Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

Zhou, Yeheng; Peng, Jiale; Li, Penghua; Du, Haibo; Li, Yaping; Li, Yingying; Zhang, Li; Sun, Wei; Liu, Xingyong; Zuo, Zhili

doi:10.1016/j.compbiolchem.2018.11.024

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…Interestingly, these inhibitors were of the tanishone family with molecular scaffolds derived from the Chinese herb Danshen, which is widely used for cardiovascular and cerebrovascular diseases. Zhou et al employed a ligand-based 3D-QSAR using pharmacophore models derived from known IDO inhibitors to find novel backbones for next generation IDO inhibitors (119). SVMs applied to the repertoire of A2AR inhibitors helped identify novel pyrazolo-triazolo-pyrimidine derivatives with A2AR activity (120).…”

Section: Application Of Virtual Screenings and Machine Learning Has Helped Identify Novel Inhibitors Of Ido And A2ar Zhang Et Al Took A Lmentioning

confidence: 99%

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Machine and deep learning approaches for cancer drug repurposing

Issa

Stathias

Schürer

et al. 2021

Seminars in Cancer Biology

170

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Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drugtarget and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

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Section: Application Of Virtual Screenings and Machine Learning Has Helped Identify Novel Inhibitors Of Ido And A2ar Zhang Et Al Took A Lmentioning

confidence: 99%

“…The ZINC database was then screened using the developed QSAR model resulting in the identification of compound P-902 with potential anticancer activity against MCF-7 breast cancer cells. Zhou et al similarly employed QSAR modeling of IDO1 inhibitor pharmacophores to discover novel backbones for next generation IDO inhibitors (119).…”

Section: Repurposingmentioning

confidence: 99%

Machine and deep learning approaches for cancer drug repurposing

Issa

Stathias

Schürer

et al. 2021

Seminars in Cancer Biology

170

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“…Traditional drug development is a lengthy, time consuming, and costly process with a very low success rate. With the continuous advancement of computing technology, computer-aided drug design has become a popular method for preclinical drug research with its unique advantages of high efficiency and low cost 23 . Novel core structure LibDock Model 24 , CHARMM force field 25 , and CDOCKER molecular docking 26 were reported as an effective virtual screening strategy.…”

Section: Introductionmentioning

confidence: 99%

Combined inhibition of JAK1/2 and DNMT1 by newly identified small-molecule compounds synergistically suppresses the survival and proliferation of cervical cancer cells

et al. 2020

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Despite substantial advances in treating cervical cancer (CC) with surgery, radiation and chemotherapy, patients with advanced CC still have poor prognosis and significantly variable clinical outcomes due to tumor recurrence and metastasis. Therefore, to develop more efficacious and specific treatments for CC remains an unmet clinical need. In this study, by virtual screening the SPECS database, we identified multiple novel JAK inhibitor candidates and validated their antitumor drug efficacies that were particularly high against CC cell lines. AH057, the best JAK inhibitor identified, effectively blocked the JAK/STAT pathways by directly inhibiting JAK1/2 kinase activities, and led to compromised cell proliferation and invasion, increased apoptosis, arrested cell cycles, and impaired tumor progression in vitro and in vivo. Next, by screening the Selleck chemical library, we identified SGI-1027, a DNMT1 inhibitor, as the compound that displayed the highest synergy with AH057. By acting on a same set of downstream effector molecules that are dually controlled by JAK1/2 and DNMT1, the combination of AH057 with SGI-1027 potently and synergistically impaired CC cell propagation via dramatically increasing apoptotic cell death and cell-cycle arrest. These findings establish a preclinical proof of concept for combating CC by dual targeting of JAK1/2 and DNMT1, and provide support for launching a clinical trial to evaluate the efficacy and safety of this drug combination in patients with CC and other malignant tumors.

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“…Such virtual screening exercises may be further coupled with other computational methods, such as ligand-target docking for confirmation of activity [24,25]. Whereas the classical drug development process is very costly and tedious, computational methods have a high efficiency and are inexpensive [26]. In this context, we developed a set of QSAR models with different descriptors and machine learning classification algorithms, integrated by stacking, to be used for virtual screening of c-src tyrosin kinase inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

IJMS

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A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

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Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

Cited by 11 publications

References 35 publications

Machine and deep learning approaches for cancer drug repurposing

Machine and deep learning approaches for cancer drug repurposing

Combined inhibition of JAK1/2 and DNMT1 by newly identified small-molecule compounds synergistically suppresses the survival and proliferation of cervical cancer cells

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

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