scholarly journals Quantitative Structure-Activity Relationship Study of Antioxidant Tripeptides Based on Model Population Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 995 ◽  
Author(s):  
Baichuan Deng ◽  
Hongrong Long ◽  
Tianyue Tang ◽  
Xiaojun Ni ◽  
Jialuo Chen ◽  
...  
Keyword(s):  
Amino Acid ◽  
Population Analysis ◽  
Prediction Performance ◽  
Quantitative Structure ◽  
Antioxidant Peptides ◽  
Model Population ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Qsar Models ◽  
Amino Acid Descriptors

Due to their beneficial effects on human health, antioxidant peptides have attracted much attention from researchers. However, the structure-activity relationships of antioxidant peptides have not been fully understood. In this paper, quantitative structure-activity relationships (QSAR) models were built on two datasets, i.e., the ferric thiocyanate (FTC) dataset and ferric-reducing antioxidant power (FRAP) dataset, containing 214 and 172 unique antioxidant tripeptides, respectively. Sixteen amino acid descriptors were used and model population analysis (MPA) was then applied to improve the QSAR models for better prediction performance. The results showed that, by applying MPA, the cross-validated coefficient of determination (Q2) was increased from 0.6170 to 0.7471 for the FTC dataset and from 0.4878 to 0.6088 for the FRAP dataset, respectively. These findings indicate that the integration of different amino acid descriptors provide additional information for model building and MPA can efficiently extract the information for better prediction performance.

scholarly journals Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

Nanoscale ◽  
2016 ◽  
Vol 8 (13) ◽  
pp. 7203-7208 ◽  
Author(s):  
Natalia Sizochenko ◽  
Agnieszka Gajewicz ◽  
Jerzy Leszczynski ◽  
Tomasz Puzyn
Keyword(s):  
Causal Inference ◽  
Quality Evaluation ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Validation Criteria ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships ◽  
Inference Methods

In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure–Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model.

scholarly journals Validation of QSAR models for legislative purposes

2009 ◽  
Vol 2 (3) ◽  
pp. 184-186 ◽  
Author(s):  
Miloň Tichý ◽  
Marián Rucki
Keyword(s):  
Oecd Countries ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Oecd Principles ◽  
The World ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

Validation of QSAR models for legislative purposesOECD principles of validation of Quantitative Structure - Activity Relationships (QSAR) models for legislative purposes are given and explained. Reasons of their origination and development, like system REACH, are described. A basic impulse has come from some OECD countries followed by all (almost) other countries of the world.

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

2017 ◽  
Vol 16 (05) ◽  
pp. 1750038 ◽  
Author(s):  
Abolfazl Barzegar ◽  
Hossein Hamidi
Keyword(s):  
Therapeutic Agents ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Qsar Models ◽  
Stepwise Procedure ◽  
Quantitative Structure Activity Relationships ◽  
Anti Hiv ◽  
Hiv 1 ◽  
Mlr Model

Human immunodeficiency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Different quantitative structure–activity relationships (QSARs) algorithms have been used in order to develop efficient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four different approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA–MLR and stepwise PCA–MLR were utilized for this aim. Twenty two different sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the “concrete” and “stepwise” procedure in two different algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA–MLR model showed that the method has higher ability to predict the compounds’ activity. The stepwise PCA–MLR model showed highly validated statistical results both in fitting and prediction processes ([Formula: see text] and [Formula: see text]). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining efficient descriptors for building good predictability stepwise PCA–MLR. The stepwise hybrid approach of PCA–MLR may be useful in derivation of highly predictive and interpretable QSAR models.

scholarly journals Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure–activity relationship

2017 ◽  
Vol 4 (9) ◽  
pp. 170516 ◽  
Author(s):  
Hui Wang ◽  
Mingyue Jiang ◽  
Shujun Li ◽  
Chung-Yun Hse ◽  
Chunde Jin ◽  
...  
Keyword(s):  
Schiff Base ◽  
Amino Acid ◽  
Antifungal Activity ◽  
Fungal Infections ◽  
Atomic Orbital ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models ◽  
Amino Acid Schiff Base

Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure–activity relationships (QSARs) for CAAS compounds against Aspergillus niger ( A. niger ) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models ( R 2  = 0.9346 for A. niger , R 2  = 0.9590 for P. citrinum, ) were constructed and validated. The models indicated that the molecular polarity and the Max atomic orbital electronic population had a significant effect on antifungal activity. Based on the best QSAR models, two new compounds were designed and synthesized. Antifungal activity tests proved that both of them have great bioactivity against the selected fungi.

Quantitative Structure-Activity Relationship Study of Radical Scavenging Peptides Based on Orac Method by Using Different Sets of Amino Acids Descriptor

2011 ◽  
Vol 365 ◽  
pp. 169-179 ◽  
Author(s):  
Yao Wang Li ◽  
Bo Li
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Radical Scavenging Activity ◽  
Radical Scavenging ◽  
Predictive Ability ◽  
Peptide Sequence ◽  
Quantitative Structure ◽  
Least Squares Regression ◽  
Structure Activity ◽  
Amino Acid Descriptors

Some radical scavenging peptides by ORAC method from different hydrolysates were used for the quantitative structure-activity relationships (QSAR) research. Partial least-squares regression analysis (PLSR) was treated as the method to build the model with 17 kinds of amino acid descriptors. In order to translate the sequence to the same length, two-terminal position numbering (TTPN) was applied. Two of amino acid descriptors VSHE and VSW were selected for their excellent performance (R2, Q2, and RMSEcwith VHSE and VSW descriptor are 0.995, 0.630, 0.318 and 0.966, 0.543, 0.181 respectively). VHSE has the definite physicochemical meanings and easy to understand while VSW has good predictive ability (Rand RMSEpwith VHSE and VSW are 0.404, 2.633 and 0.635, 2.298 respectively). It is believed that the position No.2 amino acid from N-terminal (N2) have more importance than others in sequence, and most of electronic properties are negative to activity while all the steric properties are positive to activity as well as the hydrophobic properties. The suitable amino acids in sequence are as follow: G, R, K, W, Y, N, E, H, and Q are suitable for N2position which illustrated the importance of acidic amino acids in peptide sequence for radical scavenging activity.

Sign in / Sign up

Export Citation Format

Share Document