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Histone Deacetylases

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A lazy learning-based QSAR classification study for screening potential histone deacetylase 8 (HDAC8) inhibitors.

SAR and QSAR in environmental research
Histone deacetylases 8 (HDAC8) is an enzyme repressing the transcription of various genes including tumour suppressor gene and has already become a target of human cancer treatment. In an effort to facilitate the discovery of HDAC8 inhibitors, two qu...

Identification of potential histone deacetylase1 (HDAC1) inhibitors using multistep virtual screening approach including SVM model, pharmacophore modeling, molecular docking and biological evaluation.

Journal of biomolecular structure & dynamics
Histone Deacetylases (HDACs) play a significant role in the regulation of gene expression by modifying histones and non-histone substrates. Since they are key regulators in the reversible epigenetic mechanism, they are considered as promising drug ta...

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation.

Briefings in bioinformatics
Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified throu...

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

Molecular informatics
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 use...

Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...

FID-Net: A versatile deep neural network architecture for NMR spectral reconstruction and virtual decoupling.

Journal of biomolecular NMR
In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies o...

Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations.

International journal of biological macromolecules
The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetyl...

A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Biophysical chemistry
Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutagenicity of the commonly used hydroxamates (zinc-binding groups, ZBGs) are also increasing. Considering ...