活动 悬崖信息对比学习用于分子性质预测
Wan Xiang Shen1,2, Chao Cui3,4, Xiaorui Su1
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Research square
|December 16, 2024
概括
活动悬崖 (ACs) 在药物设计中至关重要. 一种名为AC-awareness (ACA) 的新方法通过使模型对ACs敏感,从而提高药物发现的生物活性预测,从而改善分子表示学习.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 定量结构-活性关系 (QSAR) 建模对于药物设计至关重要.
- 图形神经网络 (GNN) 擅长分子活动预测,但经常错过活动悬崖 (AC).
- 类似分子具有不同的生物活性的AC对当前的GNN构成挑战.
研究的目的:
- 引入AC-awareness (ACA),以增强活动建模的分子表示学习.
- 开发ACANet,一种基于AC的对比学习方法.
- 为了提高GNN对化学化合物中AC的敏感性.
主要方法:
- 开发了AC意识 (ACA) 作为分子表示学习的诱导偏见.
- 通过共同优化隐性空间度量学习和目标空间任务性能来实现ACA.
- 集成的ACANet,一个基于AC的对比学习方法,与现有的GNN架构.
主要成果:
- 在39个基准数据集上,AC知情分子表示始终优于标准模型.
- 在生物活性预测的回归和分类任务中表现出卓越的表现.
- 展示了强大的药理动力学和与安全相关的分子性质的预测能力.
结论:
- ACA显著增强分子表示学习,用于活动预测.
- 在早期药物发现中,ACANet为识别和精制化合物提供了宝贵的工具.
- 基于活动的分子表示对于有效的虚拟查和药物开发至关重要.
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