FastTargetPred

FastTargetPred predicts potential protein targets of small-molecule compounds to support target identification in drug discovery and cheminformatics.

Key Features:

• Local computation: Runs on local machines and supports Linux, MacOS, and Windows operating systems.
• High-throughput annotation: Capable of annotating approximately 100,000 compounds within a few hours on standard personal computing resources.
• Implementation: Implemented in Python (version ≥3.7) and C, relying on the Python Standard Library.
• Analytical approach: Uses structural similarity computations to infer protein targets from compound structures.

Scientific Applications:

• Drug discovery: Predicts putative protein targets for small molecules to prioritize compounds for experimental validation.
• Cheminformatics: Enables large-scale target annotation and compound prioritization for chemical library screening and bioactivity analysis.

Methodology:

Employs structural similarity computations against a dataset of confirmed protein–ligand activities sourced from the curated ChEMBL 25 database.