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Academic literature on the topic 'QSPkR'

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Published: 4 June 2021
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Journal articles on the topic "QSPkR"

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Zhivkova, Zvetanka. "QUANTITATIVE STRUCTURE–PHARMACOKINETICS RELATIONSHIP FOR PLASMA PROTEIN BINDING OF NEUTRAL DRUGS." International Journal of Pharmacy and Pharmaceutical Sciences 10, no. 4 (April 1, 2018): 88. http://dx.doi.org/10.22159/ijpps.2018v10i4.24612.

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Objective: Plasma protein binding (PPB) of drugs is important pharmacokinetic (PK) phenomena controlling the free drug concentration in plasma and the overall PK and pharmacodynamic profile. Prediction of PPB at the very early stages of drug development process is of paramount importance for the success of new drug candidates. The study presents a quantitative structure–pharmacokinetics relationship (QSPkR) modelling of PPB for neutral drugs.Methods: The dataset consists of 117 compounds, described by 138 molecular descriptors. Genetic algorithm and stepwise multiple linear regression are used for variable selection and QSPkR models development. The QSPkRs are evaluated by internal and external validation procedures.Results: A robust, significant and predictive QSPkR with explained variance r2 0.768, cross-validated q2LOO-CV 0.731,and geometric mean fold error of prediction (GMFEP) 1.79 is generated, which is able to predict the extent of PPB for 67.6% of the drugs in the dataset within the 2-fold error of experimental values. A simple empiric rule is proposed for distinguishing between drugs with different binding affinity, which allowed correct classification of 78% of the high binders and 87.5% of the low binders.Conclusions: PPB of neutral drugs is favored by lipophilicity, dipole moment, the presence of substituted aromatic and fused rings and a nine-member ring system, and is disfavored by the presence of aromatic N-atoms. Keywords: Plasma protein binding (PPB), Quantitative structure–pharmacokinetics relationship (QSPkR), In silico prediction, Human serum albumin (HSA), Alpha-1-acid glycoprotein (AGP).
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Zhivkova, Zvetanka. "QUANTITATIVE STRUCTURE–PHARMACOKINETICS MODELING OF THE UNBOUND CLEARANCE FOR NEUTRAL DRUGS." International Journal of Current Pharmaceutical Research 10, no. 2 (March 15, 2018): 56. http://dx.doi.org/10.22159/ijcpr.2018v10i2.25849.

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Objective: Prediction of pharmacokinetic behaviour of new candidate drugs is an important step in drug design. Clearance is a key pharmacokinetic parameter, controlling drug exposure in the body. It depends on numerous factors and is frequently restricted by plasma protein binding. The study is focused on the development of quantitative structure-pharmacokinetic relationship (QSPkR) for the unbound clearance (CLu) of neutral drugs.Methods: The dataset consisted of 117 neutral drugs, divided into training set (n = 94) and external test set (n = 23). Chemical structures were encoded by 113 theoretical descriptors. Genetic algorithm and step-wise multiple linear regression were applied for model development. The model was evaluated by cross-validation in the training set and external test set.Results: Significant, predictive and interpretable QSPkR model was developed with explained variance r2 = 0.617, cross-validated correlation coefficient q2LOO-CV = 0.554, external test set predictive coefficient r2pred = 0.656, and root mean square error in prediction RMSEP = 1.89. The model was able to predict CLu for 56% of the drugs in the external test set within the 2-fold error of experimental values.Conclusion: The model reveals the main molecular features governing CLu of neutral drugs. CLu is favoured by lipophilicity, the presence of fused aromatic rings, ester groups, dihydropyridine moieties and nine-member ring systems, while polarity, molecular size and strong electron withdrawing atoms and groups as substituents in aromatic rings affect negatively CL
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Morris, Marilyn E., Xinning Yang, Yash A. Gandhi, Suraj G. Bhansali, and Lisa J. Benincosa. "Interspecies scaling: prediction of human biliary clearance and comparison with QSPKR." Biopharmaceutics & Drug Disposition 33, no. 1 (January 2012): 1–14. http://dx.doi.org/10.1002/bdd.1761.

