neuronal. En la mayoría de los casos, las redes neuronales han dado mejores resultados que los métodos de regresión multilineal (MLR), el análisis de componentes principales (PCA), o los métodos de mínimos cuadrados parciales (PLS) debido a la no linealidad inherente en los modelos de redes.

En los últimos años el interés por los modelos QSPR/QSAR basados en redes neuronales se ha incrementado. La principal ventaja de los modelos de redes recae en el hecho que un modelo QSAR/QSPR puede desarrollarse sin especificar a priori la forma analítica del modelo. Las redes neuronales son especialmente útiles para establecer las complejas relaciones existentes entre la salida del modelo (propiedades físico químicas o biológicas) y la entrada del modelo (descriptores moleculares). Además, permiten clasificar los compuestos de acuerdo a sus descriptores moleculares y usar esta información para seleccionar el conjunto de índices capaz de caracterizar mejor al conjunto de moléculas. Los modelos QSPR basados en redes usan principalmente algoritmos del tipo backpropagation. Backpropagation es un sistema basado en un aprendizaje por minimización del error. Sin embargo, ya que los compuestos químicos pueden clasificarse en grupos de acuerdo a su similitud molecular, es factible usar un clasificador cognitivo como fuzzy ARTMAP para crear una representación simultánea de la estructura y de la propiedad objetivo. Este tipo de sistema cognitivo usa un aprendizaje competitivo, en el cual hay una activa búsqueda de la categoría o la hipótesis cuyos prototipos provean una mejor representación de los datos de entrada (estructura química).

En el presente trabajo se propone y se estudia una metodología que integra dos sistemas cognitivos SOM y fuzzy ARTMAP para obtener modelos QSAR/QSPR. Los modelos estiman diferentes propiedades como las temperaturas de transición de fase (temperatura de ebullición, temperatura de fusión) y propiedades críticas (temperatura y presión), así como la actividad biológica de compuestos orgánicos diversos (indicadores de toxicidad). Dentro de este contexto, se comparan la selección de variables realizados por métodos tradicionales (PCA, o métodos combinatorios) con la realizada usando mapas auto-organizados (SOM).

El conjunto de descriptores moleculares más factible se obtiene escogiendo un representante de cada categoría de índices, en particular aquel índice con la correlación más alta con respecto a la propiedad objetivo. El proceso continúa añadiendo índices en orden decreciente de correlación. Este proceso concluye cuando una medida de disimilitud entre mapas para los diferentes conjuntos de descriptores alcanza un valor mínimo, lo cual indica que el añadir descriptores adicionales no provee información complementaria a la clasificación de los compuestos estudiados. El conjunto de descriptores seleccionados se usa como vector de entrada a la red fuzzy ARTMAP modificada para poder predecir.

Los modelos propuestos QSPR/QSAR para predecir propiedades tanto físico químicas como actividades biológicas predice mejor que los modelos obtenidos con métodos como backpropagation o métodos de contribución de grupos en los casos en los que se apliquen dichos métodos.
One of the most attractive applications of computer-aided techniques in molecular modeling stands on the possibility of assessing certain molecular properties before the molecule is synthesized. The field of Quantitative Structure Activity/Property Relationships (QSAR/QSPR) has demonstrated that the biological activity and the physical properties of a set of compounds can be mathematically related to some "simple" molecular structure parameters.

Artificial neural network (ANN) approaches provide an alternative to established predictive algorithms for analyzing massive chemical databases, potentially overcoming obstacles arising from variable selection, multicollinearity, specification of important parameters, and sensitivy to erroneous values. In most instances, ANN's have proven to be better than MLR, PCA or PLS because of their ability to handle non-linear associations.

