The quantitative structure-activity relationships between GABAA receptor and ligands based on binding interface characteristic

Background: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. Objective: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. Method: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand-receptor interaction characteristics. Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03 respectively. Conclusion : We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

A Quantitative Structure-activity Relationships Model Based on Hybrid Artificial Intelligence Methods and its Application

10.20944/preprints201805.0475.v1 ◽

2018 ◽

Author(s):

Mengshan Li ◽

Huaijin Zhang ◽

Liang Liu ◽

Bingsheng Chen ◽

Lixin Guan ◽

...

Keyword(s):

Molecular Descriptors ◽

Chemical Properties ◽

Qsar Model ◽

Particle Swarm Algorithm ◽

Accelerate Particle ◽

Quantitative Structure ◽

Ann Model ◽

Qsar Studies ◽

Model Based ◽

Quantitative structure-activity relationship (QSAR) model is adopted to study the relationship between the chemical and physical properties of various substances and the structure. Through QSAR studies, the internal relationship between the invisible structure and the activity can be obtained. In this paper, a novel chaos-enhanced accelerate particle swarm algorithm (CAPSO) is proposed, which is used to molecular descriptors screening and optimization of the weights of back propagation artificial neural network (BP ANN). Then, the QSAR model based on CAPSO and BP ANN is put forward, hereinafter referred to as CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm is a reliable method for screening molecular descriptors and the five molecular descriptors obtained by CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed the CAPSO BP ANN model has a good performance in predicting the pKa values of various compounds, the absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the higher prediction accuracy and correlation. The proposed hybrid intelligent model can be applied in all kinds of engineering design, prediction of physical and chemical properties and intelligent calculation.

Design of New Potent Insecticides of Organophosphate Derivatives Based on QSAR Analysis

Indonesian Journal of Chemistry ◽

10.22146/ijc.21331 ◽

2013 ◽

Vol 13 (1) ◽

pp. 86-93 ◽

Cited By ~ 2

Author(s):

Mudasir Mudasir ◽

Yari Mukti Wibowo ◽

Harno Dwi Pranowo

Keyword(s):

Sulfonic Acid ◽

Molecular Descriptors ◽

Qsar Model ◽

Quantitative Structure ◽

Benzene Sulfonic Acid ◽

Qsar Analysis ◽

Structure Activity ◽

Qsar Models ◽

New Compounds

Design of new potent insecticide compounds of organophosphate derivatives based on QSAR (Quantitative Structure-Activity Relationship) analytical model has been conducted. Organophosphate derivative compounds and their activities were obtained from the literature. Computational modeling of the structure of organophosphate derivative compounds and calculation of their QSAR descriptors have been done by AM1 (Austin Model 1) method. The best QSAR model was selected from the QSAR models that used only electronic descriptors and from those using both electronic and molecular descriptors. The best QSAR model obtained was:Log LD50 = 50.872 - 66.457 qC1 - 65.735 qC6 + 83.115 qO7 (n = 30, r = 0.876, adjusted r2 = 0.741, Fcal/Ftab = 9.636, PRESS = 2.414 x 10-6)The best QSAR model was then used to design in silico new compounds of insecticide of organophosphate derivatives with better activity as compared to the existing synthesized organophosphate derivatives. So far, the most potent insecticide of organophosphate compound that has been successfully synthesized had log LD50 of -5.20, while the new designed compound based on the best QSAR model, i.e.: 4-(diethoxy phosphoryloxy) benzene sulfonic acid, had log LD50 prediction of -7.29. Therefore, the new designed insecticide compound is suggested to be synthesized and tested for its activity in laboratory for further verification.

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

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1389557520666200429102334 ◽

2020 ◽

Vol 20 (14) ◽

pp. 1375-1388 ◽

Cited By ~ 2

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 ◽

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.

