Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases
Wei-Neng Zhou1, Yan-Min Zhang1, Xin Qiao1
1Laboratory of Molecular Design and Drug Discovery, School of Basic Science, China Pharmaceutical University, Nanjing, Jiangsu, China.
Introduction:
Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases.
Methods:
In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again.
Results:
Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library.
Conclusion:
In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.
Insights
Researchers identified potent Acetyl-CoA Carboxylase (ACC) inhibitors using a combined virtual screening and Bayesian modeling approach. This efficient strategy aids in discovering new drug candidates for metabolic syndrome treatments.
Area of Science:
- Biochemistry
- Pharmacology
- Computational Chemistry
Background:
- Acetyl-CoA Carboxylases (ACC) are key targets for treating metabolic syndrome and related diseases like obesity, NAFLD, and T2DM.
- Developing effective ACC inhibitors is crucial for managing these prevalent health conditions.
Purpose of the Study:
- To validate potent ACC inhibitors using a multi-faceted computational strategy.
- To identify novel compounds with high efficacy against ACC.
Main Methods:
- Employed a virtual screening strategy integrating Bayesian categorization modeling and molecular docking.
- Utilized protein-ligand interaction fingerprint (PLIF) for binding site analysis.
- Validated a high-performing Bayesian model (AUC: 0.972 training, 0.955 test) for compound screening.
Main Results:
- A combined ligand-based Bayesian model and structure-based virtual screening approach was successful.
- Identified top four compounds with excellent IC50 values against ACC.
- Achieved high enrichment rates for hit compounds within the top 1% of the validation library.
Conclusions:
- The integrated computational strategy demonstrates high efficiency in identifying ACC inhibitors.
- This approach is valuable for accelerating the discovery of inhibitors for ACC and other therapeutic targets.
- The findings support the development of new treatments for metabolic disorders.
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