Quantitative structure activity/pharmacokinetics relationship studies of HIV-1 protease inhibitors using three modelling methods

2019 ◽  
Vol 15 ◽  
Author(s):  
Dan Han ◽  
Jianjun Tan ◽  
Jingrui Men ◽  
Chunhua Li ◽  
Xiaoyi Zhang
Keyword(s):  
Clinical Application ◽  
Computational Models ◽  
Prediction Models ◽  
Good Choice ◽  
Support Vector ◽  
Quantitative Structure ◽  
Least Squares Regression ◽  
Structure Activity ◽  
Modelling Methods ◽  
Hiv 1

Background: HIV-1 protease inhibitor (PIs) is a good choice of ADIS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application. Objective: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities. Method: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN). Results: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and "r" _"pred" ^"2" of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN got the r2 of 0.952, 0.869 and 0.960, respectively, and the "r" _"pred" ^"2" of 0.852, 0.628 and 0.814, respectively. Conclusion: Among these three modelling methods, SVM showed superior ability to PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence for both koff and Ki in PLSR equations.

Quantitative Structure-Activity Relationship Study for HIV-1 LEDGF/p75 Inhibitors

2020 ◽  
Vol 16 (5) ◽  
pp. 654-666 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Yao Xi ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Training Set ◽  
Test Set ◽  
Structure Activity ◽  
Consensus Models ◽  
Hiv 1

Background: HIV-1 Integrase (IN) is an important target for the development of the new anti-AIDS drugs. HIV-1 LEDGF/p75 inhibitors, which block the integrase and LEDGF/p75 interaction, have been validated for reduction in HIV-1 viral replicative capacity. Methods: In this work, computational Quantitative Structure-Activity Relationship (QSAR) models were developed for predicting the bioactivity of HIV-1 integrase LEDGF/p75 inhibitors. We collected 190 inhibitors and their bioactivities in this study and divided the inhibitors into nine scaffolds by the method of T-distributed Stochastic Neighbor Embedding (TSNE). These 190 inhibitors were split into a training set and a test set according to the result of a Kohonen’s self-organizing map (SOM) or randomly. Multiple Linear Regression (MLR) models, support vector machine (SVM) models and two consensus models were built based on the training sets by 20 selected CORINA Symphony descriptors. Results: All the models showed a good prediction of pIC50. The correlation coefficients of all the models were more than 0.7 on the test set. For the training set of consensus Model C1, which performed better than other models, the correlation coefficient(r) achieved 0.909 on the training set, and 0.804 on the test set. Conclusion: The selected molecular descriptors show that hydrogen bond acceptor, atom charges and electronegativities (especially π atom) were important in predicting the activity of HIV-1 integrase LEDGF/p75-IN inhibitors.

scholarly journals Targeting against HIV/HCV Co-infection using Machine Learning-based multitarget-quantitative structure-activity relationships (mt-QSAR) Methods

2019 ◽  
Author(s):  
Yu Wei ◽  
Wei Li ◽  
Tengfei Du ◽  
Zhangyong Hong ◽  
Jianping Lin
Keyword(s):  
Protein Interaction ◽  
Mean Value ◽  
Support Vector ◽  
Classification Models ◽  
Quantitative Structure ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships ◽  
Approved Drugs ◽  
Hiv 1

ABSTRACTCo-infection between HIV-1 and HCV is common today in certain populations. However, treatment of co-infection is full of challenges with special consideration for potential hepatic safety and drug-drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV co-infection. However, identification of one molecule acting on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining naive Bayesian (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints (MACCS and ECFP6), 60 classification models were constructed to predict the active compounds toward 11 HIV-1 targets and 4 HCV targets based on the multitarget-quantitative structure-activity relationships (mt-QSAR). 5-fold cross-validation and test set validation was performed to confirm the performance of 60 classification models. Our results show that 60 mt-QSAR models appeared to have high classification accuracy in terms of ROC-AUC values ranging from 0.83 to 1 with a mean value of 0.97 for HIV-1 models, and ROC-AUC values ranging from 0.84 to 1 with a mean value of 0.96 for HCV. Furthermore, the 60 models were applied to comprehensively predict the potential targets for additional 46 compounds including 27 approved HIV-1 drugs, 10 approved HCV drugs and 9 selected compounds known to be active on one or more targets of HIV-1 or those of HCV. Finally, 18 hits including 7 HIV-1 approved drugs, 4 HCV approved drugs and 7 compounds were predicted to be HIV/HCV co-infection multitarget inhibitors. The reported bioactivity data confirmed that 7 compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. Of those remaining predicted hits and chemical-protein interaction pairs involving the potential ability to suppress HIV/HCV co-infection deserve further investigation by experiments. This investigation shows that the mt-QSAR method is available to predict chemical-protein interaction for discovering multitarget inhibitors and provide a unique perspective on HIV/HCV co-infection treatment.

scholarly journals The Psychonauts’ Benzodiazepines; Quantitative Structure-Activity Relationship (QSAR) Analysis and Docking Prediction of Their Biological Activity