In the last years there has been a growing interest in the application of neural networks to the development of QSAR/QSPR. The mayor advantage of ANN lies in the fact QSAR/QSPR can be developed without having to a priori specify an analytical form for the correlation model. The NN approach is especially suited for mapping complex non-linear relationships that exists between model output (physicochemical or biological properties) and input model (molecular descriptors). The NN approach could also be used to classify chemicals according to their chemical descriptors and used this information to select the most suitable indices capable of characterize the set of molecules. Existing neural networks based QSAR/QSPR for estimating properties of chemicals have relied primarily on backpropagation architecture. Backpropagation are an error based learning system in which adaptive weights are dynamically revised so as to minimize estimation errors of target values. However, since chemical compounds can be classified into various structural categories, it is also feasible to use cognitive classifiers such as fuzzy ARTMAP cognitive system, for unsupervised learning of categories, which represent structure and properties simultaneously. This class of neural networks uses a match-based learning, in that it actively searches for recognition categories or hypotheses whose prototype provides an acceptable match to input data.

The current study have been proposed a new QSAR/QSPR fuzzy ARTMAP neural network based models for predicting diverse physical properties such as phase transition temperatures (boiling and melting points) and critical properties (temperature and pressure) and the biological activities (toxicity indicators) of diverse set of compounds. In addition, traditional pre-screening methods to determine de minimum set of inputs parameters have been compared with novel methodology based in self organized maps algorithms.

The most suitable set of molecular descriptor was obtained by choosing a representative from each cluster, in particular the index that presented the highest correlation with the target variable, and additional indices afterwards in order of decreasing correlation. The selection process ended when a dissimilarity measure between the maps for the different sets of descriptors reached a minimum valued, indicating that the inclusion of more descriptors did not add supplementary information. The optimal subset of descriptors was finally used as input to a fuzzy ARTMAP architecture modified to effect predictive capabilities.

The proposed QSPR/QSAR model predicted physicochemical or biological activities significantly better than backpropagation neural networks or traditional approaches such as group contribution methods when they applied.
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Al-Fahemi, Jabir Hamad. "Momentum-space descriptors for QSPR and QSAR studies." Thesis, University of Liverpool, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439465.

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Arruda, Anna Celia. "Ampliação e aplicação do método semi-empírico topológico (IET) em modelos QSRR/QSPR/QSAR para compostos alifáticos halogenados e cicloalcanos." Florianópolis, SC, 2008. http://repositorio.ufsc.br/xmlui/handle/123456789/91111.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas. Programa de Pós-Graduação em Química.
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Este estudo foi desenvolvido para avaliar a capacidade de prognóstico do índice semi-empírico topológico (IET) em estimar a retenção cromatográfica (IR) de compostos alifáticos halogenados e cicloalcanos. Também foram desenvolvidos modelos de QSPR/QSAR para prever importantes propriedades físico-químicas, termodinâmicas e atividades biológicas. O modelo de QSRR do IRExpde 141 haloalcanos e o IET mostrou boa qualidade estatística (r2=0,9995; SD=8; r2cv=0,999). A partir do modelo de QSPR obtido entre o ponto de ebulição, Bp(ºC), com o IET (N=86; r2=0,9971; SD=4,2; r2cv=0,997), foram calculados os valores para um grupo externo de 24 compostos (r2=0,9931; SD=7,6). Uma boa correlação entre o ponto de fusão, Mp (°C), e o IET foi obtida (N=43; r2=0,9865; SD=6,1; r2cv=0,985). As correlações obtidas entre os valores calculados e experimentais de log P foram de r2=0,9871 e r2=0,9750, respectivamente para os Métodos Semi-Empírico Topológico e Contribuição dos Fragmentos. Esses resultados mostram a capacidade de prognóstico do IET para propriedades físico-químicas e termodinâmicas. A habilidade de prognóstico do IR pelo IET também foi verificada usando fases estacionárias com diferentes polaridades. Resultados satisfatórios foram encontrados aplicando o IET para estimar o IR de 48 cicloalcanos (r2=0,9905; SD=7; r2cv=0,997) e Bp(°C) (N=33; r2cv=0,988). Esse método permitiu retirar informações sobre as características estruturais, eletrônicas e geométricas das moléculas que estão influenciando no processo de retenção cromatográfico e a distinção entre isômeros cis/trans dos compostos estudados.
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Davor, Lončar. "Definisanje lipofilnosti, farmakokinetičkih parametara i antikancerogenog potencijala novosintetisane serije stiril laktona." Phd thesis, Univerzitet u Novom Sadu, Tehnološki fakultet Novi Sad, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107622&source=NDLTD&language=en.

