scholarly journals Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5779
Author(s):  
Amit Kumar Halder ◽  
Reza Haghbakhsh ◽  
Iuliia V. Voroshylova ◽  
Ana Rita C. Duarte ◽  
M. Natalia D. S. Cordeiro
Keyword(s):  
Large Scale ◽  
Predictive Accuracy ◽  
External Validation ◽  
Deep Eutectic Solvents ◽  
Structural Features ◽  
Industrial Applications ◽  
Structure Property ◽  
Consensus Prediction ◽  
Validation Set ◽  
Set Up

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

scholarly journals QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Medicines ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Junko Nagai ◽  
Mai Imamura ◽  
Hiroshi Sakagami ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Anticancer Drugs ◽  
Predictive Accuracy ◽  
Cell Cancer ◽  
External Validation ◽  
Selective Toxicity ◽  
Normal Cells ◽  
Validation Set ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.

scholarly journals An adapted neural-fuzzy inference system model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in patients after heart valve replacement

2021 ◽  
Author(s):  
Zhi-Chun Gu ◽  
Shou-Rui Huang ◽  
Dong Li ◽  
Qin Zhou ◽  
Jing Wang ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
External Validation ◽  
High Dose ◽  
Internal Validation ◽  
Inference System ◽  
Dose Warfarin ◽  
The Mean ◽  
Balanced Model ◽  
Validation Set

Abstract Background Tailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and huge inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). Methods This retrospective study enrolled patients who underwent HVR between June 1, 2012 and June 1, 2016 from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose, and the models’ predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general liner model and created two ANFIS models using imbalanced and balanced training sets. We finally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Stratified analyses were conducted across warfarin doses (low, medium, and high doses). Results A total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2,820 in the balanced training set; 1,511 in the internal validation set; and 1,511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (< 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage: 74.39–78.16%, MAE: 0.37 mg/daily, MSE: 0.39 mg/daily) and the balanced model (ideal prediction percentage: 73.46–75.31%, MAE: 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a significantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P < 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort confirmed this finding. Conclusions The ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low- and high-dose warfarin groups.

scholarly journals Estimating the Adsorption Efficiency of Sugar-Based Surfactants From QSPR Models

2019 ◽  
Vol 4 (2) ◽  
pp. 28-51
Author(s):  
Théophile Gaudin ◽  
Patricia Rotureau ◽  
Isabelle Pezron ◽  
Guillaume Fayet
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Cost Effective ◽  
Adsorption Efficiency ◽  
Structure Property ◽  
Surfaces And Interfaces ◽  
Structure Property Relationship ◽  
Different Types ◽  
Validation Set ◽  
Effective Use

Adsorption efficiency, measured as the surfactant concentration at which the surface tension of the aqueous solution decreases by 20 mN/m, characterizes their affinity for surfaces and interfaces, which is crucial for a cost-effective use of surfactants. In this article, the first Quantitative Structure-Property Relationship models to predict this efficiency were proposed based on a dataset of 82 diverse sugar-based surfactants and using different types of molecular descriptors. Finally, an easy-to-use model was evidenced with good predictivity assessed on an external validation set. Moreover, it is based on a series of fragment descriptors accounting for the different structural trends affecting the efficiency of sugar-based surfactants. Due to its predictive capabilities and to the structure-property trends it involves, this model opens perspectives to help the design of new sugar-based surfactants, notably to substitute petroleum-based ones.

scholarly journals Pyrrolidinium salt based binary and ternary deep eutectic solvents: green preparations and physiochemical property characterizations

2018 ◽  
Vol 7 (4) ◽  
pp. 353-359 ◽  
Author(s):  
Jing Wang ◽  
Sheila N. Baker
Keyword(s):  
Melting Point ◽  
Large Scale ◽  
Deep Eutectic Solvent ◽  
Deep Eutectic Solvents ◽  
Industrial Applications ◽  
Economic Feasibility ◽  
Preparation Process ◽  
Green Solvents ◽  
Liquid Form ◽  
Reaction Media

Abstract Ionic liquids (ILs) are considered to be green solvents for various applications. However, their synthesis via chemical reaction with by-products or waste produced is contradictory to the concept of green chemistry, and the purity problem and economic feasibility limit their applications in some large-scale industrial applications. 1-Butyl-1-methylpyrrolidinium bromide ([bmpy][Br]), which is a molten salt with melting point above 100°C is a precursor of pyrrolidinium ILs, but hardly can be put under the category of IL because of its high melting point. In this study, [bmpy][Br] based binary deep eutectic solvent (BDES) and ternary deep eutectic solvent (TDES) were synthesized to prepare [bmpy][Br] in liquid form. During the preparation process, no reaction media was employed, no by-product was generated, and no further purification was required, thereby making it a completely green process. The prepared TDES has better thermal stability and larger free volume than BDES, which is potentially useful for sorption applications with high temperature requirement. It is also because of the green preparation process that the TDES is also expected to be capable for the large-scale industrial applications. This work is opening up new avenues for the study of binary and ternary IL-DES system and their applications.

