Motivation: Target prediction is a crucial step in modern drug discovery. However, existing
experimental approaches to target prediction are time-consuming and costly.
Results: The LigTMap server provides a fully automated workflow to identify targets from 17
target classes with >6000 proteins. It is a hybrid approach, combining ligand similarity search
with docking and binding similarity analysis, to predict putative targets. In the validation
experiment, LigTMap achieved a top-10 success rate of almost 70%, with an average precision
rate of 0.34. The class-specific prediction method improved the success rate further with enhanced
precision. In an independent benchmarking test, LigTMap showed good performance compared to
the currently best target prediction servers. LigTMap provides straightaway the PDB of a
predicted target and the optimal ligand binding mode, which could facilitate structure-based drug
design and the repurposing of existing drugs.
InstitutionUniversity of Macau
ORCID For Submitting Author0000-0002-3695-7758
Declaration of Conflict of InterestNo conflict of interests
Version Notespreprint version 1