scholarly journals QSPR modeling of selectivity at infinite dilution of ionic liquids

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kyrylo Klimenko ◽  
Gonçalo V. S. M. Carrera
Keyword(s):  
Ionic Liquids ◽  
Infinite Dilution ◽  
External Validation ◽  
Model Development ◽  
Structure Property ◽  
Qspr Model ◽  
Internal Validation ◽  
Mixture Separation ◽  
Qspr Modeling ◽  
Data Points

AbstractThe intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.

scholarly journals QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

2020 ◽  
Vol 69 (1-2) ◽  
pp. 1-16
Author(s):  
Hadjira Maouz ◽  
Maamar Laidi ◽  
Mabrouk Hamadache ◽  
Yamina Ammi ◽  
Salah Hanini ◽  
...  
Keyword(s):  
Average Molecular Weight ◽  
External Validation ◽  
Polymeric Materials ◽  
Structure Property ◽  
Qspr Model ◽  
Internal Validation ◽  
Structure Property Relationship ◽  
Ann Models ◽  
Comparison Of The Results ◽  
Linear Regressions

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested.

scholarly journals Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids

Materials ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2500
Author(s):  
Anna Rybińska-Fryca ◽  
Anita Sosnowska ◽  
Tomasz Puzyn
Keyword(s):  
Ionic Liquids ◽  
Molecular Descriptors ◽  
Model Development ◽  
Precise Description ◽  
Qspr Model ◽  
Crucial Step ◽  
Qsar Studies ◽  
Benchmark Study ◽  
Structure Activity ◽  
Qspr Modeling

The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model’s quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors’ selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.

Prediction of glass transition temperatures of polyacrylates from the structures of motion units

2016 ◽  
Vol 15 (02) ◽  
pp. 1650011 ◽  
Author(s):  
Xinliang Yu ◽  
Xianwei Huang
Keyword(s):  
Glass Transition ◽  
Molecular Descriptors ◽  
Amorphous Polymer ◽  
External Validation ◽  
Data Sets ◽  
Structure Property ◽  
Internal Validation ◽  
The Stability ◽  
Leave One Out ◽  
Mlr Model

The glass transition temperature [Formula: see text] is the most important parameter of an amorphous polymer. A quantitative structure-property relationship (QSPR) was developed for [Formula: see text]s of 82 polyacrylates, by applying stepwise multiple linear regression (MLR) analysis. Molecular descriptors used to describe polymer structures were, for the first time, calculated from the motion units of polymer backbones, which are chain segments with 20 carbons in length (10 repeating units). After internal validation with leave-one-out (LOO) method, external validation was carried out to test the stability of the MLR model of [Formula: see text]s. Compared to the models already published in the literature, the MLR model in this paper was accurate and acceptable, although our model was based on bigger data sets. The feasibility of calculating molecular descriptors from the motion units of polymer backbones for developing [Formula: see text] models of polyacrylates has been demonstrated.

Quantitative Relationship between Organic Molecular Structure and Infinite Dilution Activity Coefficients in 1-Ethyl-3-Methylimidazolium Tetrafluoroborate

2012 ◽  
Vol 524-527 ◽  
pp. 1848-1851
Author(s):  
Jie Ming Xiong ◽  
Chen Chen ◽  
Ming Lan Ge
Keyword(s):  
Molecular Structure ◽  
Activity Coefficients ◽  
Infinite Dilution ◽  
Energy Gap ◽  
Dipole Moments ◽  
Quantitative Relationship ◽  
Structure Property ◽  
Qspr Model ◽  
Structure Property Relationship ◽  
Correlation Parameters

Base on structural descriptors including dipole moments (μ), Energy gap (∆ε), hydration energy (∆H), and hydrophobic parameter lg P of 25 organic solutes, the quantitative structure-property relationship (QSPR) method was used to correlate the values of activity coefficients at infinite dilution, , for the solutes in ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) at 323.15 K. The result showed that the QSPR model had a good correlation and could successfully describe . The quantitative relationship between organic molecular structure and in [EMIM][BF4] was obtained and the correlation parameters were analyzed to understand the interactions that affect activity coefficients at infinite dilution.

Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies

2019 ◽  
Vol 18 (04) ◽  
pp. 1950018
Author(s):  
Tahereh Mostashari-Rad ◽  
Roya Arian ◽  
Houri Sadri ◽  
Alireza Mehridehnavi ◽  
Marzieh Mokhtari ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Cancer Metastasis ◽  
Docking Simulation ◽  
Metabolic Stability ◽  
Host Cells ◽  
Structure Property ◽  
Qspr Model ◽  
Cxcr4 Receptor ◽  
Stability And Instability ◽  
Qspr Modeling

CXCR4 is involved in inflammation, cancer metastasis and also HIV-1 entry into immune host cells. In the present research, it was decided to investigate the efficacy of some CXCR4 inhibitors from both pharmacokinetics and pharmacodynamics points of view. Quantitative structure–property relationship (QSPR) approach was applied to model the metabolic stability and instability of the compounds. Using QSPR modeling, it was tried to predict the metabolic stability using new hybrid algorithm which consisted of three different steps: descriptor reduction (PCA), stable–instable classification (KNN) and biological stability prediction (PLS). In the QSPR step, it is shown that the descriptor reduction (PCA) affects the result of the classification procedure (KNN). Besides, the obtained QSPR model can predict the metabolic stability of the stable compounds with [Formula: see text] of 0.98 for train data and of 0.64 for test data. In other words, increment and decrement of stability were followed by the model. Molecular docking simulation was exploited to define the essential interactions of an effective inhibitor with CXCR4 receptor.

