Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure–activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC₅₀ values were extracted from the ‘DrugRepV’ database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T²⁷⁴) dataset. The robustness of the developed models was cross-evaluated using William’s plot. The highly robust computational models are integrated into the web server. The ‘anti-Ebola’ web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola. We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.

Graphic abstract

中文翻译：

---

抗埃博拉：通过机器学习预测埃博拉病毒抑制剂的倡议

自 1976 年以来，埃博拉病毒是一种致命的病原体，导致了一系列频繁的爆发。尽管世界各地的研究人员做出了各种努力，但它的死亡率和致死率仍然很高。对于抗病毒药物的发现，计算工作被认为非常有用。因此，我们开发了一个“抗埃博拉”网络服务器，通过具有实验性抗埃博拉活性的可用分子的定量结构-活性关系信息。三百零五种独特的抗埃博拉病毒化合物及其各自的 IC ₅₀值是从“DrugRepV”数据库中提取的。后来，这些化合物被用来提取分子描述符，然后进行基于回归的模型开发。使用十倍交叉验证采用了强大的机器学习技术，即支持向量机、随机森林和人工神经网络。采用随机化方法后，最佳预测模型显示 Pearson 的相关系数在训练/测试上介于 0.83 到 0.98 之间（T ²⁷⁴) 数据集。使用威廉图对所开发模型的稳健性进行了交叉评估。高度鲁棒的计算模型被集成到网络服务器中。“抗埃博拉”网络服务器可在 https://bioinfo.imtech.res.in/manojk/antiebola 免费获得。我们预计这将有助于科学界开发针对埃博拉病毒的有效抑制剂。

图形摘要