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Reverzno-faznom tečnom hromatografijom pod visokim pritiskom primenom dva sistemarastvarača ispitano je ponašanje i hromatografska lipofilnost prirodnih stiril laktona 7-(+)-goniofufurona, 7-epi-(+)-goniofufurona, krasalaktona B i C i dvadeset njihovihnovosintetizovanih derivata i analoga. U ranijim ispitivanjima pokazalo se da ova jedinjenjaimaju veliki biološki potencijal jer pokazuju zapaženu citotoksičnost prema više humanihtumorskih ćelijskih linija. Hromatografsko ponašanje jedinjenja uglavnom je u skladu sanjihovom strukturom. Ustanovljene su linearne veze između hromatografskih retencionihkonstanti i većine in silico parametara lipofilnosti. Primenom hemometrijske QSRR analizeutvrđeni su veoma dobri multi linearni regresioni prediktivni modeli kvantitativne zavisnostiizmeđu eksperimentalno dobijene hromatografske retencione konstante, koja definišeretenciju jedinjenja u čistoj vodi i in silico molekulskih deskriptora odnosno strukturejedinjenja. Lipofilnost jedinjenja ima najveći uticaj na njihove farmakokinetičke, tj. ADME(apsorpcija, distribucija, metabolizam, eliminacija) osobine. Definisani su i statističkipotvrđeni najbolji multi linearni regresioni modeli zavisnosti farmakokinetičkih parametarastiril laktona i od drugih molekulskih deskriptora. In vitro citotoksična aktivnost jedinjenjaevaluirana je prema četiri nove humane maligne ćelijske linije: kancer prostate (PC3), kancer debelog creva (HT-29), melanom (Hs294T), adenokancer pluća (A549). Najaktivnijenovosintetizovano jedinjenje je triciklični 4-fluorocinamatni analog, koji ispoljavananomolarnu aktivnost (IC50 2,1 nM) prema ćelijama melanoma i aktivniji je preko 2250 puta od komercijalnog antitumorskog agensa doksorubicina (DOX). SAR analizom utvrđena je zavisnost između strukture i biološke aktivnosti jedinjenja. Molekulskim dokingom ispitana je veza stiril laktona i ciljanog proteina značajnog za kancer prostate. Jedinjenja sa visokom inhibitornom aktivnošću prema ćelijama kancera prostate imaju visok doking skor i mogu graditi koordinativno-kovalentnu vezu sa Fe2+jonom prisutnim u aktivnom centru enzima. 3D-QSAR analizom, koja je izvedena metodama komparativnih polja CoMFA i CoMSIA, formiran je značajan prediktivni model između hemijske strukture i biološke aktivnosti stiril laktona.
The behavior and the chromatographic lipophilicity natural styryl lactone 7-(+)-goniofufurone, 7-epi-(+)-goniofufurone, crassalactones B and C and twenty of their newlysynthesized derivatives and analogs were examined using reverse-phase high performance liquid chromatography in the two solvent systems. In previous studies it has been shown that these compounds have great biological potential toward several human tumor cell lines. Chromatographic behavior of the compounds is generally in accordance with their structure. The relationships between the chromatographic retention constants and the majority of their in silico lipophilicity parameters are linear. The application of chemometric QSRR analysis determined very good multiple linear regression predictive models of quantitative correlation between experimentally obtained chromatographic retention constant, which determines the retention of the compound in pure water and in silico molecular descriptors, i.e. the structure of the compound. The lipophilicity of the compounds has a major influence on their pharmacokinetics, i.e. ADME (absorption, distribution, metabolism, elimination) properties. The best multi-linear regression models depending on the pharmacokinetic parameters of styryl lactone and other molecular descriptors have been defined and statistically validated. In vitro cytotoxic activity of the compounds was evaluated according to four novel human malignant cell lines: prostate cancer (PC3), colon cancer (HT-29), melanoma (Hs294T), lung adenocarcinom (A549). The most active compound was tricyclic 4-fluorocinnamic analog, which exhibits a nanomolar activity (IC50 2,1 nM) toward melanoma cells. This compound is over 2250 times more active than commercial antitumor agent doxorubicin (DOX). SAR analysis has revealed a correlation between the structure and the biological activity of the compounds. Using the molecular docking the relationship of the styryl lactone and the target protein important for prostate cancer was examined. The compounds with high inhibitory activity against prostate cancer cells have a high docking score and are capable to form a coordinative-covalent bond with a Fe2+ ion present in the active centre of the enzyme. 3DQSAR analysis, which was performed by methods of comparative CoMFA and CoMSIA fields, has formed a good predictive model between chemical structure and biological activity of the styryl lactone.
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Bitencourt, Michelle 1985. "Modelagem MIA-QSAR de inibidores de acetilcolinesterase = MIA-QSAR modeling of inhibitors actylcholinesterase." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/311808.

