HDAC1 PREDICTOR

HDAC1 PREDICTOR predicts potential histone deacetylase 1 (HDAC1) inhibitors using QSAR and machine learning to prioritize compounds with inhibitory activity relative to Vorinostat (IC50 = 11.08 nM).

Key Features:

• QSAR Modeling: Employs Quantitative Structure-Activity Relationship (QSAR) models using 2D RDKit molecular descriptors and ECPF4 (Extended Connectivity Fingerprint) circular fingerprints.
• Machine Learning Algorithms: Implements Random Forest, Gradient Boosting, and Support Vector Machine algorithms within QSAR modeling.
• Molecular Fragment Analysis: Identifies molecular fragments that contribute positively or negatively to HDAC1 inhibitory activity to inform rational design.
• Virtual Screening and Prioritization: Predicts and ranks compounds for HDAC1 inhibitory potential to support rational drug design and candidate selection.

Scientific Applications:

• Oncology: Assists identification and optimization of HDAC1 inhibitors for cancer therapeutics.
• Autoimmune diseases: Facilitates discovery of HDAC1-targeting compounds relevant to autoimmune disease mechanisms.
• Neurodegenerative disorders: Aids discovery of HDAC1 inhibitors potentially relevant to neurodegenerative disease research.
• Lead prioritization: Ranks compounds with predicted inhibitory activity relative to Vorinostat (IC50 = 11.08 nM) for candidate selection.

Methodology:

Computes 2D RDKit molecular descriptors and ECPF4 fingerprints and trains QSAR models using Random Forest, Gradient Boosting, and Support Vector Machine algorithms to predict HDAC1 inhibitory activity.