A-HIOT

A-HIOT integrates chemical-space stacked ensembles and protein-space deep learning to perform virtual screening and optimize hit/lead molecules for target-specific activity.

Key Features:

• Integrated Approach: Combines ligand-based and structure-based virtual screening (VS) techniques to analyze compound–protein interactions.
• Chemical Space-Driven Stacked Ensemble: Employs a stacked ensemble within chemical space leveraging multiple open-source algorithms to identify potential hit molecules.
• Protein Space-Driven Deep Learning Architectures: Uses deep learning models operating in protein space to optimize identified hits into selective molecules for fixed protein receptors.
• Two-Phase Workflow: Performs sequential identification via chemical-space ensemble followed by optimization via protein-space deep learning.
• High-Quality Predictions: Integration of chemical and protein spaces reduces false positive rates in virtual screening.
• Robustness and Generalizability: Validated on benchmark datasets for CXC chemokine receptor 4 (CXCR4) and androgen receptor (AR) with cross-validation accuracies of 94.8% for hit identification and 81.9% for optimization in CXCR4, and higher performance on independent datasets.

Scientific Applications:

• Hit Prioritization for Biochemical Assays: Prioritizes compounds for biochemical testing by reducing false positives from virtual screening.
• Empirical Drug Discovery: Supports identification and optimization of hit/lead molecules across diverse protein targets.
• Target-Specific Optimization: Refines hits to improve selectivity and activity for fixed protein receptors such as CXCR4 and AR.

Methodology:

Identification is performed via a chemical space-driven stacked ensemble leveraging multiple open-source algorithms; optimization is performed via deep learning models within protein space and integrates ligand-based and structure-based VS techniques.