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Li2021 - HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors

ABSTRACT: The model predicts the inhibitory potential of small molecules against Histone deacetylase 3 (HDAC3), a relevant human target for cancer, inflammation, neurodegenerative diseases and diabetes. The authors have used a dataset of 1098 compounds from ChEMBL and validated the model using the benchmark MUBD-HDAC3. Model Type: Predictive machine learning model. Model Relevance: Probability that the molecule is a HDAC3 inhibitor Model Encoded by: Sarima Chiorlu (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos1n4b

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406210001 | BioModels | 2024-08-06

REPOSITORIES: BioModels

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HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors.

Li Shan S Ding Yu Y Chen Miaomiao M Chen Ya Y Kirchmair Johannes J Zhu Zihao Z Wu Song S Xia Jie J

Molecular informatics 20201123 3

Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features f ...[more]

PMID: 33067876

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