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Orientador: Roberto Rittner Neto
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Ciências Médicas
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Resumo: O presente trabalho trata de um estudo sobre compostos que se comportam como inibidores da acetilcolinesterase, uma importante enzima do processo de cognição. A acetilcolinesterase atua na hidrólise da acetilcolina, responsável pela comunicação entre os neurônios. Uma das modalidades para o design racional de fármacos é a estimativa de propriedades biológicas de novas moléculas utilizando métodos computacionais. Análise quantitativa entre estrutura química e atividade biológica (QSAR) é uma dessas técnicas. No presente trabalho, análise multivariada de imagens aplicada em QSAR (MIA-QSAR) foi utilizada para se construírem modelos QSAR preditivos para uma série congênere de carbamatos com atividade anticolinesterásica. Os bons resultados estatísticos da modelagem credenciaram o modelo MIA-QSAR construído a predizer a atividade biológica de alguns novos derivados, potencialmente úteis para o tratamento do Mal de Alzheimer
Abstract: The present work describes the study of some compounds which act as acetylcholinesterase inhibitors a very important enzyme in the cognitive process. zAcetylcholinesterase is responsible by the hydrolysis of acetylcholine, which accounts for the communication among the neurons. One of the approaches for the rational pharmaceuticals design is the estimation of the biological properties of new molecules using computational methods. The quantitative analysis between chemical structure and biological activity (QSAR) is one of these techniques. In the present work, the multivariate analysis of images applied in QSAR (MIA-QSAR) was employed for building predictable QSAR models for a congenial series of carbamates which exhibit anticholinesterase activity. The significant statistical results from this treatment enabled the MIA-QSAR model thus obtained to reliably predict the biological activity of some new derivatives, as potentially useful for the Alzheimer Disease treatment
Mestrado
Ciencias Biomedicas
Mestra em Ciências Médicas
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Martins, João Paulo Ataíde 1980. "Desenvolvimento de softwares, algoritmos e diferentes abordagens quimiométricas em estudos de QSAR." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/248544.

