相关实验视频
Updated: Jun 28, 2025

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Engineering Antiviral Agents via Surface Plasmon Resonance
Published on: June 14, 2022
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用于预测SARS-CoV-2抑制剂的语言模型
Andrew E Blanchard1, John Gounley1, Debsindhu Bhowmik1
1Oak Ridge National Laboratory, Oak Ridge, TN, USA.
概括
深度学习模型通过生成和评分新型蛋白质标的潜在药物候选者来加速药物发现. 这种计算方法显著减少了新疗法的开发时间.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 随着COVID-19的流行,药物设计中迫切需要快速计算方法.
- 现有的药物发现管道面临着针对新型蛋白质标的速度和效率方面的挑战.
研究的目的:
- 开发和应用深度学习语言模型,用于自动生成和评分候选药物.
- 加速对治疗标的抑制剂的鉴定,包括SARS-CoV-2 Mpro和PLpro.
主要方法:
- 在约96亿个分子上预训练基于BERT的深度学习语言模型.
- 实现高计算性能 (603 petaflops) 并显著减少预训练时间.
- 微调语言模型与数千个蛋白质标的结合亲和数据.
- 使用遗传算法与语言模型相结合,以实现最佳候选人识别.
主要成果:
- 显示模型预训练时间从几天减少到几个小时.
- 将分子数据集大小增加了近一个数量级,从而提高了模型的稳定性.
- 成功应用这些模型来识别SARS-CoV-2 Mpro和PLpro.pro的潜在抑制剂.
结论:
- 深度学习语言模型提供了一种强大的,可泛化的方法,以加速新兴治疗点的药物发现.
- 开发的计算工具可以显著加快新药候选药物的识别.
- 这种方法对未来针对各种疾病的药物设计工作具有前景.
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