An QSAR Model for Predicting PBDEs Toxicity Established Based on Ridge Regression

2013 ◽  
Vol 663 ◽  
pp. 922-925 ◽  
Author(s):  
Yu Li ◽  
Long Jiang ◽  
Xiao Li Li ◽  
Jing Ya Wen
Keyword(s):  
Correlation Analysis ◽  
Ridge Regression ◽  
Preliminary Analysis ◽  
Qsar Model ◽  
Efficiency Coefficient ◽  
Quantitative Structure ◽  
Simulation Efficiency ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships ◽  
Polybrominated Biphenyl

In this paper, the ridge regression (RR) method was employed to establish the quantitative structure-activity relationships (QSAR) model for predicting toxicity with 15 polybrominated biphenyl ethers (PBDEs) and their 27 kinds of quantum descriptors. Quantum descriptors used to establish the QSAR model were filtrated out based on correlation analysis and variables importance of project (VIP) supported by partial least squares (PLS). The multicollinearity among the descriptors was removed during the calculation of RR method in order to ensure the validation of the final regression equation. The research showed that descriptors of Δα, αxx, αxy, αxz, αyz, βxxy and βyyy had significant effect on toxicity. The model with the simulation efficiency coefficient of 0.916 could be used to predict the toxicity of the unchecked PBDEs and as a preliminary analysis for environmental risk of organic compounds.

scholarly journals Non-Linear Quantitative Structure–Activity Relationships Modelling, Mechanistic Study and In-Silico Design of Flavonoids as Potent Antioxidants

2019 ◽  
Vol 20 (9) ◽  
pp. 2328 ◽  
Author(s):  
Petar Žuvela ◽  
Jonathan David ◽  
Xin Yang ◽  
Dejian Huang ◽  
Ming Wah Wong
Keyword(s):  
Electron Transfer ◽  
Antioxidant Activities ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Non Linear ◽  
Quantitative Structure Activity Relationships ◽  
Mean Square Errors ◽  
Proton Loss

In this work, we developed quantitative structure–activity relationships (QSAR) models for prediction of oxygen radical absorbance capacity (ORAC) of flavonoids. Both linear (partial least squares—PLS) and non-linear models (artificial neural networks—ANNs) were built using parameters of two well-established antioxidant activity mechanisms, namely, the hydrogen atom transfer (HAT) mechanism defined with the minimum bond dissociation enthalpy, and the sequential proton-loss electron transfer (SPLET) mechanism defined with proton affinity and electron transfer enthalpy. Due to pronounced solvent effects within the ORAC assay, the hydration energy was also considered. The four-parameter PLS-QSAR model yielded relatively high root mean square errors (RMSECV = 0.783, RMSEE = 0.668, RMSEP = 0.900). Conversely, the ANN-QSAR model yielded considerably lower errors (RMSEE = 0.180 ± 0.059, RMSEP1 = 0.164 ± 0.128, and RMSEP2 = 0.151 ± 0.114) due to the inherent non-linear relationships between molecular structures of flavonoids and ORAC values. Five-fold cross-validation was found to be unsuitable for the internal validation of the ANN-QSAR model with a high RMSECV of 0.999 ± 0.253; which is due to limited sample size where resampling with replacement is a considerably better alternative. Chemical domains of applicability were defined for both models confirming their reliability and robustness. Based on the PLS coefficients and partial derivatives, both models were interpreted in terms of the HAT and SPLET mechanisms. Theoretical computations based on density functional theory at ωb97XD/6-311++G(d,p) level of theory were also carried out to further shed light on the plausible mechanism of anti-peroxy radical activity. Calculated energetics for simplified models (genistein and quercetin) with peroxyl radical derived from 2,2′-azobis (2-amidino-propane) dihydrochloride suggested that both SPLET and single electron transfer followed by proton loss (SETPL) mechanisms are competitive and more favorable than HAT in aqueous medium. The finding is in good accord with the ANN-based QSAR modelling results. Finally, the strongly predictive ANN-QSAR model was used to predict antioxidant activities for a series of 115 flavonoids designed combinatorially with flavone as a template. Structural trends were analyzed, and general guidelines for synthesis of new flavonoid derivatives with potentially potent antioxidant activities were given.

QSAR and Ab Initio studies of quinolon-4(1H)-imine derivatives as antimalarial agents

2021 ◽  
Vol 4 (1) ◽  
pp. 192
Author(s):  
Jafar La Kilo ◽  
Akram La Kilo ◽  
Saprini Hamdiani
Keyword(s):  
Antimalarial Activity ◽  
Molecular Orbital Calculation ◽  
Qsar Model ◽  
Basis Set ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

Study on antimalarial activity of 22 quinolon-4(1H)-imine derivatives by using Quantitative Structure-Activity Relationships (QSAR) has been performed. Electronic and molecular descriptors were used in Quantitative Structure-Activity Relationships (QSAR) model and it was obtained from Hartree-Fock (HF) molecular orbital calculation with 6-31G basis set. QSAR analysis has been performed by multiple linear regression (MLR) method. The best equation of QSAR model on this study is: pEC50 = -4,177 + (37,902 x qC3) + (171,282 x qC8) + (9,061 x qC10) + (125,818 x qC11) + (-149,125 x qC17) + (191,623 x qC18), with statistical parameters, n = 22; r2 = 0,910; SEE = 0,171; Fcal/Ftab = 4,510 and PRESS = 0,697. The best equation can applied to design and predict new compounds with higher antimalarial activity.

scholarly journals 2D-quantitative structure–activity relationships model using PLS method for anti-malarial activities of anti-haemozoin compounds

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Phuong Thuy Viet Nguyen ◽  
Truong Van Dat ◽  
Shusaku Mizukami ◽  
Duy Le Hoang Nguyen ◽  
Farhana Mosaddeque ◽  
...  
Keyword(s):  
Distance Matrix ◽  
Qsar Model ◽  
Total Charge ◽  
Two Dimensional ◽  
Quantitative Structure ◽  
Pka Prediction ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Atomic Contribution ◽  
Quantitative Structure Activity Relationships

Abstract Background Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation. Methods With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers. Results Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity. Conclusions The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

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