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Zhivkova, Zvetanka Dobreva. "Quantitative Structure – Pharmacokinetics Relationships for Plasma Protein Binding of Basic Drugs." Journal of Pharmacy & Pharmaceutical Sciences 20, no. 1 (September 14, 2017): 349. http://dx.doi.org/10.18433/j33633.

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Purpose. Binding of drugs to plasma proteins is a common physiological occurrence which may have a profound effect on both pharmacokinetics and pharmacodynamics. The early prediction of plasma protein binding (PPB) of new drug candidates is an important step in drug development process. The present study is focused on the development of quantitative structure – pharmacokinetics relationship (QSPkR) for the negative logarithm of the free fraction of the drug in plasma (pfu) of basic drugs. Methods. A dataset includes 220 basic drugs, which chemical structures are encoded by 176 descriptors. Genetic algorithm, stepwise regression and multiple linear regression are used for variable selection and model development. Predictive ability of the model is assessed by internal and external validation. Results. A simple, significant, interpretable and predictive QSPkR model is constructed for pfu of basic drugs. It is able to predict 59% of the drugs from an external validation set within the 2-fold error of the experimental values with squared correlation coefficient of prediction 0.532, geometric mean fold error (GMFE) 1.94 and mean absolute error (MAE) 0.17. Conclusions. PPB of basic drugs is favored by the lipophilicity, the presence of aromatic C-atoms (either non-substituted, or involved in bridged aromatic systems) and molecular volume. The fraction ionized as a base fB and the presence of quaternary C-atoms contribute negatively to PPB. A short checklist of criteria for high PPB is defined, and an empirical rule for distinguishing between low, high and very high plasma protein binders is proposed based. This rule allows correct classification of 69% of the very high binders, 71% of the high binders and 91% of the low binders in plasma. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.
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Zhivkova, Zvetanka Dobreva, Tsvetelina Mandova, and Irini Doytchinova. "Quantitative Structure – Pharmacokinetics Relationships Analysis of Basic Drugs: Volume of Distribution." Journal of Pharmacy & Pharmaceutical Sciences 18, no. 3 (October 12, 2015): 515. http://dx.doi.org/10.18433/j3xc7s.

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Purpose. The early prediction of pharmacokinetic behavior is of paramount importance for saving time and resources and for increasing the success of new drug candidates. The steady-state volume of distribution (VDss) is one of the key pharmacokinetic parameters required for the design of a suitable dosage regimen. The aim of the study is to propose a quantitative structure – pharmacokinetics relationships (QSPkR) for VDss of basic drugs. Methods: The data set consists of 216 basic drugs, divided to a modeling (n = 180) and external validation set (n = 36). 179 structural and physicochemical descriptors are calculated using validated commercial software. Genetic algorithm, stepwise regression and multiple linear regression are applied for variable selection and model development. The models are validated by internal and external test sets. Results: A number of significant QSPkRs are developed. The most frequently emerged descriptors are used to derive the final consensus model for VDss with good explanatory (r2 0.663) and predictive ability (q2LOO-CV 0.606 and r2pred 0.593). The model reveals clear structural features determining VDss of basic drugs which are summarized in a short list of criteria for rapid discrimination between drugs with a large and small VDss. Conclusions: Descriptors like lipophilicity, fraction ionized as a base at pH 7.4, number of cycles and fused aromatic rings, presence of Cl and F atoms contribute positively to VDss, while polarity and presence of strong electrophiles have a negative effect. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.
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van der Graaf, Pieter H., Jonas Nilsson, Erno A. van Schaick, and Meindert Danhof. "Multivariate Quantitative Structure–Pharmacokinetic Relationships (QSPKR) Analysis of Adenosine A1 Receptor Agonists in rat." Journal of Pharmaceutical Sciences 88, no. 3 (March 1999): 306–12. http://dx.doi.org/10.1021/js980294a.