scholarly journals Development and external validation of a nomogram predicting overall survival after curative resection of colon cancer

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110150
Author(s):  
Shuanhu Wang ◽  
Yakui Liu ◽  
Yi Shi ◽  
Jiajia Guan ◽  
Mulin Liu ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Overall Survival ◽  
Curative Resection ◽  
Predictive Accuracy ◽  
External Validation ◽  
Tnm Staging ◽  
Cox Proportional Hazards ◽  
Calibration Plot ◽  
Metastatic Colon Cancer ◽  
Validation Set

Objective To develop and externally validate a prognostic nomogram to predict overall survival (OS) in patients with resectable colon cancer. Methods Data for 50,996 patients diagnosed with non-metastatic colon cancer were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were assigned randomly to the training set (n = 34,168) or validation set (n = 16,828). Independent prognostic factors were identified by multivariate Cox proportional hazards regression analysis and used to construct the nomogram. Harrell’s C-index and calibration plots were calculated using the SEER validation set. Additional external validation was performed using a Chinese dataset (n = 342). Results Harrell’s C-index of the nomogram for OS in the SEER validation set was 0.71, which was superior to that using the 7th edition of the American Joint Committee on Cancer TNM staging (0.59). Calibration plots showed consistency between actual observations and predicted 1-, 3-, and 5-year survival. Harrell’s C-index (0.72) and calibration plot showed excellent predictive accuracy in the external validation set. Conclusions We developed a nomogram to predict OS after curative resection for colon cancer. Validation using the SEER and external datasets revealed good discrimination and calibration. This nomogram may help predict individual survival in patients with colon cancer.

scholarly journals Prediction of polyproline II secondary structure propensity in proteins

2020 ◽  
Vol 7 (1) ◽  
pp. 191239 ◽  
Author(s):  
Kevin T. O’Brien ◽  
Catherine Mooney ◽  
Cyril Lopez ◽  
Gianluca Pollastri ◽  
Denis C. Shields
Keyword(s):  
Secondary Structure ◽  
Large Scale ◽  
External Validation ◽  
Three Dimensional ◽  
Binding Domains ◽  
Left Handed ◽  
Polyproline Ii ◽  
Polyproline Ii Helix ◽  
Binding Peptides ◽  
Validation Set

Background: The polyproline II helix (PPIIH) is an extended protein left-handed secondary structure that usually but not necessarily involves prolines. Short PPIIHs are frequently, but not exclusively, found in disordered protein regions, where they may interact with peptide-binding domains. However, no readily usable software is available to predict this state. Results: We developed PPIIPRED to predict polyproline II helix secondary structure from protein sequences, using bidirectional recurrent neural networks trained on known three-dimensional structures with dihedral angle filtering. The performance of the method was evaluated in an external validation set. In addition to proline, PPIIPRED favours amino acids whose side chains extend from the backbone (Leu, Met, Lys, Arg, Glu, Gln), as well as Ala and Val. Utility for individual residue predictions is restricted by the rarity of the PPIIH feature compared to structurally common features. Conclusion: The software, available at http://bioware.ucd.ie/PPIIPRED , is useful in large-scale studies, such as evolutionary analyses of PPIIH, or computationally reducing large datasets of candidate binding peptides for further experimental validation.

scholarly journals A simple 2D-QSPR model for the prediction of Setschenow constants of organic compounds

2016 ◽  
Vol 35 (1) ◽  
pp. 53 ◽  
Author(s):  
Qi Xu ◽  
Lingling Fan ◽  
Jie Xu
Keyword(s):  
Organic Compounds ◽  
External Validation ◽  
Structure Property ◽  
Training Set ◽  
Multilinear Regression ◽  
Qspr Model ◽  
Multilinear Regression Analysis ◽  
Validation Set ◽  
Reliability And Robustness ◽  
Leave One Out

A quantitative structure-property relationship (QSPR) analysis of the Setschenow constants (Ksalt) of organic compounds in a sodium chloride solution was carried out using only two-dimensional (2D) descriptors as input parameters. The whole set of 101 compounds was split into a training set of 71 compounds and a validation set of 30 compounds by means of the Kennard and Stones algorithm. A general four-parameter equation, with correlation coefficient (R) of 0.887 and standard error of estimation (s) of 0.031, was obtained by stepwise multilinear regression analysis (MLRA) on the training set. The reliability and robustness of the present model was verified with leave-one-out cross-validation, randomization tests, and the external validation set. All of the descriptors contained in this model are calculated directly from the molecular 2D structures; thus, this model can be used to easily predict the Ksalt of other compounds not involved in the present dataset.