Studies on the EC50 of Natural Monoterpenes as Fungal Inhibitors with Quantitative Structure-Activity Relationships (QSARs)

The Natural Products Journal ◽

10.2174/2210315509666190117150153 ◽

2020 ◽

Vol 10 (1) ◽

pp. 44-60

Author(s):

Mohamed E.I. Badawy ◽

Entsar I. Rabea ◽

Samir A.M. Abdelgaleil

Keyword(s):

Biological Activity ◽

Plant Pathogens ◽

Molecular Descriptors ◽

Microbial Pathogens ◽

Quantitative Structure ◽

Qsar Study ◽

Qsar Analysis ◽

Pharmacophore Modelling ◽

Structure Activity ◽

Wide Range

Background:Monoterpenes are the main constituents of the essential oils obtained from plants. These natural products offered wide spectra of biological activity and extensively tested against microbial pathogens and other agricultural pests.Methods:Antifungal activity of 10 monoterpenes, including two hydrocarbons (camphene and (S)- limonene) and eight oxygenated hydrocarbons ((R)-camphor, (R)-carvone, (S)-fenchone, geraniol, (R)-linalool, (+)-menthol, menthone, and thymol), was determined against fungi of Alternaria alternata, Botrytis cinerea, Botryodiplodia theobromae, Fusarium graminearum, Phoma exigua, Phytophthora infestans, and Sclerotinia sclerotiorum by the mycelia radial growth technique. Subsequently, Quantitative Structure-Activity Relationship (QSAR) analysis using different molecular descriptors with multiple regression analysis based on systematic search and LOOCV technique was performed. Moreover, pharmacophore modelling was carried out using LigandScout software to evaluate the common features essential for the activity and the hypothetical geometries adopted by these ligands in their most active forms.Results:The results showed that the antifungal activities were high, but depended on the chemical structure and the type of microorganism. Thymol showed the highest effect against all fungi tested with respective EC50 in the range of 10-86 mg/L. The QSAR study proved that the molecular descriptors HBA, MR, Pz, tPSA, and Vp were correlated positively with the biological activity in all of the best models with a correlation coefficient (r) ≥ 0.98 and cross-validated values (Q2) ≥ 0.77.Conclusion:The results of this work offer the opportunity to choose monoterpenes with preferential antimicrobial activity against a wide range of plant pathogens.

1P-047 Comparison of Pseudo Structure Activity Relationship (PSAR) Model with quantitative structure activity relationship (QSAR) model(The 46th Annual Meeting of the Biophysical Society of Japan)

Seibutsu Butsuri ◽

10.2142/biophys.48.s28_2 ◽

2008 ◽

Vol 48 (supplement) ◽

pp. S28

Author(s):

Hiroaki Fukunishi ◽

Reiji Teramoto ◽

Jiro Shimada

Keyword(s):

Annual Meeting ◽

Qsar Model ◽

Structure Activity Relationship ◽

Activity Relationship ◽

Quantitative Structure ◽

Structure Activity ◽

Biophysical Society

Development of a new quantitative structure–activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™

Genes and Environment ◽

10.1186/s41021-021-00182-6 ◽

2021 ◽

Vol 43 (1) ◽

Author(s):

Toshio Kasamatsu ◽

Airi Kitazawa ◽

Sumie Tajima ◽

Masahiro Kaneko ◽

Kei-ichi Sugiyama ◽

...

Keyword(s):