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Durairaj, Chandrasekar, Jaymin C. Shah, Shruti Senapati, and Uday B. Kompella. "Prediction of Vitreal Half-Life Based on Drug Physicochemical Properties: Quantitative Structure–Pharmacokinetic Relationships (QSPKR)." Pharmaceutical Research 26, no. 5 (October 8, 2008): 1236–60. http://dx.doi.org/10.1007/s11095-008-9728-7.

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Louis, Bruno, and Vijay K. Agrawal. "Quantitative structure-pharmacokinetic relationship (QSPkP) analysis of the volume of distribution values of anti-infective agents from j group of the ATC classification in humans." Acta Pharmaceutica 62, no. 3 (September 1, 2012): 305–23. http://dx.doi.org/10.2478/v10007-012-0024-z.

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In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs.
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Zhivkova, Zvetanka Dobreva. "Quantitative Structure – Pharmacokinetic Relationships for Plasma Clearance of Basic Drugs with Consideration of the Major Elimination Pathway." Journal of Pharmacy & Pharmaceutical Sciences 20 (May 29, 2017): 135. http://dx.doi.org/10.18433/j3mg71.

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Purpose. The success of a new drug candidate is determined not only by its efficacy and safety, but also by proper pharmacokinetic behavior. The early prediction of pharmacokinetic parameters could save time and resources and accelerate drug development process. Plasma clearance (CL) is one of the key determinants of drug dosing regimen. The aim of the study is development of quantitative structure – pharmacokinetics relationships (QSPkRs) for the CL. Methods. A dataset consisted of 263 basic drugs, which chemical structures were described by 154 descriptors. Genetic algorithm, stepwise regression and multiple linear regression were used for variable selection and model development. Predictive ability of the models was assessed by internal and external validation. Results. A number of significant QSPkR models for the CL were derived with respect to the primary elimination pathway (renal excretion, metabolism, or CYP3A4 mediated biotransformation), as well for the unbound clearance (CLu). The models were able to predict 52 – 80% of the drugs from external validation sets within the 2-fold error of the experimental values with geometric mean fold error 1.57 – 2.00. Conclusions. Plasma protein binding was the major restrictive factor for the CL of drugs, primarily cleared by metabolism. The clearance was favored by lipophilicity and several structural features like OH-groups, aromatic rings, large hydrophobic centers, aliphatic groups, connected with electro-negative atoms, and non-substituted aromatic C-atoms. The presence of Cl-atoms and abundance of 6-member aromatic rings or fused rings had negative effect. The presence of ether O-atoms contributed negatively to the CL of both metabolism and renally excreted drugs, and urine excretion was favored by the presence of 3-valence N-atoms. These findings give insight on the main structural features governing plasma CL of basic drugs and could serve as a guide for lead optimization in the drug development process. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.
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Honey, Suresh Thareja, Manoj Kumar, and V. R. Sinha. "Self-organizing molecular field analysis of NSAIDs: Assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach." European Journal of Medicinal Chemistry 53 (July 2012): 76–82. http://dx.doi.org/10.1016/j.ejmech.2012.03.037.

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Dissertations / Theses on the topic "QSPkR"

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Gottipati, Gopichand. "PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES USING QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS) APPROACHES FOR FOUR DIFFERENT PHARMACOLOGICAL CLASSES OF COMPOUNDS." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3525.

