BET bromodomain inhibitors: fragment-based in silico design using multi-target QSAR models

Speck‐Planche, Alejandro; Scotti, Marcus Tullius

doi:10.1007/s11030-018-9890-8

Cited by 42 publications

(40 citation statements)

References 66 publications

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“…Multi-target QSAR modelling based on the Box–Jenkins approach was successfully utilised in recent years to establish a number of validated predictive chemometric models for various targets [9,10,11,50,60,82,86]. In the current work, we carried out such kind of mt-QSAR modelling on inhibitors of four different isoforms of class I PI3K enzyme using the recently introduced QSAR-Co tool [11].…”

Section: Resultsmentioning

confidence: 99%

“…In this equation, Δ(Di)cj is a deviation descriptor that actually measures to what extent a chemical structurally deviates from a set of compounds assigned as active and tested against the same experimental condition [11,81,82]. In the QSAR-Co software, the calculated D i descriptors are provided as inputs as these descriptors are automatically converted into Δ(Di)cj by this tool for the development of mt-QSAR models [11].…”

Section: Methodsmentioning

confidence: 99%

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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

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

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

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“…At the same time, this cut-off prevents excessive imbalance between active and inactive compounds. The software QUBILs-MAS v1.0 [30] was employed to calculate the molecular descriptors known as the atom-based quadratic indices, which have been earlier proved to be highly efficient for developing mt-QSAR models [27,31,32,33,34,35]. A detailed description of how these descriptors are calculated is provided in the Materials and Methods section.…”

Section: Resultsmentioning

confidence: 99%

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

2019

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Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

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“…expected numerical data for groups of endpoints, which affect the phenomenon under consideration, e.g., therapeutic effect, inhibition, biocide potential, etc.). Nonetheless, traditional approaches serve as a basis to solve the task of building up multi-target QSARs, e.g., using multiple regression [99], partial least squares (PLS) [100], artificial neural networks (ANN) [101][102][103], and random forest [104].…”

Section: Multi-target Qsar Modelsmentioning

confidence: 99%

“…Nowadays, multi-target QSAR is a part of "intensive" studies [96][97][98][99][100][101][102][103][104]. The development of criteria of the predictive potential of models (Table 3) also is a part of the "intensive" studies.…”

Section: The Simplicity or The Efficiency: Which Is Better?mentioning

confidence: 99%

QSPR/QSAR: State-of-Art, Weirdness, the Future

Toropov

Toropova

2020

Molecules

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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BET bromodomain inhibitors: fragment-based in silico design using multi-target QSAR models

Cited by 42 publications

References 66 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

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

QSPR/QSAR: State-of-Art, Weirdness, the Future

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