抗病毒分子分析中的深度学习如何识别了抗SARS-CoV-2抑制剂
Mohammed Ali1,2, In Ho Park3,4, Junebeom Kim1,2
1Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Biomedicines
|December 23, 2023
概括
人工智能加速了对抗SARS-CoV-2化合物的发现. 研究人员确定了有前途的候选药物,包括硫氨酸酸,可以减少小鼠肺部的病毒载荷.
科学领域:
- 计算化学是一种计算化学.
- 病毒学 病毒学
- 药物发现 药物发现
背景情况:
- 人工智能 (AI) 正在改变药物发现.
- 确定有效的抗SARS-CoV-2治疗方法仍然是优先事项.
研究的目的:
- 采用计算实验方法来识别潜在的抗SARS-CoV-2化合物.
- 开发和利用一个预测AI模型用于复合活性评估.
主要方法:
- 使用人工智能驱动的预测模型选了70万种化合物.
- 在Caco-2和Vero细胞系进行了体外测试以验证.
- 采用聚类来识别核类模拟抑制剂.
主要成果:
- 在100种选择的化合物中,有19种在体外表现出抑制活性.
- 八种化合物表现出剂量依赖的活性,EC50值从17μM.
- 确定了阿扎西奥普林和氨酸作为有希望的候选药物;氨酸降低了小鼠肺中的病毒载量.
结论:
- 人工智能显著加快了潜在抗病毒药物的识别.
- 该研究验证了用于药物发现的计算-实验工作流.
- 提奥伊诺辛酸显示出作为对SARS-CoV-2的体内治疗剂的潜力.
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