Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

• Published in: Briefings in bioinformatics 2023-03, Vol.24 (2)
• Main Authors: Ji, Kai-Yue, Liu, Chong, Liu, Zhao-Qian, Deng, Ya-Feng, Hou, Ting-Jun, Cao, Dong-Sheng
• Format: Article
• Language: English
• Subjects: Algorithms; Bioactive compounds; Biological activity; Fishing; Ligands; Methods; Predictions; Target recognition; Web services
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbad014

Abstract Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand–target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.