2021 ◽  
Vol 14 (8) ◽  
pp. 720
Author(s):  
Valeria Catalani ◽  
Michelle Botha ◽  
John Martin Corkery ◽  
Amira Guirguis ◽  
Alessandro Vento ◽  
...  
Keyword(s):  
Biological Activity ◽  
Computational Models ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Docking Studies ◽  
Health Concern ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
High Biological Activity ◽  
Structure Activity

Designer benzodiazepines (DBZDs) represent a serious health concern and are increasingly reported in polydrug consumption-related fatalities. When new DBZDs are identified, very limited information is available on their pharmacodynamics. Here, computational models (i.e., quantitative structure-activity relationship/QSAR and Molecular Docking) were used to analyse DBZDs identified online by an automated web crawler (NPSfinder®) and to predict their possible activity/affinity on the gamma-aminobutyric acid A receptors (GABA-ARs). The computational software MOE was used to calculate 2D QSAR models, perform docking studies on crystallised GABA-A receptors (6HUO, 6HUP) and generate pharmacophore queries from the docking conformational results. 101 DBZDs were identified online by NPSfinder®. The validated QSAR model predicted high biological activity values for 41% of these DBDZs. These predictions were supported by the docking studies (good binding affinity) and the pharmacophore modelling confirmed the importance of the presence and location of hydrophobic and polar functions identified by QSAR. This study confirms once again the importance of web-based analysis in the assessment of drug scenarios (DBZDs), and how computational models could be used to acquire fast and reliable information on biological activity for index novel DBZDs, as preliminary data for further investigations.

scholarly journals Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 419 ◽  
Author(s):  
Dongdong Du ◽  
Jun Wang ◽  
Bo Wang ◽  
Luyi Zhu ◽  
Xuezhen Hong
Keyword(s):  
Prediction Models ◽  
Extraction Methods ◽  
Oxide Semiconductor ◽  
Soluble Solids ◽  
Support Vector ◽  
Least Squares Regression ◽  
Accuracy Rate ◽  
Linear Discriminant ◽  
The Difference ◽  
Ripe Stage

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal “eating ripe” stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R2 = 0.9928; testing: R2 = 0.9928), SSC (training: R2 = 0.9749; testing: R2 = 0.9143) and firmness (training: R2 = 0.9814; testing: R2 = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

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

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 ◽  
Quantitative Structure Activity Relationship ◽  
Good Choice ◽  
Ionic Structure ◽  
Quantitative Structure ◽  
Imidazolium Ionic Liquids ◽  
Structure Activity

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.

Quantitative Structure Activity Relationship (QSAR) study predicts small molecule binding to RNA structure

2021 ◽  
Author(s):  
Zhengguo Cai ◽  
Martina Zafferani ◽  
Olanrewaju Akande ◽  
Amanda Hargrove
Keyword(s):  
High Resolution ◽  
Small Molecules ◽  
Small Molecule ◽  
Rna Structure ◽  
Quantitative Structure ◽  
Rna Structures ◽  
Qsar Study ◽  
Structure Activity ◽  
Qsar Models ◽  
Hiv 1

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model RNA system. A set of small molecules bearing diverse scaffolds was screened against the HIV-1-TAR construct using surface plasmon resonance, which provided the binding kinetics and affinities. The data was then analyzed using multiple linear regression (MLR) combined with feature selection to afford robust models for binding of diverse RNA-targeted scaffolds. The predictivity of the model was validated on untested small molecules. The QSAR models presented herein represent the first application of validated and predictive 2D-QSAR using multiple scaffolds against an RNA target. We expect the workflow to be generally applicable to other RNA structures, ultimately providing essential insight into the small molecule descriptors that drive selective binding interactions and, consequently, providing a platform that can exponentially increase the efficiency of ligand design and optimization without the need for high-resolution RNA structures.

Graph Mining in Chemoinformatics

2011 ◽  
pp. 95-128 ◽  
Author(s):  
Hiroto Saigo ◽  
Koji Tsuda
Keyword(s):  
Graph Mining ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Least Squares Regression ◽  
Lead Compounds ◽  
Least Angle Regression ◽  
Attributed Graphs ◽  
Vector Machines

In standard QSAR (Quantitative Structure Activity Relationship) approaches, chemical compounds are represented as a set of physicochemical property descriptors, which are then used as numerical features for classification or regression. However, standard descriptors such as structural keys and fingerprints are not comprehensive enough in many cases. Since chemical compounds are naturally represented as attributed graphs, graph mining techniques allow us to create subgraph patterns (i.e., structural motifs) that can be used as additional descriptors. In this chapter, the authors present theoretically motivated QSAR algorithms that can automatically identify informative subgraph patterns. A graph mining subroutine is embedded in the mother algorithm and it is called repeatedly to collect patterns progressively. The authors present three variations that build on support vector machines (SVM), partial least squares regression (PLS) and least angle regression (LARS). In comparison to graph kernels, our methods are more interpretable, thereby allows chemists to identify salient subgraph features to improve the druglikeliness of lead compounds.

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