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LigTMap: Ligand and Structure-Based Target Identification and Activity Prediction for Small Molecules

preprint
submitted on 06.09.2020, 07:17 and posted on 07.09.2020, 10:36 by Faraz Shaikh, Hio Kuan Tai, Nirali Desai, Shirley Siu
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.

Funding

MYRG2017-00146-FST

History

Email Address of Submitting Author

siuwengin@gmail.com

Institution

University of Macau

Country

Macau

ORCID For Submitting Author

0000-0002-3695-7758

Declaration of Conflict of Interest

No conflict of interests

Version Notes

preprint version 1

Exports