用于基于变压器预测强效化合物的meta-learning
Hengwei Chen1, Jürgen Bajorath2
1Department of Life Science Informatics and Data Science, B-IT, Lamarr Institute for Machine Learning and Artificial Intelligence, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115, Bonn, Germany.
Scientific reports
|September 26, 2023
概括
超级学习通过改进强效化合物预测来增强用于药物发现的机器学习,特别是在有限的训练数据下. 与标准变压器模型相比,这种方法显示了显著的性能增长,并产生了更强大的化合物.
科学领域:
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
- 计算化学计算化学
背景情况:
- 有限的训练数据是机器学习 (ML) 在药物发现中的关键挑战,特别是在化合物设计和活动预测方面.
- 深度学习模型通常需要大量的数据,这阻碍了它们在低数据场景中的应用.
- 超级学习提供了一个潜在的解决方案,通过模型输出组合和超级数据利用,使低数据模式中的学习成为可能.
研究的目的:
- 探索元学习在药物发现中使用生成式变压器模型预测强效化合物的有效性.
- 在不同的微调数据条件下,与标准变压器相比,评估元学习模型的性能.
- 评估由元学习模型产生的化合物的效力和选择性.
主要方法:
- 开发和应用超级学习策略与变压器模型用于生成性化合物设计.
- 训练模型,从不同活动类别的弱效模板中预测高强度化合物.
- 与传统的变压器模型进行比较,使用不同数量的微调数据来比较元学习模型的性能.
主要成果:
- 超级学习始终在预测模型性能上产生了统计学上显著的改进.
- 当微调数据有限时,性能增长尤为明显.
- 通过meta-learning模型生成的化合物与其他变压器产生的化合物相比,具有更高的强度和更大的强度差异.
结论:
- 超级学习在药物发现中显示出低数据化合物设计的巨大潜力.
- 这种方法有效地提高了对强效化合物的预测,即使训练数据稀缺.
- 超级学习有助于生成具有更好的功效和选择性概况的化合物.
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