Literature DB >> 30799792

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis.

Mahmoud A Al-Sha'er1, Rua'a A Al-Aqtash1, Mutasem O Taha2.   

Abstract

BACKGROUND: PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding.
METHODS: Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors).
RESULTS: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values.
CONCLUSION: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  Co-crystallized structure; PI3Kδ; anticancer; docking; pharmacophore modeling; roc analysis.

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Year:  2019        PMID: 30799792     DOI: 10.2174/1573406415666190222125333

Source DB:  PubMed          Journal:  Med Chem        ISSN: 1573-4064            Impact factor:   2.745


  1 in total

1.  CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery.

Authors:  Yaqin Li; Yongjin Xu; Yi Yu
Journal:  Molecules       Date:  2021-11-30       Impact factor: 4.411

  1 in total

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