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Orientador: Márcia Miguel Castro Ferreira
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Química
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Resumo: O planejamento de fármacos com o auxílio do computador é uma área de pesquisa de extrema importância em química e áreas correlatas. O conjunto de ferramentas disponíveis para tal fim consiste, dentre outras, em programas para geração de descritores e construção e validação de modelos matemáticos em QSAR (do inglês, Quantitative Structure-Activity Relationship). Com o objetivo de tornar esse estudo mais acessível para a comunidade científica, novas metodologias e programas para geração de descritores e construção e validação de modelos QSAR foram desenvolvidos nessa tese. Uma nova metodologia de QSAR 4D, conhecida com LQTA-QSAR, foi desenvolvida com o objetivo de gerar descritores espaciais levando em conta os perfis de amostragem conformacional das moléculas em estudo obtidos a partir de simulações de dinâmica molecular. A geração desses perfis é feita com o software livre GROMACS e os descritores são gerados a partir de um novo software desenvolvido nesse trabalho, chamado de LQTAgrid. Os resultados obtidos com essa metodologia foram validados comparando-os com resultados obtidos para conjuntos de dados disponíveis na literatura. Um outro software de fácil uso, e que engloba as principais ferramentas de construção e validação de modelos em QSAR, foi desenvolvido e chamado de QSAR modeling. Esse software implementa o método de seleção de variáveis OPS, desenvolvido em nosso laboratório, e utiliza PLS (do inglês Partial Least Squares) como método de regressão. A escolha do algoritmo PLS implementado no programa foi feita com base em um estudo sobre o desempenho e a precisão no erro de validação dos principais algoritmos PLS disponíveis na literatura. Além disso, o programa QSAR modeling foi utilizado em um estudo de QSAR 2D para um conjunto de 20 flavonóides com atividade anti-mutagênica contra 3-nitrofluoranteno (3-NFA)
Abstract: Computer aided drug design is an important research field in chemistry and related areas. The available tools used in such studies involve software to generate molecular descriptors and to build and validate mathematical models in QSAR (Quantitative Structure-Activity Relationship). A new set of methodologies and software to generate molecular descriptors and to build and validate QSAR models were developed aiming to make these kind of studies more accessible to scientific community. A new 4DQSAR methodology, known as LQTA-QSAR, was developed with the purpose to generate spatial descriptors taking into account conformational ensemble profile obtained from molecular dynamics simulations. The generation of these profiles is performed by free software GROMACS and the descriptors are generated by a new software developed in this work, called LQTAgrid. The results obtained with this methodology were validated comparing them with results available in literature. Another user friendly software, which contains some of the most important tools used to build and validate QSAR models was developed and called QSAR modeling. This software implements the OPS variable selection algorithm, developed in our laboratory, and uses PLS (Partial Least Squares) as regression method. The choice of PLS algorithm implemented in the program was performed by a study about the performance and validation precision error involving the most important PLS algorithms available in literature. Further, QSAR modeling was used in a 2D QSAR study with 20 flavonoid derivatives with antimutagenic activity against 3-nitrofluoranthene (3-NFA)
Doutorado
Físico-Química
Doutor em Ciências
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Hellberg, Sven. "A multivariate approach to QSAR." Doctoral thesis, Umeå universitet, Kemiska institutionen, 1986. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-100713.

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Quantitative structure-activity relationships (OSAR) constitute empirical analogy models connecting chemical structure and biological activity. The analogy approach to QSAR assume that the factors important in the biological system also are contained in chemical model systems. The development of a QSAR can be divided into subproblems: 1. to quantify chemical structure in terms of latent variables expressing analogy, 2. to design test series of compounds, 3. to measure biological activity and 4. to construct a mathematical model connecting chemical structure and biological activity. In this thesis it is proposed that many possibly relevant descriptors should be considered simultaneously in order to efficiently capture the unknown factors inherent in the descriptors. The importance of multivariately and multipositionally varied test series is discussed. Multivariate projection methods such as PCA and PLS are shown to be appropriate far QSAR and to closely correspond to the analogy assumption. The multivariate analogy approach is applied to a beta- adrenergic agents, b haloalkanes, c halogenated ethyl methyl ethers and d four different families of peptides.

Diss. (sammanfattning) Umeå : Umeå universitet, 1986, härtill 8 uppsatser


digitalisering@umu
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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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Bartlett, Alison. "QSAR study of immunotoxicity in antibiotics." Thesis, Liverpool John Moores University, 1995. http://researchonline.ljmu.ac.uk/5135/.

