Prediction of Human Dihydroorotate Dehydrogenase Inhibitor Activity by a Weighted Average Ensemble-Based Prediction

Authors : Eyüp Sıramkaya, Sema Atasever

Pages : 245-253

Doi:10.46810/tdfd.1800883

View : 30 | Download : 87

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :This study concentrates on the weighted average ensemble-based prediction of pIC50 value for Human Dihydroorotate Dehydrogenase (hDHODH) using hybrid molecular fingerprints. By querying the ChEMBL database for IC50 data, a diverse collection of 1585 molecules was obtained, and these values were converted to pIC50 values to develop ensemble-based prediction models. We used the weighted average (W.Avg) of Light Gradient Boosting Machine (LGBM), Bootstrap Aggregating (Bagging), and Random Forest (RF) algorithms to estimate pIC50 values. Model performance was evaluated using 5x3 repeated K-fold cross-validation (CV). Root mean square error (RMSE) and mean squared error (MSE) were used as the performance metrics. The W.Avg combination demonstrated overall success beyond individual models. The results showed that our ensemble model outperformed all other baseline models with R²=0.8266, RMSE=0.6568, and MSE=0.4337. Paired t-test results indicate that the W.Avg model is statistically significantly superior to the other models in terms of R², RMSE, and MSE (p < 0.05). This ensemble-based method accelerated hDHODH inhibitor discovery by reducing screening time and increasing predictive accuracy.
Keywords : Makine öğrenmesi, hibrit moleküler parmak izleri, hDHODH inhibitörü

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