基准测试作为恢复功能和选SARS-CoV-2 Mpro的FDA药物的ANI潜力
Irem N Zengin1, M Serdar Koca2,3, Omer Tayfuroglu1
1Department of Chemistry, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
Journal of computer-aided molecular design
|March 27, 2024
概括
人工智能机器学习 (ANI-ML) 的潜力为分子对接提供了一个强大的新评分功能,在准确性和计算成本方面与现有方法竞争. 这种方法通过准确预测相互作用和选数百万名候选人来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 分子建模分子建模
背景情况:
- 分子对接对于识别候选药物至关重要.
- 现有的评分函数在准确性和计算效率方面存在局限性.
- 准确预测主机-客户互动对于可靠的对接至关重要.
研究的目的:
- 引入和评估人工智能机器学习 (ANI-ML) 潜力,作为分子对接中的新型回归函数.
- 为了比较ANI-ML潜力的性能与已建立的评分函数.
- 在药物查活动中展示ANI-ML潜力的实用性.
主要方法:
- 在分子对接中利用ANI-ML潜力作为回归函数.
- 在CASF-2016数据集上的基准ANI-ML潜力与其他34个评分函数相比.
- 结合GOLD-PLP对接,ANI-ML复刻和分子动力学 (MD) 模拟,使用自由能量方法进行药物选.
- 对SARS-CoV-2主要蛋白酶 (Mpro) 进行选的FDA批准的药物.
主要成果:
- ANI-ML潜力展示了具有竞争力的"对接能力",具有类似计算成本的当前评分功能.
- 在CASF-2016数据集中测试的34个分数函数中,ANI-ML排在前5位.
- 将ANI-ML与GOLD-PLP结合起来,提高了排名第一的对接解决方案的准确性.
- 查方案确定了六个有希望的药物分子对抗SARS-CoV-2 Mpro,与之前的研究一致.
结论:
- ANI-ML潜力代表了分子对接得分函数的重大进步.
- ANI-ML的准确性和效率有助于大规模的候选药物查.
- 经过验证的查方法对识别COVID-19等疾病的新疗法充满了希望.
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