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Since their inception the B-Iactam antibiotics have become one of the most important classes of phannaceutical agents, both therapeutically and economically, in modern day usage for the treatment of a wide spectrum of bacterial infections. However, due to the versatility of bacteria many previously treatable species are developing resistance to the antibiotics currently available and so there is ever a need to develop more ~-lactam antibiotics, which are effective and yet safe. A major drawback to the ~-lactams is the degree of immunologically adverse reactions they induce. It was the aim of this study to develop both mechanistic and immunological methods to enable the prediction of a B-lactam's potential to induce an allergic response and to determine if a relationship between these responses and the molecular properties of the ~-lactams was present. In this study a database pertaining to frequency by which 70 p-lactams induce adverse reactions has been compiled and used to produce 27 QSAR models. A highly sensitive assay for the quantitation of cross-reactivity between B-lactams and serum anti-benzylpenicillin antibodies has been developed and used to determine the cross-reactivity potential of 31 ~-lactams and to develop 18 QSAR models. All of the QSARs developed suggest that the shape and electron separation of the ~-lactams are crucial to the development and extent of adverse response or crossreactivity induced by a specific p-lactam antibiotic, new or old. The QSARs developed will enable the design and development of new ~-lactam antibiotics which present a significantly lower risk of inducing immunologically mediated adverse responses when used therapeutically. Two sensitive assays for the quantitative detennination of the cytokines IL2 and IL4 in lymphocyte culture supernatants have been developed, and have been shown to have a potential use in the prediction of the type of immunological response initiated following p-Iactam stimulation of a sensitised individual.
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Thomsen, Marianne. "QSARs in environmental risk assessment : interpretation and validation of SAR/QSAR based on multivariate data analysis /." Roskilde : Roskilde University, Department of Life Science and Chemistry, 2001. http://hdl.handle.net/1800/538.

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Books on the topic "QSAR"

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. A Primer on QSAR/QSPR Modeling. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1.

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V, Diudea Mircea, ed. QSPR/QSAR studies by molecular descriptors. Huntington, N.Y: Nova Science Publishers, 2001.

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Corwin, Hansch, Leo Albert, and Hoekman D. H, eds. Exploring QSAR. Washington, DC: American Chemical Society, 1995.

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Corwin, Hansch, Leo Albert, and Hoekman David, eds. Exploring QSAR. Washington, DC: American Chemical Society, 1995.

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1956-, Devillers J., ed. Comparative QSAR. Washington, DC: Taylor & Francis, 1998.

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Corwin, Hansch, and Leo Albert, eds. Exploring QSAR. Washington, DC: American Chemical Society, 1995.

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Dehmer, Matthias, Kurt Varmuza, and Danail Bonchev, eds. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527645121.

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Toropova, Alla P., and Andrey A. Toropov, eds. QSPR/QSAR Analysis Using SMILES and Quasi-SMILES. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28401-4.

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1956-, Devillers J., and Balaban Alexandru T, eds. Topological indices and related descriptors in QSAR and QSPR. Amsterdam: Gordon and Breach, 1999.

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Roy, Kunal, ed. Advances in QSAR Modeling. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56850-8.

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Book chapters on the topic "QSAR"

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Sippl, Wolfgang, and Dina Robaa. "QSAR/QSPR." In Applied Chemoinformatics, 9–52. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527806539.ch2.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "QSAR/QSPR Methods." In SpringerBriefs in Molecular Science, 61–103. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_3.

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Golbraikh, Alexander, and Alexander Tropsha. "QSAR/QSPR Revisited." In Chemoinformatics, 465–95. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527816880.ch12.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "QSAR/QSPR Modeling: Introduction." In SpringerBriefs in Molecular Science, 1–36. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_1.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "Statistical Methods in QSAR/QSPR." In SpringerBriefs in Molecular Science, 37–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_2.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. "Newer Directions in QSAR/QSPR." In SpringerBriefs in Molecular Science, 105–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17281-1_4.

