Abstract

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149, respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota–Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

Graphic abstract

中文翻译：

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用机器学习和深度学习方法对化学诱导的血液毒性进行计算机模拟预测

摘要

化学诱导的血液毒性是药物发现中的一个重要问题，因为它发生时通常是致命的。对可能引起血液毒性的化学物质给予特别关注对我们非常有用。在本研究中，我们专注于使用机器学习 (ML) 和深度学习 (DL) 方法对化学诱导的血液毒性进行计算机模拟预测。我们收集了一个包含 632 种血液毒性化学品和 1525 种批准的无血液毒性药物的大型数据集。计算模型是使用集成在在线化学建模环境 (OCHEM) 上的几种不同机器学习和深度学习算法构建的。基于三个最好的个体模型，开发了一个共识模型。它在外部验证中产生了 0.83 的预测准确度和 0.77 的平衡准确度。使用随机森林回归和分类算法 (RFR) 和 QNPR 描述符开发的共识模型和最佳个体模型分别可在 https://ochem.eu/article/135149 上获得。还研究了 8 种常用分子特性和化学诱导的血液毒性的相关性。几种分子特性对化学诱导的血液毒性有明显的区分作用。此外，使用来自 Klekota-Roth 指纹的子结构的频率分析确定了 12 个负责化学血液毒性的结构警报。这些结果应该为药物发现和环境风险评估中的血液毒性评估提供有意义的知识和有用的工具。

图形摘要