scholarly journals Pengembangan alat tambal ban portable sebagai media pembelajaran siswa SMK

TAMAN VOKASI ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 76
Author(s):  
Bambang Sudarsono
Keyword(s):  
Data Collection ◽  
Research And Development ◽  
External Validation ◽  
Model Development ◽  
Vocational School ◽  
Research Subjects ◽  
School Students ◽  
Internal Validation ◽  
Student Responses

Penelitian ini bertujuan untuk mengembangkan alat tambal ban portable yang layak digunakan sebagai media pembelajaran perbaikan ban. Desain penelitian ini menggunakan desain penelitian pengembangan (R&D) yang diadopsi dari Richey and Klein dengan tahapan pengembangan model, validasi internal dan validasi eksternal. Subyek penelitian yang digunakan adalah 4 mekanik dan siswa SMK Kompetensi Keahlian Teknik Sepeda Montor (TSM) yang berjumlah 70 siswa. Sedangkan obyek penelitian dilaksanakan di Astra Honda Yogyakarta, Yamaha Mataram Sakti dan SMK Muhammadiyah Salam. Teknik pengumpulan data menggunakan angket media dan angat tanggapan siswa. Hasil penelitian menunjukkan bahwa alat tambal ban portable layak digunakan dengan hasil validasi produk dari Astra Honda 90,67%. dan 88,78% dari Yamaha. Tanggapan siswa menunjukkan hasil bahwa siswa sangat setuju Alat Tambal Ban Portable menjadi media pembelajaran dengan skor 4,08. ABSTRACTThis study aims to develop a portable tire patching device which is suitable as a learning media for tire repairing. The design of this study used a research and development (R&D) design adopted from Richey and Klein with the stages of model development, internal validation and external validation. The research subjects were 4 mechanics and 70 vocational school students major in motorcycle engineering. While the object of research was carried out at Astra Honda Yogyakarta, Yamaha Mataram Sakti and SMK Muhammadiyah Salam. The data collection techniques used media questionnaires and the response of students. The results showed that portable tire patching tool was suitable to use with the product validation result from Astra Honda 90.67%. and 88.78% of Yamaha. The student responses showed the results that students strongly agreed Portable Tire Patching Tool became learning media with a score of 4.08.

scholarly journals Predicting Sex from Retinal Fundus Photographs Using Automated Deep Learning

2021 ◽  
Author(s):  
Edward Korot ◽  
Nikolas Pontikos ◽  
Xiaoxuan Liu ◽  
Siegfried K Wagner ◽  
Livia Faes ◽  
...  
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Development ◽  
Model Performance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Fundus Photographs ◽  
The Uk ◽  
Retinal Fundus

Abstract Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.

scholarly journals Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study

2017 ◽  
Vol 21 (18) ◽  
pp. 1-100 ◽  
Author(s):  
Shakila Thangaratinam ◽  
John Allotey ◽  
Nadine Marlin ◽  
Ben W Mol ◽  
Peter Von Dadelszen ◽  
...  
Keyword(s):  
Early Onset ◽  
Prediction Models ◽  
Antihypertensive Drugs ◽  
External Validation ◽  
Model Development ◽  
Maternal Complications ◽  
Data Set ◽  
Internal Validation ◽  
Development Data ◽  
S Model

BackgroundThe prognosis of early-onset pre-eclampsia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women.ObjectiveTo develop and validate prediction models for outcomes in early-onset pre-eclampsia.DesignProspective cohort for model development, with validation in two external data sets.SettingModel development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies.ParticipantsPregnant women with early-onset pre-eclampsia.Sample sizeNine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets.PredictorsThe predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey.Main outcome measuresThe primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications.AnalysisWe developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope).ResultsThe PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with anoptimism-adjustedc-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with ac-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had ac-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications.ConclusionsThe PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high- or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.Trial registrationCurrent Controlled Trials ISRCTN40384046.FundingThe National Institute for Health Research Health Technology Assessment programme.

QSPR Modeling For Critical Temperatures Of Organic Compounds Using Hybrid Optimal Descriptors

2019 ◽  
Vol 4 (4) ◽  
pp. 15-26
Author(s):  
Khalid Bouhedjar ◽  
Abdelmalek Khorief Nacereddine ◽  
Hamida Ghorab ◽  
Abdelhafid Djerourou
Keyword(s):  
Organic Compounds ◽  
High Speed ◽  
External Validation ◽  
Model Performance ◽  
Predictive Ability ◽  
Structure Property ◽  
Critical Temperatures ◽  
Quantitative Structure Property Relationships ◽  
Qspr Modeling ◽  
External Test

The simplified molecular input line entry system (SMILES) is particularly suitable for high-speed machine processing, based on the Monte Carlo method using CORAL software. Quantitative structure-property relationships (QSPR) of critical temperatures have been established using a dataset of 165 diverse organic compounds employing hybrid optimal descriptors defined by graph and SMILES notation. External validation is one of the most important parts in the evaluation of model performance. However, previous models on the same dataset have poor predictive power in the external test set, or the authors had not done that check. In the present work, the predictive ability of model has been tested using external validation. The statistical quality of the three splits are similar and good. The r2 values for the best model are: r2 = 0.98 for the training set, r2 = 0.95 for the calibration set, and r2 = 0.94 for the validation set.

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