Activity Prediction of Small Molecule Inhibitors for Antirheumatoid Arthritis Targets Based on Artificial Intelligence

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.