Zetta Darlene Nabila, Srisongkram Tarapong
Graduate School in the Program of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
ACS Omega. 2025 Jul 10;10(28):30450-30466. doi: 10.1021/acsomega.5c02188. eCollection 2025 Jul 22.
Stacking ensemble learning is a method to improve model generalization and robustness. Deep neural networks have demonstrated significant potential for predicting chemical properties due to their effectiveness in learning complex patterns within the chemical space. Nevertheless, an individual model may rely on a single molecular feature set that might not explicitly explain all of the relationships between drugs and targets. Integrating a stacking ensemble with deep learning (DL) and various molecular features could potentially enhance the learning process and improve the ability to capture complex relationships between molecular structures and bioactivities. Chemicals binding to thyroid peroxidase (TPO) are associated with thyroid dysfunction, highlighting the importance of assessing their potential risks to human health and the environment. In this study, we developed a novel stacking ensemble neural network model to predict TPO inhibitory activity. This model integrates convolutional neural networks, bidirectional long short-term memory networks, and attention mechanisms combined with top-performing molecular fingerprints to generate three probability features. These features were used as inputs in a meta-decision model, enhancing learning probability. The meta-model was validated through y-randomization, ensuring that the model does not produce outputs randomly. The applicability domain of this model was also assessed to affirm the reliability and trustworthiness of each prediction. The final attention-based meta-model achieved a recall of 0.55, specificity of 0.95, Matthews correlation coefficient of 0.56, area under the curve of 0.85, balanced accuracy of 0.75, and precision of 0.70. Furthermore, the developed model was generalized to other external test sets, effectively predicting TPO inhibition and identifying potentially toxic compounds from a selected Thai indigenous vegetable. These findings will contribute to the application of stacking ensemble neural networks in the toxicity screening of chemical compounds, enhancing their learning ability to capture more diverse chemical risk assessments.
堆叠集成学习是一种提高模型泛化能力和鲁棒性的方法。深度神经网络由于在学习化学空间内的复杂模式方面的有效性，已在预测化学性质方面展现出巨大潜力。然而，单个模型可能依赖于单一的分子特征集，而该特征集可能无法明确解释药物与靶点之间的所有关系。将堆叠集成与深度学习（DL）以及各种分子特征相结合，可能会增强学习过程，并提高捕捉分子结构与生物活性之间复杂关系的能力。与甲状腺过氧化物酶（TPO）结合的化学物质与甲状腺功能障碍有关，这凸显了评估其对人类健康和环境潜在风险的重要性。在本研究中，我们开发了一种新型的堆叠集成神经网络模型来预测TPO抑制活性。该模型集成了卷积神经网络、双向长短期记忆网络和注意力机制，并结合性能最佳的分子指纹来生成三个概率特征。这些特征被用作元决策模型的输入，提高了学习概率。通过y随机化对元模型进行了验证，确保模型不会随机产生输出。还评估了该模型的适用范围，以确认每个预测的可靠性和可信度。最终基于注意力的元模型的召回率为0.55，特异性为0.95，马修斯相关系数为0.56，曲线下面积为0.85，平衡准确率为0.75，精确率为0.70。此外，所开发的模型被推广到其他外部测试集，有效地预测了TPO抑制作用，并从选定的泰国本土蔬菜中识别出潜在的有毒化合物。这些发现将有助于堆叠集成神经网络在化合物毒性筛选中的应用，增强其捕捉更多样化化学风险评估的学习能力。