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This research developed and validated QSPKR models for predicting in-vivo human, systemic biologically relevant PK properties (i.e., reflecting the disposition of the unbound drug) of four, preselected, pharmacological classes of drugs, namely, benzodiazepines (BZD), neuromuscular blocking agents (NMB), triptans (TRP) and class III antiarrhythmic agents (AAR), as well as PK allometric scaling (PK-AS) models for BZD and NMB, using pertinent human and animal systemic PK information (fu, CLtot, Vdss and fe) from published literature. Overall, lipophilicity (logD7.4) and molecular weight (MW) were found to be the most important and statistically significant molecular properties, affecting biologically relevant systemic PK properties, and the observed relationships were mechanistically plausible: For relatively small MW and lipophilic molecules, (e.g., BZD), an increase in logD7.4 was associated with a decrease in fu, an increase in Vdssu and CLnonrenu, suggesting the prevalence of nonspecific hydrophobic interactions with biological membranes/plasma proteins as well as hepatic partitioning/DME binding. Similar trends were observed in fu and Vdssu for intermediate to large MW, hydrophilic molecules (e.g., NMB). However, although similar trends were observed in fu and Vdssu for relatively hydrophilic, intermediate MW molecules (e.g., TRP), and a heterogeneous class (e.g., Class III AAR), logD7.4 and MW were found to be highly correlated, i.e., the indepdendent effects of logD7,4 and MW cannot be assessed NMB, TRP and Class III AAR show mechanistically diverse clearance pathways, e.g., hepatobiliary, extrahepatic, enzymatic/chemical degradation and renal excretion; therefore, effects of the logD7.4 and/or MW are note generalizable for any of the clearances across classes. PK-AS analyses showed that Vdssu and Vdss scaled well with body weight across animal species (including humans) for BZD. Overall, within the limitations of the methods (and the sample size), ‘acceptable’ predictions (i.e., within 0.5- to 2.0-fold error range) were obtained for Vdssu and Vdss for BZD (and fu correction resulted in improvement of the prediction); however, none of the CLtot predictions were acceptable, suggesting major, qualitative interspecies differences in drug metabolism, even after correcting for body weight (BW). NMB undergo little extravascular distribution owing to their relatively large MW and charged nature, and, as a result, a high percentage of acceptable predictions was obtained for Vdss (based on BW). Similarly, the prediction of CLren (based on BW and glomerular filtration rate, GFR) was acceptable, suggesting that NMB are cleared by GFR across species, and there are no interspecies differences in their tubular handling. On the other hand, CLtot (and/or CLnonren) could not be acceptably predicted by PK-AS, suggesting major differences in their clearance mechanisms across animal species.
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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
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Turner, Joseph Vernon. "Application of Artificial Neural Networks in Pharmacokinetics." University of Sydney, 2003. http://hdl.handle.net/2123/488.

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Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.
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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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Al, Tafif Abdullah. "PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC PROPERTIES BASED ON PHYSICOCHEMICAL PROPERTIES OF CALCIUM CHANNEL BLOCKERS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2868.

