In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

• Published in: Journal of cheminformatics Vol. 15; no. 1; p. 48
• Main Authors: Yang, Su-Qing, Zhang, Liu-Xia, Ge, You-Jin, Zhang, Jin-Wei, Hu, Jian-Xin, Shen, Cheng-Ying, Lu, Ai-Ping, Hou, Ting-Jun, Cao, Dong-Sheng
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 23.04.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Analysis; Chemistry; Chemistry and Materials Science; Chemogenomic; Computational Biology/Bioinformatics; Computer applications; Computer Applications in Chemistry; Documentation and Information in Chemistry; Drug discovery; Ensemble model; Predictions; Proteins; Software; Target detection; Target prediction; Theoretical and Computational Chemistry; XGBoost; Ensemble model; Chemogenomic; XGBoost; Target prediction
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-023-00720-0

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing. Graphical Abstract