Department of Computer Science, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran.
Department of QA, Kimia Zist Parsian Pharmaceutical Company, Zanjan, Iran.
PLoS One. 2024 Sep 12;19(9):e0299342. doi: 10.1371/journal.pone.0299342. eCollection 2024.
Monkeypox (MPXV) is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle MPXV. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the MPXV. This paper proposes a computational framework for MPXV antiviral prediction. To do this, we have generated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. The target protein structure is modeled using homology modeling and, then, refined and validated. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of MPXV antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for MPXV treatment.
猴痘（MPXV）是近年来导致发病和死亡的传染性病毒之一。尽管它对公共卫生构成威胁，但目前尚无批准的药物来应对 MPXV。另一方面，药物重定位是一种有前途的筛选方法，可以利用计算方法为新出现的疾病和病毒低成本引入已批准的药物。因此，药物重定位是为 MPXV 提出已批准药物的一种很有前途的方法。本文提出了一种用于 MPXV 抗病毒预测的计算框架。为此，我们生成了一个新的病毒-抗病毒数据集。此外，我们应用了几种机器学习和一种深度学习方法来进行病毒-抗病毒预测。通过对接研究调查了学习方法建议的药物。使用同源建模来模拟目标蛋白结构，然后对其进行细化和验证。据我们所知，这项工作是第一个研究深度学习方法预测 MPXV 抗病毒药物的工作。筛选结果证实，盐酸噻洛酮、伐昔洛韦、利巴韦林、法匹拉韦和巴洛沙韦马波西利是治疗 MPXV 的有效药物。
