Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing

Liu, Jian; Zhu, Yehua; He, Yuhang; Zhu, Haohao; Gao, Yi Qin; Li, Zhi; Zhu, Junru; Sun, Xinjie; Fang, Fang; Wen, Hongmei; Li, Wei

doi:10.1080/07391102.2019.1590241

Cited by 19 publications

(5 citation statements)

References 89 publications

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“…Pharmacophore based drug design has shown to be a better approach to develop ligands bearing active interactive features of different targets than other strategies (Arooj et al., 2013 ; Jana & Singh, 2019 ; Mitra et al., 2019 ; Thangapandian et al., 2011 ; Xu et al., 2015 ). Pharmacophore can be defined as a common denominator of molecular interactive features including electronic and steric, that is shared by a set of ligands that are active against a particular target (Liu et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads

Mitra

Ghanta

Acharya

et al. 2020

Journal of Biomolecular Structure and Dynamics

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SARS-related coronaviruses poses continual threat to humanity by rapidly mutating and emerging as severe pandemic outbreaks, including the current nCoV-19 pandemic. Hence a rapid drug repositioning and lead identification strategy are required to mitigate these outbreaks. We report a pharmacophore and molecular dynamics-based approach for drug repositioning and lead identification against dual targets (3CLp and PLp) of SARS-CoV-2. The pharmacophore model of 3CLp inhibitors was apolar with two aromatic and two H-bond acceptors, whereas that of PLp was relatively polar, bearing one aromatic and three H-bond acceptors. Pharmacophore-based virtual screening yielded six existing FDA-approved drugs and twelve natural products with both the pharmacophoric features. Among them are nelfinavir, tipranavir and licochalcone-D, which has shown better binding characteristics with both the proteases compared to lopinavir. The molecular dynamics revealed that the connecting loop (residues 176-199) of 3CLp is highly flexible, and hence, inhibitors should avoid high-affinity interactions with it. Lopinavir, due to its high affinity with the loop region, exhibited unstable binding. Further, the van der Waals size of the 3CLp inhibitors positively correlated with their binding affinity with 3CLp. However, the van der Waals size of a ligand should not cross a threshold of 572Å 3 , beyond which the ligands are likely to make high-affinity interaction with the loop and suffer unstable binding as observed in the case of lopinavir. Similarly, the total polar surface area of the ligands were found to be negatively correlated with their binding affinity with PLp.

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Section: Introductionmentioning

confidence: 99%

Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads

Mitra

Ghanta

Acharya

et al. 2020

Journal of Biomolecular Structure and Dynamics

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“…Drug repositioning involves the identification of novel treatments for diseases based on "old" drugs or compounds [58]. Several strategies were developed to achieve the identification of druggable compounds against novel targets, like QSAR studies, in conjunction with molecular docking [36], with molecular docking and molecular dynamics [37,38], and even with quantum mechanics/molecular mechanics methods [39]. High performance QSAR models can be obtained by using machine learning approaches to classify the molecular descriptors of compounds from large datasets [59].…”

Section: Discussionmentioning

confidence: 99%

“…Particularly useful in drug design, discovery, and development is 3D-QSAR methodology, which helps to understand the relationship between spatial parameters of molecules and their biological properties [ 35 ]. Recent studies have used 3D-QSAR as a screening step in drug repositioning strategies, some examples being [ 36 , 37 , 38 , 39 ]. In these cases, QSAR models were used to screen compounds from: (i) DrugBank database [ 31 ] in order to identify promising candidates that modulate histone deacetylases [ 37 ] or inhibit SARS-CoV main protease [ 38 ]; (ii) FDA approved drugs from ZINC database [ 40 ] that inhibit Sirt2 [ 39 ]; or (iii) FDA approved drugs from e-Drug3D database [ 41 ] to identify druggable compounds for iatrogenic botulism treatment [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have used 3D-QSAR as a screening step in drug repositioning strategies, some examples being [ 36 , 37 , 38 , 39 ]. In these cases, QSAR models were used to screen compounds from: (i) DrugBank database [ 31 ] in order to identify promising candidates that modulate histone deacetylases [ 37 ] or inhibit SARS-CoV main protease [ 38 ]; (ii) FDA approved drugs from ZINC database [ 40 ] that inhibit Sirt2 [ 39 ]; or (iii) FDA approved drugs from e-Drug3D database [ 41 ] to identify druggable compounds for iatrogenic botulism treatment [ 36 ]. 3D-QSAR is valuable in drug repositioning, being complementary to other methods like molecular docking, because it predicts the activity of compounds (high/low activity) and yields the molecular features important for their effect [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

