Recent Trends in the Development of Benzimidazole Hybrid Derivatives and Their Antimalarial Activities

A parasite of the Plasmodium species initiates malaria. The parasite is transmitted to communities through the bite of an infected mosquito. Malarial resistance towards the commonly used antimalarial agents is a genuine human health problem. Benzimidazole derivatives exhibit a wide range of antimalarial activities against Plasmodium falciparum (P. falciparum) strain. The present review has summarized the antimalarial activity of benzimidazole hybrid derivatives and described its structural activity relationship (SAR) and quantitative structural-activity relationship (QSAR) model. A total of 14 papers were systematically reviewed. The literature survey has revealed that novel benzimidazole hybrid derivatives diminished the P. falciparum activity in the liver and gametocyte stages and inhibited heme synthesis and β-hematin formation. The QSAR models explain imminent antimalarial agent's growth through multiple linear regression (MLR) and artificial neural networks (ANN).

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Quantitative structural activity relationship study of bis-tetraazacyclic compounds. A novel series of HIV-1 and HIV-2 inhibitors

Journal of Medicinal Chemistry ◽

10.1021/jm00019a017 ◽

1995 ◽

Vol 38 (19) ◽

pp. 3865-3873 ◽

Cited By ~ 25

Author(s):

Heidi C. Joao ◽

Karen De Vreese ◽

Rudi Pauwels ◽

Erik De Clercq ◽

Geoff W. Henson ◽

...

Keyword(s):

Activity Relationship ◽

Structural Activity Relationship ◽

Relationship Study ◽

Quantitative Structural Activity Relationship ◽

Hiv 1

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Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

Drug Research ◽

10.1055/a-1138-8725 ◽

2020 ◽

Vol 70 (05) ◽

pp. 226-232 ◽

Cited By ~ 3

Author(s):

Ashwani Kumar ◽

Kiran Bagri ◽

Parvin Kumar ◽

Keyword(s):

Research Work ◽

Activity Relationship ◽

Training Set ◽

Molecular Docking Study ◽

Statistical Parameters ◽

Monte Carlo Optimization ◽

Fructose 1,6 Bisphosphatase ◽

Structural Activity Relationship ◽

Quantitative Structural Activity Relationship

AbstractFructose-1,6-bisphosphatase performs a significant function in regulating the blood glucose level in type 2 diabetes by controlling the process gluconeogenesis. In this research work optimal descriptor (graph) based quantitative structural activity relationship studies of a set of 203 fructose-1,6-bisphosphatase has been performed with the help of Monte Carlo optimization. Distribution of compounds into different sets such as training set, invisible training set, calibration set and validation sets resulted in formation of splits. Statistical parameters obtained from quantitative structural activity relationship modeling were good for various designed splits. The statistical parameters such as R2 and Q2 for calibration and validation sets of best split developed were found to be 0.8338, 0.7908 & 0.7920 and 0.7036 respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoters and demoters of the endpoint. Further these structural attributes were used in designing of new fructose-1,6-bisphosphatase inhibitors and molecular docking study was accomplished for the determination of interactions of designed molecules with the enzyme.

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Exploring three-dimensional quantitative structural activity relationship (3D-QSAR) analysis of SCH 66336 (Sarasar) analogues of farnesyltransferase inhibitors

European Journal of Medicinal Chemistry ◽

10.1016/j.ejmech.2007.02.013 ◽

2008 ◽

Vol 43 (1) ◽

pp. 204-209 ◽

Cited By ~ 15

Author(s):

Tabish Equbal ◽

Om Silakari ◽

Muttineni Ravikumar

Keyword(s):

Three Dimensional ◽

3D Qsar ◽

Activity Relationship ◽

Farnesyltransferase Inhibitors ◽

Qsar Analysis ◽

Structural Activity Relationship ◽

Quantitative Structural Activity Relationship

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VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models

Bioinformatics ◽

10.1093/bioinformatics/btaa187 ◽

2020 ◽

Vol 36 (11) ◽

pp. 3610-3612

Author(s):

Qingyang Ding ◽

Siyu Hou ◽

Songpeng Zu ◽

Yonghui Zhang ◽

Shao Li

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Qsar Model ◽

Structure Activity Relationship ◽

Global Perspective ◽

Supplementary Information ◽

Activity Relationship ◽

Structure Activity ◽

Interactive Tool ◽

Qsar Models

Abstract Summary Although many quantitative structure–activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualization of QSAR model results remain challenging, especially for ‘black box’ models such as deep neural network (DNN). Here, we take a step forward to interpret the learned chemical features from DNN QSAR models, and present VISAR, an interactive tool for visualizing the structure–activity relationship. VISAR first provides functions to construct and train DNN models. Then VISAR builds the activity landscapes based on a series of compounds using the trained model, showing the correlation between the chemical feature space and the experimental activity space after model training, and allowing for knowledge mining from a global perspective. VISAR also maps the gradients of the chemical features to the corresponding compounds as contribution weights for each atom, and visualizes the positive and negative contributor substructures suggested by the models from a local perspective. Using the web application of VISAR, users could interactively explore the activity landscape and the color-coded atom contributions. We propose that VISAR could serve as a helpful tool for training and interactive analysis of the DNN QSAR model, providing insights for drug design, and an additional level of model validation. Availability and implementation The source code and usage instructions for VISAR are available on github https://github.com/qid12/visar. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?

Journal of Chemistry ◽

10.1155/2021/5563066 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Qingwei Bu ◽

Qingshan Li ◽

Yun Liu ◽

Chun Cai

Keyword(s):

Prediction Models ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Structure Activity Relationship ◽

Specific Model ◽

Performance Comparison ◽

Activity Relationship ◽

Statistical Measures ◽

Structure Activity ◽

Qsar Models

Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.

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