ArticleMolecular informatics2017
Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction.
Article in Molecular informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- QSAR, molecular docking, and pharmacokinetic analysis of thiosemicarbazone-indole compounds targeting prostate cancer cells.Journal of Taibah University Medical Sciences · 2024Article
- Review
- Development of Provesicular Nanodelivery System of Curcumin as a Safe and Effective Antiviral Agent: Statistical Optimization, In Vitro Characterization, and Antiviral Effectiveness.Molecules (Basel, Switzerland) · 2020Article
- Prediction of Passive Membrane Permeability by Semi-Empirical Method Considering Viscous and Inertial Resistances and Different Rates of Conformational Change and Diffusion.Molecular informatics · 2020Article
- Prediction of Protein-compound Binding Energies from Known Activity Data: Docking-score-based Method and its Applications.Molecular informatics · 2018Article
- Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction.Molecular informatics · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In order to improve docking score correction, we developed several structure-based quantitative structure activity relationship (QSAR) models by protein-drug docking simulations and applied these models to public affinity data. The prediction models used descriptor-based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. The binding free energy that corresponded to the docking score was approximated by a weighted average of docking scores for multiple proteins, and we tried linear, weighted linear and polynomial regression models considering the compound similarities. In addition, we tried a combination of these regression models for individual data sets such as IC
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