Platelet Anti-Aggregating Activity and Tolerance of Clopidogrel in Atherosclerotic Patients

1996 ◽  
Vol 76 (06) ◽  
pp. 0939-0943 ◽  
Author(s):  
B Boneu ◽  
G Destelle ◽  
Keyword(s):  
Platelet Aggregation ◽  
Large Scale ◽  
Bleeding Time ◽  
Antithrombotic Activity ◽  
Ambulatory Patients ◽  
Patients At Risk ◽  
Set Up ◽  
Ischemic Events ◽  
Large Scale Clinical Trial ◽  
Selection Of

SummaryThe anti-aggregating activity of five rising doses of clopidogrel has been compared to that of ticlopidine in atherosclerotic patients. The aim of this study was to determine the dose of clopidogrel which should be tested in a large scale clinical trial of secondary prevention of ischemic events in patients suffering from vascular manifestations of atherosclerosis [CAPRIE (Clopidogrel vs Aspirin in Patients at Risk of Ischemic Events) trial]. A multicenter study involving 9 haematological laboratories and 29 clinical centers was set up. One hundred and fifty ambulatory patients were randomized into one of the seven following groups: clopidogrel at doses of 10, 25, 50,75 or 100 mg OD, ticlopidine 250 mg BID or placebo. ADP and collagen-induced platelet aggregation tests were performed before starting treatment and after 7 and 28 days. Bleeding time was performed on days 0 and 28. Patients were seen on days 0, 7 and 28 to check the clinical and biological tolerability of the treatment. Clopidogrel exerted a dose-related inhibition of ADP-induced platelet aggregation and bleeding time prolongation. In the presence of ADP (5 \lM) this inhibition ranged between 29% and 44% in comparison to pretreatment values. The bleeding times were prolonged by 1.5 to 1.7 times. These effects were non significantly different from those produced by ticlopidine. The clinical tolerability was good or fair in 97.5% of the patients. No haematological adverse events were recorded. These results allowed the selection of 75 mg once a day to evaluate and compare the antithrombotic activity of clopidogrel to that of aspirin in the CAPRIE trial.

Probing the Structural Features and the Micro-heterogeneity of Various Deep Eutectic Solvents and their Water Dilutions by the Photophysical Behaviour of Two Fluorophores

2020 ◽  
Author(s):  
Matteo Tiecco ◽  
Irene Di Guida ◽  
Pier Luigi Gentili ◽  
Raimondo Germani ◽  
Carmela Bonaccorso ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Transient Absorption ◽  
Photophysical Properties ◽  
Deep Eutectic Solvents ◽  
Structural Features ◽  
Solvation Dynamics ◽  
Structured Systems ◽  
Reduced Viscosity ◽  
Bond Network ◽  
Fluorescent Molecules

<div><div><div><p>The structural features of a series of diverse Deep Eutectic Solvents (DESs) have been investigated and characterized by means of two fluorescent probes. The spectral and photophysical properties of the latter are strictly dependent on the experienced environment, so that they can provide insights into the polarity, viscosity, hydrogen-bond network, and micro-heterogeneity of the various DESs.</p><p>In fact, the investigated DESs exhibit a variety of properties with regards to their hydrophilicity, acidity, and hydrogen-bond ability, and these details were deeply probed by the two fluorescent molecules. The effect of the addition of water, which is a key strategy for tuning the properties of these structured systems, was also tested. In particular, the excited state dynamics of the probes, measured by femtosecond-resolved transient absorption, proved instrumental in understanding the changes in the structural properties of the DESs, namely reduced viscosity and enhanced heterogeneity, as the water percentage increases. Differences between the various DESs in terms of both local microheterogeneity and bulk viscosity also emerged from the peculiar multi-exponential solvation dynamics undergone by the excited states of the probes.</p></div></div></div>

Probing the Structural Features and the Micro-heterogeneity of Various Deep Eutectic Solvents and their Water Dilutions by the Photophysical Behaviour of Two Fluorophores

2020 ◽  
Author(s):  
Matteo Tiecco ◽  
Irene Di Guida ◽  
Pier Luigi Gentili ◽  
Raimondo Germani ◽  
Carmela Bonaccorso ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Transient Absorption ◽  
Photophysical Properties ◽  
Deep Eutectic Solvents ◽  
Structural Features ◽  
Solvation Dynamics ◽  
Structured Systems ◽  
Reduced Viscosity ◽  
Bond Network ◽  
Fluorescent Molecules

<div><div><div><p>The structural features of a series of diverse Deep Eutectic Solvents (DESs) have been investigated and characterized by means of two fluorescent probes. The spectral and photophysical properties of the latter are strictly dependent on the experienced environment, so that they can provide insights into the polarity, viscosity, hydrogen-bond network, and micro-heterogeneity of the various DESs.</p><p>In fact, the investigated DESs exhibit a variety of properties with regards to their hydrophilicity, acidity, and hydrogen-bond ability, and these details were deeply probed by the two fluorescent molecules. The effect of the addition of water, which is a key strategy for tuning the properties of these structured systems, was also tested. In particular, the excited state dynamics of the probes, measured by femtosecond-resolved transient absorption, proved instrumental in understanding the changes in the structural properties of the DESs, namely reduced viscosity and enhanced heterogeneity, as the water percentage increases. Differences between the various DESs in terms of both local microheterogeneity and bulk viscosity also emerged from the peculiar multi-exponential solvation dynamics undergone by the excited states of the probes.</p></div></div></div>

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