用图像化方法和可解释AI预测化学物质毒性，提升准确率与透明度。
Combining Deep Learning and Explainable AI for Toxicity Prediction of Chemical Compounds
- 将化学结构转为2D图像，用DenseNet121模型进行毒性预测
- 在Tox21数据集上达到与传统模型相当的性能
- 结合Grad-CAM可视化，揭示毒性相关分子区域，适合药物安全研究
本研究基于Tox21数据集，在计算毒理学领域探讨化学化合物毒性预测任务。通过分析化学毒性背景，对比多种机器学习与深度学习方法的性能、鲁棒性与可解释性。提出一种基于DenseNet121的新型图像化处理流程，将化学结构的二维图形表示输入模型。同时采用Grad-CAM可解释AI技术，可视化模型决策依据，定位影响毒性分类的关键分子区域。实验表明，该方法在预测性能上与传统模型相当，验证了深度卷积网络在化学生物信息学中的潜力。研究强调，结合图像表示与可解释AI能同时提升预测准确性与模型透明度。
原文摘要 · Abstract (English)
The task here is to predict the toxicological activity of chemical compounds based on the Tox21 dataset, a benchmark in computational toxicology. After a domain-specific overview of chemical toxicity, we discuss current computational strategies, focusing on machine learning and deep learning. Several architectures are compared in terms of performance, robustness, and interpretability. This research introduces a novel image-based pipeline based on DenseNet121, which processes 2D graphical representations of chemical structures. Additionally, we employ Grad-CAM visualizations, an explainable AI technique, to interpret the model's predictions and highlight molecular regions contributing to toxicity classification. The proposed architecture achieves competitive results compared to traditional models, demonstrating the potential of deep convolutional networks in cheminformatics. Our findings emphasize the value of combining image-based representations with explainable AI methods to improve both predictive accuracy and model transparency in toxicology.
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