wSDTNBI: a novel network-based inference method for virtual screening

Wu, Zengrui; Ma, Hui; Liu, Zehui; Zheng, Lulu; Yu, Zhuohang; Cao, Shixun; Fang, Wenqing; Wu, Li; Li, Weihua; Liu, Guixia; Huang, Jin; Tang, Yun

doi:10.1039/d1sc05613a

Cited by 17 publications

(24 citation statements)

References 81 publications

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“…Recently, Wu et al utilized a new type of network-based VS to study RORγt modulators. Seven of the seventy-two compounds were confirmed to be low micromolar (IC 50 from 0.1 µM to 4.97 µM) RORγt inverse agonists by in vitro experiments, resulting in a hit rate of 9.7% [38]. These studies show that VS can be used both successfully and diversely to identify novel RORγt compounds.…”

Section: Novel Virtual Screening Strategy Yielded An Effective Hit Ra...mentioning

confidence: 76%

“…Both structure-based and ligand-based VS approaches have been successfully employed to identify novel compounds for RORγt [33][34][35][36][37][38][39]. For example, we have screened RORγt using molecular docking and NIB screening [37].…”

Section: Novel Virtual Screening Strategy Yielded An Effective Hit Ra...mentioning

confidence: 99%

“…Both structure-based and ligand-based VS approaches have been successfully employed to identify novel compounds for targeting RORγt in the literature [33][34][35][36][37][38][39]. Initial hits have demonstrated micromolar activities in the AlphaScreen assay and a cell-based reporter gene assay and, after optimization, a few compounds have displayed significantly enhanced RORγt inhibition with half maximal inhibitory concentration (IC 50 ) values at the nanomolar level [35,36].…”

Section: Introductionmentioning

confidence: 99%

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Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

et al. 2023

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Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target’s binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

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Section: Novel Virtual Screening Strategy Yielded An Effective Hit Ra...mentioning

confidence: 76%

Section: Novel Virtual Screening Strategy Yielded An Effective Hit Ra...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

et al. 2023

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“…The tool named substructure-drug-target network-based inference (SDTNBI) was devised to prioritize potential targets for old drugs (“drug repositioning”), failed drugs, and new chemical entities by bridging the gap between new chemical entities and known drug-target interactions (DTIs) [ 133 ]. A later modification (wSDTNBI) [ 134 ] uses weighted DTI networks, whose edge weights are correlated with binding affinities, and network-based VS, which does not rely on the receptors’ 3D structures [ 135 ]. The publicly available SwissTargetPrediction web server ( , accessed on 20 December 2022) [ 136 ] also attempts to predict the most likely target(s) (in mice, rats, or human beings) for a SMILES-defined input molecule by using a computational method that combines different measures of similarities (both in 2D chemical structure and in 3D molecular shape) with known ligands [ 137 ].…”

Section: Linking Nps To Their Targets: Computational Methodologies Fo...mentioning

confidence: 99%

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

Gago

2023

Marine Drugs

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The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.

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“…In recent years, a new type of computational method named network-based inference (NBI) has achieved great success in the prediction of drug-target interactions (DTIs) with two advantages, namely, independence from the three-dimensional structures of targets and negative DTI samples ( i . e ., inactive DTIs determined by experiments). ,− Therefore, it may provide a new approach to predict potential EDCs integrating computational target profiles obtained by both network-based and other methods such as machine learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Zhou

et al. 2023

Environ. Sci. Technol.

Self Cite

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Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

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wSDTNBI: a novel network-based inference method for virtual screening

Abstract: In recent years, the rapid development of network-based methods for the prediction of drug-target interactions (DTIs) provides an opportunity for the emergence of a new type of virtual screening (VS),...

Cited by 17 publications

References 81 publications

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

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