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Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial
Guomeng Xing1, Li Liang1, Chenglong Deng1
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China.
Abstract:
Rheumatoid arthritis (RA) is a chronic autoimmune disease, which is compared to "immortal cancer" in industry. Currently, SYK, BTK, and JAK are the three major targets of protein tyrosine kinase for this disease. According to existing research, marketed and research drugs for RA are mostly based on single target, which limits their efficacy. Therefore, designing multitarget or dual-target inhibitors provide new insights for the treatment of RA regarding of the specific association between SYK, BTK, and JAK from two signal transduction pathways. In this study, machine learning (XGBoost, SVM) and deep learning (DNN) models were combined for the first time to build a powerful integrated model for SYK, BTK, and JAK. The predictive power of the integrated model was proved to be superior to that of a single classifier. In order to accurately assess the generalization ability of the integrated model, comprehensive similarity analysis was performed on the training and the test set, and the prediction accuracy of the integrated model was specifically analyzed under different similarity thresholds. External validation was conducted using single-target and dual-target inhibitors, respectively. Results showed that our model not only obtained a high recall rate (97%) in single-target prediction, but also achieved a favorable yield (54.4%) in dual-target prediction. Furthermore, by clustering dual-target inhibitors, the prediction performance of model in various classes were proved, evaluating the applicability domain of the model in the dual-target drug screening. In summary, the integrated model proposed is promising to screen dual-target inhibitors of SYK/JAK or BTK/JAK as RA drugs, which is beneficial for the clinical treatment of rheumatoid arthritis.
Insights
This study introduces an integrated machine and deep learning model to discover dual-target inhibitors for rheumatoid arthritis (RA), improving upon single-target treatments by targeting SYK, BTK, and JAK kinases.
Area of Science:
- Computational chemistry and drug discovery
- Machine learning applications in pharmacology
- Rheumatoid arthritis (RA) therapeutic strategies
Background:
- Rheumatoid arthritis (RA) is a chronic autoimmune disease often compared to 'immortal cancer'.
- Current RA treatments primarily target single protein tyrosine kinases (SYK, BTK, JAK), limiting efficacy.
- Developing multitarget or dual-target inhibitors offers a promising therapeutic avenue for RA.
Purpose of the Study:
- To develop a novel integrated machine learning and deep learning model for predicting dual-target inhibitors of SYK, BTK, and JAK kinases.
- To evaluate the model's predictive power and generalization ability for identifying novel RA drug candidates.
- To assess the model's utility in screening dual-target inhibitors, specifically for SYK/JAK or BTK/JAK combinations.
Main Methods:
- Integration of machine learning (XGBoost, SVM) and deep learning (DNN) models for synergistic predictive performance.
- Comprehensive similarity analysis between training and test sets to validate model generalization.
- External validation using single-target and dual-target inhibitors, including clustering analysis of dual-target compounds.
Main Results:
- The integrated model demonstrated superior predictive power compared to single classifiers.
- Achieved a high recall rate of 97% for single-target prediction.
- Obtained a favorable yield of 54.4% in dual-target prediction, with performance validated across different inhibitor classes.
Conclusions:
- The proposed integrated model is effective for screening dual-target inhibitors of SYK/JAK or BTK/JAK.
- This approach offers a promising strategy for developing improved RA therapeutics.
- The model's applicability domain was evaluated for dual-target drug screening in RA treatment.
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