NPred: QSAR classification model for identifying plant based naturally occurring anti-cancerous inhibitors

Dhiman, Kanika; Agarwal, Subhash Mohan

doi:10.1039/c6ra02772e

Cited by 21 publications

(15 citation statements)

References 24 publications

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“…Further, it has been recommended that after QSAR model is validated, the applicability domain must also be verified in order to confirm that the predicted data is reliable (Dhiman & Agarwal, ). Therefore, the applicability domain of the developed QSAR model was evaluated using Williams plot.…”

Section: Methodsmentioning

confidence: 99%

“…So, in order to overcome the problem of drug resistance due to T790M mutation, new irreversible inhibitors need to be designed. In the last few years, various drug designing approaches such as molecular docking, dynamics (Sharma, Nandekar, Sangamwar, Pérez-Sánchez, & Agarwal, 2016;Yadav et al, ), and QSAR (Dhiman & Agarwal, 2016;Singh, Singh, Singla, Agarwal, & Raghava, 2015) are being employed for the purpose of finding new inhibitors due to their proven utility. Also, irreversible inhibitors are widely used in clinical practice , and ~20% of the covalent drugs in the market are anti-cancer (Kumalo, Bhakat, & Soliman, 2015).…”

Section: Introductionmentioning

confidence: 99%

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QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors

2019

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EGFR is a well‐established therapeutic target of clinical relevance in cancer. However, acquisition of secondary mutation (T790M) makes first‐generation inhibitors ineffective. Therefore, to circumvent the problem of resistance, new T790M/L858R (TMLR) double mutant inhibitors are required. In this study, fragment‐based QSAR models (GQSAR) were generated for pyridinylimidazole derivatives having biological activity against TMLR mutants. The GQSAR model developed using partial least squares regression via stepwise forward–backward variable selection technique showed best results as judged using statistical parameters (r2, q2, and pred_r2). Additionally, applicability domain of the model was verified using Williams plot, which indicated that the predicted data are reliable. The GQSAR provided site‐specific clues wherein modifications related to decreasing lipophilic character and rotatable bonds and increasing SaaCHE‐index are required for improving inhibitory activity. Overall, the study indicated that the presence of acrylamide at R5 is essential for covalent bond formation with Cys797 and occurrence of aromatic residue at R2 is required for occupying hydrophobic region next to Met790 gatekeeper residue. Based on this information, new derivatives were designed that show better inhibitory activity than the experimentally reported most active molecules. Thus, the model developed can be used to design new pyridinylimidazole derivatives with improved TMLR bioactivity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors

2019

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“…The current trend in anticancer drug discovery is to screen for NPs with potential activity against known therapeutic targets to select leads for optimisation and development . In the recent years, chemoinformatic tools and in silico methods have contributed significantly to different stages of drug development . Remarkable advances have been made which are aiding lead identification and optimisation including computational modelling and virtual screening .…”

Section: Introductionmentioning

confidence: 99%

“…[22][23][24] In the recent years, chemoinformatic tools and in silico methods have contributed significantly to different stages of drug development. [25][26][27][28][29][30] Remarkable advances have been made which are aiding lead identification and optimisation including computational modelling and virtual screening. [31][32][33] Although these tools are robust yet higher attrition rates of drug-like candidates has been observed due to non-consideration of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties during drug discovery programmes.…”

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confidence: 99%

Pharmacokinetic profiling of anticancer phytocompounds using computational approach

Sharma

Gupta

et al. 2018

Phytochemical Analysis

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“…In recent years, designing inhibitors using in silico structure and ligand‐based approaches have been useful in accelerating the pace of drug discovery . Quantitative structure–activity relationship (QSAR) is one approach that plays an important role and is widely used to identify the structural requirements of various molecules for predicting biological activity . It is a method that establishes a correlation between the structural or various molecular properties of a set of compounds with their experimentally known biological inhibitory activities.…”

Section: Introductionmentioning

confidence: 99%

Insight into structural requirements of antiamoebic flavonoids: 3D‐QSAR and G‐QSAR studies

2018

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Plant-based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D-QSAR analysis using k-nearest neighbour molecular field analysis via stepwise forward-backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q = 0.64 and pred_r = 0.56). Also, a group-based QSAR (G-QSAR) model was developed based on partial least squares regression combined with stepwise forward-backward variable selection method. It gave best parametric results (r = 0.74, q = 0.56 and pred_r = 0.54) which implied that the model is highly predictive. 3D-QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G-QSAR provided site-specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic inhibitors.

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NPred: QSAR classification model for identifying plant based naturally occurring anti-cancerous inhibitors

Abstract: Prediction of naturally occurring plant based compounds as anticancer agents is the key to developing new chemical entities in the area of therapeutic oncology. A webserver for assessing anticancer potential of phytomolecules has been developed.

Cited by 21 publications

References 24 publications

QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors

QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors

Pharmacokinetic profiling of anticancer phytocompounds using computational approach

Insight into structural requirements of antiamoebic flavonoids: 3D‐QSAR and G‐QSAR studies

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