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This research explored quantitative relationships (QSPKR) between different molecular descriptors and pertinent, systemic PK properties for 14 calcium channel blockers (CCB). Physicochemical properties (PC) such as molecular weight (MW), molar volume (MV), calculated logP (clogP), pKa, calculated logD7.4 (clogD), % ionized at pH 6.3 and pH 7.4, hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and number of rotatable bonds (nRot) were chosen as possible predictor variables for systemic PK properties for CCB, obtained from pertinent literature, assessing the PK of CCB after intravenous administration to healthy humans. All PC properties and molecular descriptors were computed using ACD-solubility/DB 12.01. Total body clearance (CLtot), steady-state volume of distribution (Vdss), total area under the plasma concentration-time profile (AUCoo), terminal half-life (t1/2), and fraction of drug excreted unchanged in urine (fe), if available, were obtained or derived from original references, exclusively from IV studies that administered CCB to healthy human volunteers. Several articles focused on drug interactions with grapefruit juice or the impact of renal/hepatic dysfunction, and in such cases, data from the healthy control group were used. Each study was evaluated for study design, PK sampling schedule, bioanalytical and PK analysis methods before inclusion into the final database. The assumption of linear systemic PK was verified by assessing AUCoo versus (IV) dose. Plasma protein binding information was collected from in-vitro experiments to obtain the fraction unbound in plasma (fu). Unbound volume of distribution at a steady state (Vdssu), unbound total (CLtotu), renal (CLrenu), and non-renal clearance (CLnonrenu) were estimated and compared with the relevant physiological references for Vdssu (plasma volume, blood volume, extracellular and intracellular spaces, total body water and body weight) and for the unbound clearances (liver blood flow, renal plasma flow, and glomerular filtration rate, GFR). Final PK property values were obtained by averaging across available studies. The distribution of both PC and PK properties were evaluated, and correlation matrices amongst PC properties were constructed to assess for collinearity. If two PC descriptors were found to be collinear, i.e. r, ≥ 0.8, only one of them was used in the final univariate analysis. Finally, univariate linear regression of all PK variables versus each molecular descriptor was performed; any relationship with p<0.05 and r2≥0.30 was considered to be statistically significant. The PC properties of the final 14 CCB were reasonably normally distributed with few exceptions. Overall, CCBs are small (MW range of 316-496 Da), basic and lipophilic (logD7.4 range of 1.5-5.1) molecules. On the other hand, for the PK properties, the distributions were found to be skewed with high standard deviations. Thus, all PK variables (except fu) were log-transformed. Although CCB are mostly highly plasma protein bound (fu range of 0.2-20%), they are characterized by extensive extravascular tissue distribution (Vdss range of 0.6-20.4 l/kg) and high, mainly metabolic, clearance (CLtot range of 3.7-131.7 ml/min/kg). Clevidipine is the only CCB undergoing extensive, extra-hepatic ester hydrolysis, responsible for the highest CLtot value. Urinary excretion for CCB is negligible. Amlodipine is a PK outlier due to its high Vdss (20.4 l/kg) and low CLtot (6.9 ml/min/kg, due to low hepatic extraction) with fu of 2%. Therefore, the final QSPKR analysis was performed including, as well as excluding amlodipine. Excluding amlodipine, the relationship between fu and logD7.4 was negative and significant (r2 of 0.4, n=12). The relationships between CLtotu, CLnonrenu and CLrenu and logD7.4 were found to be positive and significant (r2 between 0.6-0.7, n=3-12); none of the other PC variables affected any of the clearance terms. Although the relationship between Vdssu and logD7.4 was not significant (r2 of 0.25, n=12), it showed the expected positive slope. In fact, after removing bepridil (the remaining outlier in Vdssu), the relationship with logD7.4 became statistically significant (r2=0.46, n=11). The QSPKR obtained in this study for CCB, with logD7.4 being the main PC determinant for systemic PK properties, were similar to those previously reported for opioids, β-adrenergic receptor ligands and benzodiazepines. However, slope estimates for the relationships of CLnonrenu and CLtotu as a function of logD7.4 for CCB were higher compared to these previously studied compounds, which showed higher sensitivity, most likely as a result of their higher lipophilicity. Overall, lipophilicity measured as logD7.4 was found to be a statistically significant and plausible PC determinant for the biologically relevant systemic PK properties for CCB and other classes of drugs.
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Badri, Prajakta. "PREDICTION OF HUMAN SYSTEMIC, BIOLOGICALLY RELEVANT PHARMACOKINETIC (PK) PROPERTIES BASED ON QUANTITATIVE STRUCTURE PHARMACOKINETIC RELATIONSHIPS (QSPKR) AND INTERSPECIES PHARMACOKINETIC ALLOMETRIC SCALING (PK-AS)." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/124.

