Affiliations 

  • 1 School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia. Electronic address: alhasbary@gmail.com
  • 2 School of Computer Sciences, Universiti Sains Malaysia, 11800, Pulau Pinang, Malaysia. Electronic address: nurulhashimah@usm.my
  • 3 Malaysian Institute of Pharmaceuticals and Nutraceuticals (IPharm), National Institutes of Biotechnology Malaysia (NIBM), Halaman Bukit Gambir, 11700, Gelugor, Pulau Pinang, Malaysia. Electronic address: zuraidah@nibm.my
Comput Biol Med, 2025 Jan;184:109351.
PMID: 39536385 DOI: 10.1016/j.compbiomed.2024.109351

Abstract

Natural products are invaluable resources in drug discovery due to their substantial structural diversity. However, predicting their interactions with druggable protein targets remains a challenge, primarily due to the limited availability of bioactivity data. This study introduces CTAPred (Compound-Target Activity Prediction), an open-source command-line tool designed to predict potential protein targets for natural products. CTAPred employs a two-stage approach, combining fingerprinting and similarity-based search techniques to identify likely drug targets for these bioactive compounds. Despite its simplicity, the tool's performance is comparable to that of more complex methods, demonstrating proficiency in target retrieval for natural product compounds. Furthermore, this study explores the optimal number of reference compounds most similar to the query compound, aiming to refine target prediction accuracy. The findings demonstrated the superior performance of considering only the most similar reference compounds for target prediction. CTAPred is freely available at https://github.com/Alhasbary/CTAPred, offering a valuable resource for deciphering natural product-target associations and advancing drug discovery.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.