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Talevi, Alan. "In Silico ADME: QSPR/QSAR." In The ADME Encyclopedia, 525–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84860-6_149.

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Talevi, Alan. "In Silico ADME: QSPR/QSAR." In The ADME Encyclopedia, 1–7. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51519-5_149-1.

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Ahmadi, Shahin, Sepideh Ketabi, and Marjan Jebeli Javan. "Molecular Descriptors in QSPR/QSAR Modeling." In Challenges and Advances in Computational Chemistry and Physics, 25–56. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28401-4_2.

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Ahmadi, Shahin, and Neda Azimi. "Quasi-SMILES-Based QSPR/QSAR Modeling." In Challenges and Advances in Computational Chemistry and Physics, 191–210. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28401-4_8.

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Conference papers on the topic "QSAR"

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Duprat, A., J. L. Ploix, F. Dioury, and G. Dreyfus. "Toward big data in QSAR/QSPR." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958884.

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Skvortsova, M. I., I. I. Baskin, V. A. Palyulin, O. L. Slovokhotova, and N. S. Zefirov. "Structural design inverse problems for topological indices in QSAR/QSPR studies." In The first European conference on computational chemistry (E.C.C.C.1). AIP, 1995. http://dx.doi.org/10.1063/1.47751.

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Laghridat, Charifa, Ilham Mounir, and Mohamed Essalih. "Understanding changes in the structure of complex networks using QSAR/QSPR." In 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC). IEEE, 2022. http://dx.doi.org/10.1109/isivc54825.2022.9800741.

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Zeryouh, Meryam, Mohamed El Marraki, and Mohamed Essalih. "Some tools of QSAR/QSPR and drug development: Wiener and Terminal Wiener indices." In 2015 International Conference on Cloud Technologies and Applications (CloudTech). IEEE, 2015. http://dx.doi.org/10.1109/cloudtech.2015.7336963.

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Basak, Subhash. "QSAR for the characterization of drug resistance: Differential QSAR (DiffQSAR) using mathematical chemodescriptors." In MOL2NET, International Conference on Multidisciplinary Sciences. Basel, Switzerland: MDPI, 2015. http://dx.doi.org/10.3390/mol2net-1-b025.

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Cocchi, M., M. C. Menziani, F. Fanelli, and P. G. de Benedetti. "Theoretical QSAR and QSSR analyses of 5-HT1A serotonin and α1-adrenergic receptors ligands." In The first European conference on computational chemistry (E.C.C.C.1). AIP, 1995. http://dx.doi.org/10.1063/1.47824.

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Ma, Eddie Y. T., and Stefan C. Kremer. "Neural Grammar Networks in QSAR Chemistry." In 2009 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2009. http://dx.doi.org/10.1109/bibm.2009.60.

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Schroeter, Timon, Anton Schwaighofer, Sebastian Mika, Antonius Ter Laak, Detlev Suelzle, Ursula Ganzer, Nikolaus Heinrich, et al. "Predicting Error Bars for QSAR Models." In COMPLIFE 2007: The Third International Symposium on Computational Life Science. AIP, 2007. http://dx.doi.org/10.1063/1.2793398.

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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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Reports on the topic "QSAR"

1

Ward, Keith B. Antiviral Drugs: Molecular Modeling and QSAR. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada256419.

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Lu, P.-Y., and K. Yuracko. LiverTox: Advanced QSAR and Toxicogeomic Software for Hepatotoxicity Prediction. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1006280.

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Brashear, W. T., and P. P. Lu. Evaluation of QSAR for Use in Predictive Toxicology Modeling. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada274144.

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Mills, Jeffrey D. IL QC QSPR - Preliminary Results. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada422511.

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Katritzky, Alan R. Detoxification of Military Wastes by Nearcritical and Supercritical Water and QSPR Investigations. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada357837.

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Leszczynski, Jerzy. Development of efficient solar cells using combination of QSPR and DFT approaches. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1785077.

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