Molecular Weight ◽

Ames Test ◽

Qsar Model ◽

Structure Activity Relationship ◽

Activity Relationship ◽

Low Molecular Weight ◽

Quantitative Structure ◽

Industrial Chemicals ◽

Abstract Background Food flavors are relatively low molecular weight chemicals with unique odor-related functional groups that may also be associated with mutagenicity. These chemicals are often difficult to test for mutagenicity by the Ames test because of their low production and peculiar odor. Therefore, application of the quantitative structure–activity relationship (QSAR) approach is being considered. We used the StarDrop™ Auto-Modeller™ to develop a new QSAR model. Results In the first step, we developed a new robust Ames database of 406 food flavor chemicals consisting of existing Ames flavor chemical data and newly acquired Ames test data. Ames results for some existing flavor chemicals have been revised by expert reviews. We also collected 428 Ames test datasets for industrial chemicals from other databases that are structurally similar to flavor chemicals. A total of 834 chemicals’ Ames test datasets were used to develop the new QSAR models. We repeated the development and verification of prototypes by selecting appropriate modeling methods and descriptors and developed a local QSAR model. A new QSAR model “StarDrop NIHS 834_67” showed excellent performance (sensitivity: 79.5%, specificity: 96.4%, accuracy: 94.6%) for predicting Ames mutagenicity of 406 food flavors and was better than other commercial QSAR tools. Conclusions A local QSAR model, StarDrop NIHS 834_67, was customized to predict the Ames mutagenicity of food flavor chemicals and other low molecular weight chemicals. The model can be used to assess the mutagenicity of food flavors without actual testing.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

Research Journal of Biotechnology ◽

10.25303/1610rjbt5058 ◽

2021 ◽

Vol 16 (10) ◽

pp. 50-58

Author(s):

Ali Qusay Khalid ◽

Vasudeva Rao Avupati ◽

Husniza Hussain ◽

Tabarek Najeeb Zaidan

Keyword(s):

Dengue Fever ◽

Three Dimensional ◽

3D Qsar ◽

Qsar Model ◽

Activity Relationship ◽

Quantitative Structure ◽

Contour Maps ◽

Structure Activity ◽

Bioactive Ligands

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.

Using nano-QSAR to determine the most responsible factor(s) in gold nanoparticle exocytosis

RSC Advances ◽

10.1039/c5ra06198a ◽

2015 ◽

Vol 5 (70) ◽

pp. 57030-57037 ◽

Cited By ~ 21

Author(s):

Arafeh Bigdeli ◽

Mohammad Reza Hormozi-Nezhad ◽

Hadi Parastar

Keyword(s):

Gold Nanoparticles ◽

Gold Nanoparticle ◽

Qsar Model ◽

Structure Activity Relationship ◽

Activity Relationship ◽

Quantitative Structure ◽

Structure Activity ◽

Determining Factors

A nano-quantitative structure-activity relationship (nano-QSAR) model is proposed to indicate the determining factors responsible in the exocytosis of gold nanoparticles in macrophages.

Conceptual Framework for the Conservation of Natural Environment from Toxic Ionic Liquids by QSAR Model

E3S Web of Conferences ◽

10.1051/e3sconf/202128703007 ◽

2021 ◽

Vol 287 ◽

pp. 03007

Author(s):

Muhammad Ishaq Khan ◽

Dzulkarnain Zaini ◽

Azmi Mohd Shariff

Keyword(s):

Ionic Liquids ◽

Natural Environment ◽

Daily Life ◽

Qsar Model ◽

Good Choice ◽

Ionic Structure ◽

Quantitative Structure ◽

Imidazolium Ionic Liquids ◽

The natural environment has been affected by human activities to fulfil daily life needs. Abundance and hazardousness of the chemicals including ionic liquids is one of the most challenging aspect to be handled by human as well as for the natural environment. Due to ionic structure, ionic liquids are very good choice for a variety of applications. The natural environment might be affected by the ionic liquids which can be toxic. Therefore, there is a need to address this problem by studying the ecotoxicological behaviour of these ionic liquids. The main objective of current research is to model the toxicity ecotoxicological behaviour is studied by quantitative structure activity relationship (QSAR). QSARs predicts the toxicity of ionic liquids. In current research a relationship between polarizability and toxicity for imidazolium ionic liquids with different alky chain length having NTF2 anion has been modelled. The success of current research will be very helpful to protect the nature by minimizing the killing of testing animals as well as ensuring the safety of biotic components of the ecosystem.