Delijewski Marcin, Haneczok Jacek
Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
Erste Group IT, Am Belvedere 1, 1100 Vienna, Austria.
Med Drug Discov. 2021 Mar;9:100077. doi: 10.1016/j.medidd.2020.100077. Epub 2020 Dec 24.
Over the past few years, AI has been considered as potential important area for improving drug development and in the current urgent need to fight the global COVID-19 pandemic new technologies are even more in focus with the hope to speed up this process. The purpose of our study was to identify the best repurposing candidates among FDA-approved drugs, based on their predicted antiviral activity against SARS-CoV-2.
This article describes a drug discovery screening based on a supervised machine learning model, trained on data encoded in chemical fingerprints, representing particular molecular substructures. Predictive performance of our model has been evaluated using so-called scaffold splits offering a state-of-the-art setup for assessing model's ability to generalize to new chemical spaces, critical for drug repurposing applications.
Our study identified zafirlukast as the best repurposing candidate for COVID-19.
Zafirlukast could be potent against COVID-19 both due to its predicted antiviral properties and its ability to attenuate the so called . Thus, these two critical mechanisms of action may be combined in one drug as a novel and promising pharmacotherapy in the current pandemic.
在过去几年中，人工智能一直被视为改善药物研发的潜在重要领域，在当前抗击全球新冠疫情的迫切需求下，新技术更是备受关注，以期加速这一进程。我们研究的目的是基于FDA批准药物对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的预测抗病毒活性，确定最佳的药物重新利用候选药物。
本文描述了一种基于监督机器学习模型的药物发现筛选方法，该模型在化学指纹编码的数据上进行训练，这些数据代表特定的分子亚结构。我们使用所谓的支架拆分评估了模型的预测性能，为评估模型推广到新化学空间的能力提供了一种先进的设置，这对药物重新利用应用至关重要。
我们的研究确定扎鲁司特是新冠疫情最佳的药物重新利用候选药物。
扎鲁司特可能对新冠病毒有效，这既是由于其预测的抗病毒特性，也是由于其减轻所谓……的能力。因此，在当前疫情中，这两种关键作用机制可能结合在一种药物中，成为一种新颖且有前景的药物治疗方法。
