ArticleBMC chemistry2020
Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions.
Article in BMC chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
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- Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors.International journal of molecular sciences · 2026Article
- AnCurrent pharmaceutical design · 2026Article
- High-Throughput, High-Quality: Benchmarking GNINA and AutoDock Vina for Precision Virtual Screening Workflow.Molecules (Basel, Switzerland) · 2025Article
- Systems Biology-Driven Discovery of Host-Targeted Therapeutics for Oropouche Virus: Integrating Network Pharmacology, Molecular Docking, and Drug Repurposing.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Epigallocatechin-3-gallate: a multi-target bioactive molecule derived from green tea against Oropouche virus-a computational approach to host-pathogen network modulation.Frontiers in chemistry · 2025Article
- Pharmacophore screening, molecular docking, and MD simulations for identification of VEGFR-2 and c-Met potential dual inhibitors.Frontiers in pharmacology · 2025Article
- Docking‑based virtual screening of BRD4 (BD1) inhibitors: assessment of docking methods, scoring functions and in silico molecular properties.BMC chemistry · 2024Article
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- The Discovery of Indole-2-carboxylic Acid Derivatives as Novel HIV-1 Integrase Strand Transfer Inhibitors.Molecules (Basel, Switzerland) · 2023Article
- Virtual screening for potential discoidin domain receptor 1 (DDR1) inhibitors based on structural assessment.Molecular diversity · 2023Article
- Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease.Journal of molecular structure · 2023Article
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- Structure-Guide Design and Optimization of Potential Druglikeness Inhibitors for TGFβRI with the Pyrrolopyrimidine Scaffold.Pharmaceuticals (Basel, Switzerland) · 2022Article
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- A novel insight into mechanism of derangement of coagulation balance: interactions of quantum dots with coagulation-related proteins.Particle and fibre toxicology · 2022Article
- Screening the components of Saussurea involucrata for novel targets for the treatment of NSCLC using network pharmacology.BMC complementary medicine and therapies · 2022Article
- Article
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4 authors.
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Abstract
To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (TβR1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study. LibDock and CDOCKER protocols were performed on a test set of 24 TβR1 protein-ligand complexes. Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to obtain more accurate dockings in medium-size virtual screening experiments of TβR1, with a successful docking rate of 95%. A dataset including 281 known active and 8677 inactive ligands was used to determine the best scoring function. The receiver operating characteristic (ROC) curves were used to compare the performance of scoring functions in attributing best scores to active than inactive ligands. The results show that Ludi 1, PMF, Ludi 2, Ludi 3, PMF04, PLP1, PLP2, LigScore2, Jain and LigScore1 are better scoring functions than the random distribution model, with AUC of 0.864, 0.856, 0.842, 0.812, 0.776, 0.774, 0.769, 0.762, 0.697 and 0.660, respectively. Based on the pairwise comparison of ROC curves, Ludi 1 and PMF were chosen as the best scoring functions for virtual screening of TβR1 inhibitors. Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.
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