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This research developed validated QSPKR and PK-AS models for predicting human systemic PK properties of three, preselected, pharmacological classes of drugs, namely opioids, β-adrenergic receptor ligands (β-ARL) and β-lactam antibiotics (β-LAs) using pertinent human and animal systemic PK properties (fu,, CLtot, Vdss, fe) and their biologically relevant unbound counterparts from the published literature, followed by an assessment of the effect of different molecular descriptors on these PK properties and on the PK-AS slopes for CLtot and Vdss from two species (rat and dog). Lipophilicity (log (D)7.4) and molecular weight (MW) were found to be the most statistically significant and biologically plausible, molecular properties affecting the biologically relevant, systemic PK properties: For compounds with log (D)7.4 > -2.0 and MW < 350 D (e.g., most opioids and β-ARL), increased log (D)7.4 resulted in decreased fu and increased Vdssu, CLtotu and CLnonrenu, indicating the prevalence of hydrophobic interactions with biological membrane/proteins. As result, the final QSPKR models using log (D)7.4 provided acceptable predictions for fu, Vdssu, CLtotu and CLnonrenu. CLnonrenu and CLtotu. For both the datasets, inclusion of drugs undergoing extrahepatic clearance worsened the QSPKR predictions. For compounds with log (D)7.4 < -2.0 and MW > 350 D (e.g., β-LA), increased MW (leading to more hydrogen bond donors/acceptors) resulted in a decrease in fu, likely indicating hydrogen bonding interactions with plasma proteins. In general, it was more difficult to predict PK parameters for β-LAs, as their Vdssu approached plasma volume and CLrenu and CLnonrenu were low - as a result of their high hydrophilicity and large MW, requiring specific drug transporters for distribution and excretion. The PK-AS analysis showed that animal body size accounted for most of the observed variability (r2> 0.80) in systemic PK variables, with single species methods, particularly those using dog, gave the best predictions. The fu correction of PK variables improved goodness of fit and predictability of human PK. There were no apparent effects of molecular properties on the predictions. CLren, CLrenu, CLnonren, and CLnonrenu were the most difficult variables to predict, possibly due to the associated interspecies differences in the metabolism, renal and hepatobiliary drug transporters.
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Tämm, Kaido. "QSPR modeling of some properties of organic compounds /." Online version, 2006. http://dspace.utlib.ee/dspace/bitstream/10062/475/5/tammkaido.pdf.

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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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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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Aguado, Ullate Sonia. "Modeling of homogeneous catalysis: from dft to qspr approaches." Doctoral thesis, Universitat Rovira i Virgili, 2012. http://hdl.handle.net/10803/79119.

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La catálisis es un campo de la ciencia que explora soluciones a los problemas ambientales como la contaminación, la eliminación de los residuos generados en el proceso de síntesis de materiales o la regeneración de los recursos naturales. En la presente Tesis, hemos reportado un estudio de cálculos DFT para la σ activación del enlace NH de amoníaco considerando las especies μ3-alquilidinos de titanio utilizando el complejo modelo [{Ti(η5-C5H5)(μ-O)}3(μ3-CH)]. Posteriormente, con el fin de analizar la hidroformilación asimétrica de estireno catalizada por complejos Rh-Binaphos, se han combinando estudios basados en la aproximación de la determinación del estado de transición y un análisis cualitativo a través de un descriptor molecular recién definido (volumen de distancia ponderada, VW). Usando nuestro conocimiento mecanicista anterior, hemos presentado un estudio QSPR para predecir la actividad y la enantioselectividad de la hidroformilación de estireno catalizada por complejos Rh-difosfinas. También, hemos desarrollado una nueva metodología 3D-QSSR para predecir la enantioselectividad basada en la cuantificación de la representación de diagramas por cuadrantes y aplicándola en el ciclopropanación asimétrica de alquenos catalizadas por complejos de cobre.
Catalysis is a field of science that explores solutions to environmental problems such as pollution, elimination of waste generated in the process of materials synthesis or regeneration of natural resources. In the present Thesis, we have reported a DFT study on the N-H σ-bond activation of ammonia by the µ3-alkylidyne titanium species using the [{Ti(η5-C5H5)(µ-O)}3(µ3-CH)] model complex. Afterwards, we have combined the TS-based approach and qualitative analysis through a newly defined molecular descriptor (distance-weighted volume, VW), in order to analyze the asymmetric hydroformylation of styrene catalyzed by Rh-binaphos complexes. Using our previous mechanistic knowledge, we have presented a QSPR study to predict the activity and the enantioselectivity in the hydroformylation of styrene catalyzed by Rh-diphosphane complexes. We have also developed a new methodology to predict enantioselectivity based on the quantitative quadrant-diagram representation of the catalysts and 3D-QSSR modeling; and we have applied it in the asymmetric cyclopropanation of alkenes catalyzed by copper complexes.
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Books on the topic "QSPkR"

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

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Diudea, Mircea V. QSPR / QSAR Studies by Molecular Descriptors. Nova Science Publishers, 2001.

