Discovering Anti-Cancer Drugs via Computational Methods

Cui, Weihong; Aouidate, Adnane; Wang, Shouguo; Yu, Qiuliyang; Li, Yanhua; Yuan, Shuguang

doi:10.3389/fphar.2020.00733

Cited by 172 publications

(94 citation statements)

References 133 publications

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“… 4 , 5 This has driven the widespread and continuous search for new effective anticancer treatments while remaining a significant challenge to overcome. 6 …”

Section: Introductionmentioning

confidence: 99%

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

Al‐Jarf

Sá

Pires

et al. 2021

J. Chem. Inf. Model.

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The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure–activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson’s correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at .

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“… 4 , 5 This has driven the widespread and continuous search for new effective anticancer treatments while remaining a significant challenge to overcome. 6 …”

Section: Introductionmentioning

confidence: 99%

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

Al‐Jarf

Sá

Pires

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

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“…Being the crucial players of these pathway, β-catenin, p53 and KRAS genes are always found mutated in CRC, resulting in resistance to current therapies, conferring poor prognosis (Aran et al, 2016 (Hiramatsu et al, 1993). With p53 and KRAS being the undruggable molecular targets, the discovery of new anticancer drugs for these genes by aiming the protein products (Lazo & Sharlow, 2016) and aided using computational techniques (Cui et al, 2020) have been reported. Thus, in this study, novel potential inhibitors based on anthraquinones from Morinda citrifolia were discovered through the deployment of ligand-based protein docking for β-catenin, MDM2-p53 and KRAS.…”

Section: Introductionmentioning

confidence: 99%

Morindone From Morinda Citrifolia as a Potential Antiproliferative Agent Against Colorectal Cancer Cell Lines

Chee

Zamakshshari

Lee

et al. 2021

Preprint

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There is an increasing demand in developing new, effective, and affordable anti-cancer against colon and rectal. In this study, our aim is to identify the potential anthraquinone compounds from the root bark of Morinda citrifolia to be tested in vitro against colorectal cancer cell lines. Eight potential anthraquinone compounds were successfully isolated, purified and tested for both in-silico and in-vitro analyses. Based on the in-silico prediction, two anthraquinones, morindone and rubiadin, exhibit a comparable binding affinity towards multitargets of β-catenin, MDM2-p53 and KRAS. Subsequently, we constructed a 2D interaction analysis based on the above results and it suggests that the predicted anthraquinones from Morinda citrifolia offer an attractive starting point for potential antiproliferative agents against colorectal cancer. In vitro analyses further indicated that morindone and damnacanthal have significant cytotoxicity effect and selectivity activity against colorectal cancer cell lines.

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“…In recent years, drug discovery approaches, including advanced computer-aided drug design-guided structure–activity relationship studies, have facilitated many NCE drug discoveries and innovation in the diverse classes of diseases, such as Alzheimer’s disease (AD), cancer, anddiabetes [ 32 , 33 , 34 , 35 , 36 ]. The ligand-based drug design can possibly be performedwith the software programs such asSYBYL/comparative molecular field analysis (CoMFA) [ 37 ], comparative molecular similarity indices analysis (CoMSIA) [ 38 ], and Catalyst/HypoGen (quantitative) and HipHop (qualitative) [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches

et al. 2021

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Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.

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Discovering Anti-Cancer Drugs via Computational Methods

Cited by 172 publications

References 133 publications

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

Morindone From Morinda Citrifolia as a Potential Antiproliferative Agent Against Colorectal Cancer Cell Lines

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches

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