Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening, molecular docking, and binding free energy analysis

Peddi, Saikiran Reddy; Sivan, Sree Kanth; Manga, Vijjulatha

doi:10.1007/s11224-018-1154-9

Cited by 13 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So far, a number of different ligand-based in silico methods and protocols have been employed by different researchers for the discovery of PI3K inhibitors, and these include 2D-quantitative structure–activity relationship (2D-QSAR) modelling, 3D-QSAR, 3D-pharmacophore mapping, etc. [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]. However, all these methods suffer from at least one of the following shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Halder

Cordeiro

2019

IJMS

View full text Add to dashboard Cite

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Halder

Cordeiro

2019

IJMS

View full text Add to dashboard Cite

show abstract

“…[54] Glide: All proteins were prepared using the Protein Preparation Wizard tool in Schrödinger, employing the OPLS_2005 force field. [55] All ligands were prepared using the LigPrep tool with the default settings. [56] Two docking modes, SP and XP, were used for the docking protocol with the default parameters.…”

Section: Methodsmentioning

confidence: 99%

Integration of Multicomplex‐Based Pharmacophore Modeling and Molecular Docking in Machine Learning‐Based Virtual Screening: Toward the Discovery of Novel PI3K Inhibitors

Qiu,

Jia,

Yuan

et al. 2024

Advcd Theory and Sims

View full text Add to dashboard Cite

The phosphatidylinositol‐3 kinase (PI3K) pathway is a crucial intracellular signaling pathway within living cells. The hyperactivation of PI3K signaling cascades is a common occurrence in human cancers, rendering PI3K a promising therapeutic target. Although several PI3K inhibitors are already available on the market, the adverse side effects of current therapies continue to highlight the necessity for the development of novel PI3K inhibitors. In this study, a virtual screening strategy employing naïve Bayesian classification (NBC) models, based on multicomplex‐based molecular docking and pharmacophore modeling, is developed. First, the docking accuracy and scoring reliability of four docking software are assessed, and Glide demonstrated higher predictability for PI3K inhibitors. Second, pharmacophore models are generated based on the current reported PI3K‐inhibitor interactions, and five pharmacophore hypotheses displayed significant capability in discriminating active PI3K molecules from inactive ones. Subsequently, three NBC models are constructed based on molecular docking and/or pharmacophore models, and the validation results showed that the NBC model, combining multicomplex‐based molecular docking and pharmacophore, significantly improved the hit rate of virtual screening against PI3K. Finally, the optimal NBC model is employed for virtual screening against the ChEMBL database, leading to the identification of multiple molecules with high potential as active PI3K inhibitors.

show abstract

“…A tree depth of 5 with a box size of initial set to 32Å and a nal box size set to1 Å was used. Hypotheses were produced by systematically changing the number of sites and number of matching active molecules [21].…”

Section: Prediction Of Common Pharmacophore Hypothesismentioning

confidence: 99%

Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking

Bathula

Lanka

Chakravarty³

et al. 2022

Struct Chem

Self Cite

View full text Add to dashboard Cite

Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type II arginine enzyme that is crucial for many cellular processes and is associated with many cancer diseases. In this study, pharmacophore-based 3D QSAR modeling, virtual screening and binding free energy studies were carried out from a set of 61 potent compounds reported being inhibitors of PRMT5 protein. A vepoint pharmacophore model (AADHR) was generated and this model is used to generate an atom-based 3-Dimensional quantitative structure-activity relationship (3D-QSAR). The obtained 3D-QSAR model has high correlation coe cient (R 2 = 0.91), cross-validation coe cient (Q 2 = 0.82), F value (140.3), low RMSE (0.47) and pearson R-value (0.91). A library of 329825 molecules (ChEMBL database) is screened with pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best t-active conformations binding at the receptor site of PRMT5 protein. Further, we are calculated ADME and toxicity properties using QikProp module and pkCSM server and nally prioritized the lead molecules by binding free energy prediction.

show abstract

Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening, molecular docking, and binding free energy analysis

Cited by 13 publications

References 29 publications

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

Integration of Multicomplex‐Based Pharmacophore Modeling and Molecular Docking in Machine Learning‐Based Virtual Screening: Toward the Discovery of Novel PI3K Inhibitors

Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking

Contact Info

Product

Resources

About