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Roy, Kunal, Supratik Kar, and Rudra Narayan Das. Primer on QSAR/QSPR Modeling: Fundamental Concepts. Springer, 2015.

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(Editor), James Devillers, and Alexandru T. Balaban (Editor), eds. Topological Indices and Related Descriptors in QSAR and QSPAR. CRC, 2000.

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Bonchev, Danail, Frank Emmert-Streib, Matthias Dehmer, and Kurt Varmuza. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Wiley & Sons, Incorporated, John, 2012.

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Bonchev, Danail, Frank Emmert-Streib, Matthias Dehmer, and Kurt Varmuza. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Wiley & Sons, Incorporated, John, 2012.

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Dickenson, Eric. Evaluation of QSPR Techniques for Wastewater Treatment Processes. IWA Publishing, 2010.

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Bonchev, Danail, Frank Emmert-Streib, Matthias Dehmer, and Kurt Varmuza. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Wiley & Sons, Incorporated, John, 2012.

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Book chapters on the topic "QSPkR"

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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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Ruby-Figueroa, René. "Quantitative Structure-Property Relationships (QSPR)." In Encyclopedia of Membranes, 1705–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-44324-8_2001.

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Solov'ev, Vitaly, and Alexandre Varnek. "QSPR Models on Fragment Descriptors." In Tutorials in Chemoinformatics, 135–62. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119161110.ch9.

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Ruby-Figueroa, René. "Quantitative Structure-Property Relationships (QSPR)." In Encyclopedia of Membranes, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40872-4_2001-1.

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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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Conference papers on the topic "QSPkR"

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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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Mirajkar, K. G., A. V. Deshpande, and H. H. Budihal. "QSPR analysis of KCD coindices for some Chemical compounds." In INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS, COMPUTING AND COMMUNICATION TECHNOLOGIES: (ICAMCCT 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0070749.

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Mirajkar, K. G., A. Morajkar, and H. H. Budihal. "QSPR analysis of some chemical structures using KCD indices." In INTERNATIONAL CONFERENCE ON ADVANCES IN MATERIALS, COMPUTING AND COMMUNICATION TECHNOLOGIES: (ICAMCCT 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0070746.

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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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Oukhemanou, F., A. Maldonado, P. Moreau, and B. Creton. "Application of Quantitative Structure-property Relationship (QSPR) Method for Chemical EOR." In IOR 2013 - 17th European Symposium on Improved Oil Recovery. Netherlands: EAGE Publications BV, 2013. http://dx.doi.org/10.3997/2214-4609.20142620.

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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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Jack, Kevin, Heping Liu, Idriss Blakey, David Hill, Wang Yueh, Heidi Cao, Michael Leeson, Greg Denbeaux, Justin Waterman, and Andrew Whittaker. "The rational design of polymeric EUV resist materials by QSPR modelling." In Advanced Lithography, edited by Qinghuang Lin. SPIE, 2007. http://dx.doi.org/10.1117/12.716213.

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Vladimirova, Nadezhda, Julia Ashina, and Dmitry Kirsanov. "QSPR Modelling of Potentiometric HCO3−/Cl− Selectivity for Polymeric Membrane Sensors." In CSAC2021. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/csac2021-10621.

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Davronov, Rifkat, Bakhtiyor Rasulev, and Fatima Adilova. "Mathematical modeling of refractive index based on machine learning (kNN-QSPR) method." In 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2020. http://dx.doi.org/10.1109/aict50176.2020.9368648.

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Pham-The, Hai, Huong Le-Thi-Thu, Teresa Garrigues, Marival Bermejo, Isabel González-Álvarez, and Miguel Cabrera-Pérez. "Towards computational prediction of Biopharmaceutics Classification System: a QSPR approach." In MOL2NET, International Conference on Multidisciplinary Sciences. Basel, Switzerland: MDPI, 2015. http://dx.doi.org/10.3390/mol2net-1-b008.

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Reports on the topic "QSPkR"

1

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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