大规模预训提高了基于主动学习的虚拟选的样本效率
Zhonglin Cao1, Simone Sciabola1, Ye Wang1
1Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States.
Journal of chemical information and modeling
|March 5, 2024
概括
预训练的变压器和图形神经网络模型显著提高了药物发现中的虚拟查效率. 这些模型更快地识别顶级候选药物,减少了对庞大的化合物库进行选的需要.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
背景情况:
- 虚拟查对于从大型化合物库中识别候选药物至关重要.
- 积极学习和贝叶斯优化提高了虚拟选效率.
- 替代机器学习模型对于预测化合物特性至关重要.
研究的目的:
- 在贝叶斯优化主动学习框架内评估预训练的基于变压器的语言模型和图形神经网络.
- 评估这些模型在加快识别命中化合物的性能.
主要方法:
- 使用贝叶斯优化主动学习框架.
- 使用预训练过的基于变压器的语言模型和图形神经网络作为替代模型.
- 在一个超大复合库 (99.5万个复合物) 上进行基准性能测试.
主要成果:
- 最好的预训练模型在仅选0.6%的图书馆后,识别了58.97%的前5万种化合物.
- 与之前的最先进的基线相比,实现了8%的改善.
- 在基于结构和基于连接体的药物发现场景中都表现出卓越的性能.
结论:
- 预训练的模型在基于主动学习的虚拟选中显著提高了准确性和样本效率.
- 这些模型提供了一种强大的方法来加速药物发现中的成功识别.
- 这些发现表明,预训练的模型是导航广的化学空间的宝贵工具.
相关概念视频
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
02:07Improving Translational Accuracy
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...