Fuadah Yunendah Nur, Pramudito Muhammad Adnan, Firdaus Lulu, Vanheusden Frederique J, Lim Ki Moo
Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia.
ACS Omega. 2024 Dec 12;9(51):50796-50808. doi: 10.1021/acsomega.4c09356. eCollection 2024 Dec 24.
This study introduces an innovative computational approach using hybrid machine learning models to predict toxicity across eight critical end points: cardiac toxicity, inhalation toxicity, dermal toxicity, oral toxicity, skin irritation, skin sensitization, eye irritation, and respiratory irritation. Leveraging advanced cheminformatics tools, we extracted relevant features from curated data sets, incorporating a range of descriptors such as Morgan circular fingerprints, MACCS keys, Mordred calculation descriptors, and physicochemical properties. The consensus model was developed by selecting the best-performing classifier-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), or Support Vector Machines (SVM)-for each descriptor, optimizing predictive accuracy and robustness across the end points. The model obtained strong predictive performance, with area under the curve (AUC) scores ranging from 0.78 to 0.90. This framework offers a reliable, ethical, and effective in silico approach to chemical safety assessment, underscoring the potential of advanced computational methods to support both regulatory and research applications in toxicity prediction.
本研究介绍了一种创新的计算方法，该方法使用混合机器学习模型来预测八个关键终点的毒性：心脏毒性、吸入毒性、皮肤毒性、口服毒性、皮肤刺激性、皮肤致敏性、眼睛刺激性和呼吸道刺激性。利用先进的化学信息学工具，我们从经过整理的数据集中提取了相关特征，纳入了一系列描述符，如摩根圆形指纹、MACCS键、Mordred计算描述符和物理化学性质。通过为每个描述符选择性能最佳的分类器——随机森林（RF）、极端梯度提升（XGBoost）或支持向量机（SVM），开发了共识模型，优化了各终点的预测准确性和稳健性。该模型获得了强大的预测性能，曲线下面积（AUC）分数在0.78至0.90之间。该框架为化学安全评估提供了一种可靠、符合伦理且有效的计算机模拟方法，突出了先进计算方法在支持毒性预测的监管和研究应用方面的潜力。
