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Machine learning-based classification models for non-covalent Bruton’s tyrosine kinase inhibitors: predictive ability and interpretability

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Abstract

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton’s tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.

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Acknowledgements

We thank the Molecular Networks GmbH, Nuremberg, Germany for providing the programs SONNIA for our scientific work.

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Authors and Affiliations

  1. State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People’s Republic of China

    Guo Li, Jiaxuan Li, Yujia Tian, Yunyang Zhao, Xiaoyang Pang & Aixia Yan

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Contributions

Data collection, main modeling, and manuscript drafting: GL; k-means algorithm script, article grammar modification, and guidance: JL and YT; DNN algorithm script, submission guidance, and submission format modification: YZ; Supplemental calculation of the latest crystal structure 8FLL: XP; Correspondence: AY. All authors reviewed and approved the final manuscript.

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Correspondence to Aixia Yan.

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Li, G., Li, J., Tian, Y. et al. Machine learning-based classification models for non-covalent Bruton’s tyrosine kinase inhibitors: predictive ability and interpretability. Mol Divers (2023). https://doi.org/10.1007/s11030-023-10696-6

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