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D2Screen:嵌入预训练的表示学习模型和用于虚拟选的分子对接

Tingli Qian¹, Jiao Zhou^1,2, Xiang Liu^3,4

¹Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China.

ACS medicinal chemistry letters

|October 15, 2025

概括

一个新的D2Screen管道结合了深度学习和分子对接,用于药物发现. 这种方法成功地发现了针对耐药突变的新型SARS-CoV-2抑制剂.

科学领域:

计算化学和药物发现
药理学中的人工智能
病毒学和传染病学.

背景情况:

虚拟选和分子对接是药物发现中的关键计算方法.
深度学习提供了一种强大的方法,通过利用现有的药理学数据来识别生物活性化合物.
病毒感染的耐药性,如COVID-19,需要开发新的治疗策略.

研究的目的:

开发和验证一个新的计算管道,D2Screen (深度学习和基于对接的选),集成深度学习和分子对接.
与传统方法相比,提高复合选的准确性和效率.
发现针对SARS-CoV-2主要蛋白酶 (Mpro) 的新型抑制剂,包括那些对抗耐药变种有效的抑制剂.

主要方法:

开发一个端到端的管道,D2Screen,将深度学习算法与分子对接模拟相结合.
使用BedROC和EF1%指标评估D2Screen的性能,将其与独立的深度学习和分子对接方法进行比较.
在一个案例研究中应用D2Screen来识别针对SARS-CoV-2 Mpro的抑制剂,重点关注对抗耐药突变的疗效.

主要成果:

D2Screen显示出比个人深度学习或分子对接方法更高的精度,由改进的BedROC和EF1%指标证明.
管道成功确定了一系列针对SARS-CoV-2 Mpro的非共价抑制剂,其中最强效的化合物具有5.9μM的IC50.
抑制剂对耐药Mpro突变 (T21I-E166V) 的敏感性显著降低,与Nirmatrelvir的1000倍以上的降低相比,疗效降低了7.6倍.

结论:

D2Screen管道代表了计算药物发现的重大进步,有效地整合了深度学习和分子对接.
这种混合方法提高了预测的准确性,并促进了强效药物候选者的识别.
D2Screen成功发现了一种针对SARS-CoV-2 Mpro的有前途的非共价抑制剂,对抗耐药菌株具有显著活性,为新的COVID-19治疗提供了潜力.

关键词:

这就是SARS-CoV-2病毒.抗药性抗药性是一种抗药性.深度学习是一种深度学习.分子对接的分子对接.这是主要的蛋白质酶.虚拟选是虚拟的选.

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