Abstract：
Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.
摘要：
确保化合物的安全性和有效性在小分子药物开发中至关重要。在药物开发的后期，有毒化合物构成了重大挑战，失去宝贵的资源和时间。使用深度学习模型对化合物毒性的早期和准确预测提供了一种有前途的解决方案，可以在药物发现期间减轻这些风险。在这项研究中,我们介绍了几种旨在评估不同类型化合物毒性的深度学习模型的发展，包括急性毒性，致癌性，hERG_心脏毒性（人类ether-a-go-go相关基因引起的心脏毒性），肝毒性，和诱变性。为了解决数据大小的固有变化，标签类型,以及在不同类型的毒性中的分布，我们采用了不同的培训策略。我们的第一种方法涉及利用图卷积网络（GCN）回归模型来预测急性毒性，在腹膜内用PearsonR0.76、0.74和0.65取得了显著的性能，静脉注射,和口服给药途径，分别。此外，我们训练了多个GCN二元分类模型，每种都适合特定类型的毒性。这些模型表现出很高的曲线下面积（AUC）得分，预测致癌性的AUC为0.69、0.77、0.88和0.79，hERG_心脏毒性，致突变性,和肝毒性，分别。此外,我们使用批准的药物数据集来确定模型使用预测评分的适当阈值.我们将这些模型整合到虚拟筛选管道中，以评估其在识别潜在低毒候选药物方面的有效性。我们的研究结果表明，这种深度学习方法有可能通过加快选择低毒性化合物来显著降低与药物开发相关的成本和风险。因此,本研究开发的模型有望成为早期候选药物筛选和选择的关键工具.
