使用预训-BERT和贝叶斯主动学习进行分子性质预测:一种数据效率高的药物设计方法
Muhammad Arslan Masood1, Samuel Kaski2,3, Tianyu Cui2
1Department of Computer Science, Aalto University, Espoo, Finland. arslan.masood@aalto.fi.
Journal of cheminformatics
|April 24, 2025
概括
这项研究通过使用预训练的BERT模型与主动学习来提高分子选择来增强药物发现. 这种方法可以更有效地识别有毒化合物,需要更少的实验测试.
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
- 生物信息学是一种生物信息学.
背景情况:
- 为测试优先考虑的化合物在药物发现中至关重要.
- 主动学习通常只使用标记数据,忽略未标记的分子数据.
- 这限制了预测性能和分子选择效率.
研究的目的:
- 通过整合预训练过的变压器模型,改善药物发现中的积极学习.
- 通过利用未标记的分子数据来解决完全监督的积极学习的局限性.
- 为了提高分子选择和预测性能在化合物优先级.
主要方法:
- 集成了一个基于变压器的BERT模型,在126万个化合物上进行预训练,进入一个积极的学习管道.
- 从不确定性估计中学习分离的表示,使用预训练的分子表示.
- 在Tox21和ClinTox数据集上进行实验.
主要成果:
- 与传统主动学习相比,实现了同等的有毒化合物识别,代次数减少了50%.
- 证明预训练的BERT表示创建了一个结构化的嵌入空间,以使用有限的标记数据进行可靠的不确定性估计.
- 在药物发现中展示了改进的模型性能和获取效率.
结论:
- 高质量的分子表示对于药物发现中的积极学习成功至关重要.
- 拟议的框架将预训练过的变压器模型与贝叶斯主动学习相结合,以进行高效的选.
- 这种方法为优化药物研究中的化合物优先级提供了一个可扩展的基础.
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