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Related Experiment Video

Updated: Sep 6, 2025

Simultaneous Measurement of HDAC1 and HDAC6 Activity in HeLa Cells Using UHPLC-MS

Simultaneous Measurement of HDAC1 and HDAC6 Activity in HeLa Cells Using UHPLC-MS

Published on: August 10, 2017

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Rourou Li¹, Yujia Tian¹, Zhenwu Yang¹

¹State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, China.

Molecular Diversity

|June 23, 2022

Summary

This summary is machine-generated.

This study identifies key structural features of potent Histone Deacetylase 1 (HDAC1) inhibitors. Machine learning models predicted the best inhibitor structures, aiding in the development of new cancer therapies.

Keywords:

Classification models Histone deacetylase (HDAC) 1 inhibitor Machine learning method Structure clustering Structure–activity relationship (SAR)

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Assays for Validating Histone Acetyltransferase Inhibitors

Assays for Validating Histone Acetyltransferase Inhibitors

Published on: August 6, 2020

Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Histone deacetylase 1 (HDAC1) is a crucial target in various cancers.
Understanding structure-activity relationships (SAR) of HDAC1 inhibitors is vital for drug development.

Purpose of the Study:

To build predictive models for identifying potent HDAC1 inhibitors.
To investigate the substructural features influencing HDAC1 inhibitor activity.

Main Methods:

A dataset of 7313 HDAC1 inhibitors was curated.
Molecular structures were represented using various fingerprints (e.g., ECFP4).
Eighty classification models were built using five machine learning algorithms, including XGBoost.

Main Results:

The XGBoost model (15A_2) using ECFP4 fingerprints achieved the highest accuracy (88.08%) and MCC (0.76).
HDAC1 inhibitors were clustered into 31 subsets, revealing key substructural features.
Specific substructures like benzimidazole and hydroxamic acid derivatives were linked to high activity.

Conclusions:

Machine learning, particularly XGBoost, effectively predicts HDAC1 inhibitor activity.
Identified substructures provide valuable insights for designing novel, high-activity HDAC1 inhibitors for cancer treatment.