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3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

et al. 2021

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The current treatment of depression involves antidepressant synthetic drugs that have a variety of side effects. In searching for alternatives, natural compounds could represent a solution, as many studies reported that such compounds modulate the nervous system and exhibit antidepressant effects. We used bioinformatics methods to predict the antidepressant effect of ten natural compounds with neuroleptic activity, reported in the literature. For all compounds we computed their drug-likeness, absorption, distribution, metabolism, excretion (ADME), and toxicity profiles. Their antidepressant and neuroleptic activities were predicted by 3D-ALMOND-QSAR models built by considering three important targets, namely serotonin transporter (SERT), 5-hydroxytryptamine receptor 1A (5-HT1A), and dopamine D2 receptor. For our QSAR models we have used the following molecular descriptors: hydrophobicity, electrostatic, and hydrogen bond donor/acceptor. Our results showed that all compounds present drug-likeness features as well as promising ADME features and no toxicity. Most compounds appear to modulate SERT, and fewer appear as ligands for 5-HT1A and D2 receptors. From our prediction, linalyl acetate appears as the only ligand for all three targets, neryl acetate appears as a ligand for SERT and D2 receptors, while 1,8-cineole appears as a ligand for 5-HT1A and D2 receptors.

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“…These findings indicate that HDAC7 and HDAC9 may serve as potential therapeutic targets and are indicative of poor prognosis in childhood ALL. Liu et al (2020), investigated the use of drug repurposing, pharmacophore modelling, 3D-QSAR and docking studies to identify novel HDAC inhibitors. They aimed to discover new inhibitors that could be used to treat cancer and other diseases, and through their research they identified several potential HDAC inhibitors that had not been previously studied.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Simulation Environment for the Importance of Histone Deacetylase in Childhood Acute Leukemia with Explainable Artificial Inteligence

Uysal,

Kose

2023

BRAIN

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This study aims to explore new therapeutic opportunities for histone deacetylase (HDAC) inhibitors by leveraging drug repurposing approaches and analyzing their bioactivity and molecular fingerprints. The methodology includes investigating drug repurposing opportunities for HDAC inhibitors, evaluating the bioactivity of repurposing drugs on HDAC enzymes, investigating the role of HDAC genes in therapeutic effects, and analyzing molecular fingerprints with explainable artificial intelligence (XAI) to identify structurally similar compounds with potential HDAC inhibitory activity. In this context, chemical compounds with IC50 (7903 compounds) and Inhibition (1084 compounds) standard types of HDAC genes reported to be associated with childhood acute leukemia were represented by molecular fingerprints. Regression and classification models were applied to the molecular fingerprints, and the results obtained were supported by XAI. All the study results were shared interactively on the website address https://iuysal1905-childhoodacuteleukemia-drug-interacito-arayuz-r89zld.streamlit.app/ by designing a simulation environment. The influence of molecular fingerprints on the models and their impact on potential drug development in childhood acute leukemia were evaluated using XAI techniques, particularly through the analysis of SHAP values. The study contributes to the literature on the use of XAI technology in drug repurposing studies, especially in cancer, the study of molecular properties, and the active use of XAI in drug repurposing studies

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Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing

Cited by 19 publications

References 89 publications

Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads

Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads

3D-ALMOND-QSAR Models to Predict the Antidepressant Effect of Some Natural Compounds

Development of a Simulation Environment for the Importance of Histone Deacetylase in Childhood Acute Leukemia with Explainable Artificial Inteligence

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