Drug property prediction, especially toxicity, helps reduce risks in a range of real-world applications. In this paper, we aim to apply various machine-learning models for solving the drug toxicity prediction problem. Among various machine-learning approaches, we select five suitable representatives: random forest, multi-layer perceptron, logistic regression, graph convolutional neural network, and graph isomorphism network (GIN) for conducting experiments on six datasets for toxicity prediction, including Tox 21, ClinTox, ToxCast, SIDER, HIV, and BACE. We design the GIN with four hidden layers and select the Adam optimizer with the learning rate ${10}^{-4}$ and the batch size $256$. Furthermore, we use a batch norm layer inside each of the GIN hidden layers. Experimental results show that the designed GIN model is most efficient in distinguishing between safe and toxic drugs and outperforms the others under the supervision of ROC AUC score and recall.

药物特性预测，尤其是毒性，有助于降低一系列实际应用中的风险。在本文中，我们的目标是应用各种机器学习模型来解决药物毒性预测问题。在各种机器学习方法中，我们选择了五种合适的代表：随机森林、多层感知器、逻辑回归、图卷积神经网络和图同构网络（GIN），在六个数据集上进行毒性预测实验，包括Tox 21、 ClinTox、ToxCast、SIDER、HIV 和 BACE。我们设计了具有四个隐藏层的GIN，并选择具有学习率的Adam优化器${10}^{-4}$和批量大小$256$。此外，我们在每个 GIN 隐藏层内使用批量归一化层。实验结果表明，设计的 GIN 模型在区分安全药物和有毒药物方面最有效，并且在 ROC AUC 评分和召回率的监督下优于其他模型。