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. 2023 Sep 12;24(1):338.
doi: 10.1186/s12859-023-05455-1.

Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features

Binyou Wang  1   2 , Jianmin Guo  1 , Xiaofeng Liu  1 , Yang Yu  1   3 , Jianming Wu  4   5   6   7 , Yiwei Wang  8   9   10
Affiliations

Prediction of the effects of small molecules on the gut microbiome using machine learning method integrating with optimal molecular features

Binyou Wang et al. BMC Bioinformatics. .

Abstract

Background: The human gut microbiome (HGM), consisting of trillions of microorganisms, is crucial to human health. Adverse drug use is one of the most important causes of HGM disorder. Thus, it is necessary to identify drugs or compounds with anti-commensal effects on HGM in the early drug discovery stage. This study proposes a novel anti-commensal effects classification using a machine learning method and optimal molecular features. To improve the prediction performance, we explored combinations of six fingerprints and three descriptors to filter the best characterization as molecular features.

Results: The final consensus model based on optimal features yielded the F1-score of 0.725 ± 0.014, ACC of 82.9 ± 0.7%, and AUC of 0.791 ± 0.009 for five-fold cross-validation. In addition, this novel model outperformed the prior studies by using the same algorithm. Furthermore, the important chemical descriptors and misclassified anti-commensal compounds are analyzed to better understand and interpret the model. Finally, seven structural alerts responsible for the chemical anti-commensal effect are identified, implying valuable information for drug design.

Conclusion: Our study would be a promising tool for screening anti-commensal compounds in the early stage of drug discovery and assessing the potential risks of these drugs in vivo.

Keywords: Anti-commensal effect; Consensus model; Human gut microbiome; Machine learning; Molecular features.

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Conflict of interest statement

The authors declare that they have no competing interest.

Figures

Fig. 1
Fig. 1
Flow chart for the development of consensus model for predicting commensal or anti-commensal compounds
Fig. 2
Fig. 2
Structures of the six misclassified compounds in the external validation set
Fig. 3
Fig. 3
SE values of the single descriptor and full descriptors models
Fig. 4
Fig. 4
IG value distributions of the KRFP fragments
See this image and copyright information in PMC

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