Progressive docking: a hybrid QSAR/docking approach for accelerating in silico high throughput screening.

Area of Science:

• Computational chemistry
• Drug discovery
• Bioinformatics

Background:

• Virtual screening is crucial for identifying drug candidates.
• Traditional docking methods can be computationally intensive.
• Quantitative Structure-Activity Relationship (QSAR) models can predict compound activity.

Purpose of the Study:

• To develop and validate a "progressive docking" approach to accelerate virtual screening.
• To reduce computational costs in drug discovery while maintaining high accuracy.
• To identify novel potent ligands for targets like human sex hormone binding globulin (SHBG).

Main Methods:

• Combined protein-ligand docking and ligand-based QSAR.
• Utilized docking scores from processed compounds to build predictive QSAR models.
• Applied the progressive docking approach to drug-like compounds from the NCI database against multiple targets.

Main Results:

• Progressive docking reduced computations by 1.2- to 2.6-fold compared to traditional docking.
• Achieved 80-99% hit recovery rates.
• Successfully identified novel potent nonsteroidal SHBG ligands.

Conclusions:

• Progressive docking substantially accelerates high-throughput screening, particularly for large datasets and high-accuracy docking.
• This method offers a significant computational advantage in drug discovery pipelines.
• The identified novel SHBG ligands warrant further investigation for therapeutic potential.