Abstract：
Skin irritation is an adverse effect associated with various substances, including chemicals, drugs, or natural products. Dipterocarpol, extracted from Dipterocarpus alatus, contains several skin benefits notably anticancer, wound healing, and antibacterial properties. However, the skin irritation of dipterocarpol remains unassessed. Quantitative structure-activity relationship (QSAR) is a recommended tool for toxicity assessment involving less time, money, and animal testing to access unavailable acute toxicity data. Therefore, our study aimed to develop a highly accurate machine learning-based QSAR model for predicting skin irritation. We utilized a stacked ensemble learning model with 1064 chemicals. We also adhered to the recommendations from the OECD for QSAR validation. Subsequently, we used the proposed model to explore the cytotoxicity of dipterocarpol on keratinocytes. Our findings indicate that the model displayed promising statistical quality in terms of accuracy, precision, and recall in both 10-fold cross-validation and test datasets. Moreover, the model predicted that dipterocarpol does not have skin irritation, which was confirmed by the cell-based assay. In conclusion, our proposed model can be applied for the risk assessment of skin irritation in untested compounds that fall within its applicability domain. The web application of this model is available at https://qsarlabs.com/#stackhacat.
摘要：
皮肤刺激是与各种物质相关的不利影响，包括化学品,毒品,或天然产品。Dipterocarpol,从双叶茎中提取，含有几种皮肤益处，特别是抗癌，伤口愈合，和抗菌性能。然而,diptercarpol的皮肤刺激仍未评估。定量结构-活性关系（QSAR）是毒性评估的推荐工具，涉及更少的时间，钱，和动物试验以获取不可获得的急性毒性数据。因此,我们的研究旨在开发一种基于机器学习的高精度QSAR模型来预测皮肤刺激。我们使用了具有1064种化学物质的堆叠集成学习模型。我们还遵守了经合组织关于QSAR验证的建议。随后，我们使用提出的模型来探索diptercarpol对角质形成细胞的细胞毒性。我们的发现表明，该模型在准确性方面显示出有希望的统计质量，精度,并在10倍交叉验证和测试数据集中召回。此外，该模型预测diptercarpol没有皮肤刺激，这通过基于细胞的测定得到证实。总之,我们提出的模型可应用于未测试化合物中皮肤刺激的风险评估，这些化合物属于其适用性范围。此模型的Web应用程序可在https://qsarlabs.com/#stackhacat获得。
