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Updated: Mar 8, 2026
Simultaneous Measurement of HDAC1 and HDAC6 Activity in HeLa Cells Using UHPLC-MS
Published on: August 10, 2017
A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors
Huabin Hu1, Jie Xia2, Dongmei Wang3
1State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China. dreaming@imm.ac.cn.
Abstract:
Histone deacetylase 3 (HDAC3) has been recently identified as a potential target for the treatment of cancer and other diseases, such as chronic inflammation, neurodegenerative diseases, and diabetes. Virtual screening (VS) is currently a routine technique for hit identification, but its success depends on rational development of VS strategies. To facilitate this process, we applied our previously released benchmarking dataset, i.e., MUBD-HDAC3 to the evaluation of structure-based VS (SBVS) and ligand-based VS (LBVS) combinatorial approaches. We have identified FRED (Chemgauss4) docking against a structural model of HDAC3, i.e., SAHA-3 generated by a computationally inexpensive "flexible docking", as the best SBVS approach and a common feature pharmacophore model, i.e., Hypo1 generated by Catalyst/HipHop as the optimal model for LBVS. We then developed a pipeline that was composed of Hypo1, FRED (Chemgauss4), and SAHA-3 sequentially, and demonstrated that it was superior to other combinations in terms of ligand enrichment. In summary, we present the first highly-validated, rationally-designed VS strategy specific to HDAC3 inhibitor discovery. The constructed pipeline is publicly accessible for the scientific community to identify novel HDAC3 inhibitors in a time-efficient and cost-effective way.
Insights
We developed a validated virtual screening (VS) strategy for discovering histone deacetylase 3 (HDAC3) inhibitors. This pipeline combines structure-based and ligand-based VS approaches for efficient drug discovery.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Pharmacology
Background:
- Histone deacetylase 3 (HDAC3) is a key target for treating cancer, inflammation, neurodegenerative diseases, and diabetes.
- Effective virtual screening (VS) strategies are crucial for identifying potential drug candidates targeting HDAC3.
- Previous VS approaches lacked specific validation for HDAC3 inhibitor discovery.
Purpose of the Study:
- To evaluate and optimize combinatorial structure-based VS (SBVS) and ligand-based VS (LBVS) approaches for HDAC3.
- To develop a highly validated, rational VS strategy for novel HDAC3 inhibitor discovery.
- To provide a publicly accessible pipeline for efficient and cost-effective identification of HDAC3 inhibitors.
Main Methods:
- Utilized the MUBD-HDAC3 benchmarking dataset to assess SBVS and LBVS methods.
- Evaluated FRED (Chemgauss4) docking against an HDAC3 structural model (SAHA-3) for SBVS.
- Assessed a common feature pharmacophore model (Hypo1) generated by Catalyst/HipHop for LBVS.
- Developed and tested a sequential pipeline combining Hypo1, FRED (Chemgauss4), and SAHA-3.
Main Results:
- Identified FRED (Chemgauss4) docking with the SAHA-3 model as the optimal SBVS approach.
- Determined the Hypo1 pharmacophore model as the best LBVS strategy.
- The sequential pipeline (Hypo1 -> FRED -> SAHA-3) significantly outperformed other combinations in ligand enrichment.
- The developed strategy demonstrated superior performance in identifying potential HDAC3 inhibitors.
Conclusions:
- Presented the first highly validated, rationally designed VS strategy specifically for HDAC3 inhibitor discovery.
- The constructed pipeline offers a time-efficient and cost-effective solution for the scientific community.
- This validated approach facilitates the identification of novel therapeutic agents targeting HDAC3.
