ACtriplet:通过整合三重损失和预训,改进了活动悬崖预测的深度学习模型
Xinxin Yu1, Yimeng Wang1, Long Chen1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
Journal of pharmaceutical analysis
|September 2, 2025
概括
这项研究介绍了ACtriplet,这是一种新的深度学习模型,可以提高药物发现中的活动悬崖 (ACs) 的预测. 通过整合三倍损失和预训练,ACtriplet 提高了利用现有数据进行分子优化.
科学领域:
- 计算化学
- 药物发现
- 机器学习
背景情况:
- 活动悬崖 (AC) 对于优化分子结构至关重要,但对结构-活动关系 (SAR) 模型构成挑战.
- 现有的深度学习 (DL) 模型往往难以准确预测AC的效力.
- 需要改进DL方法,以便更好地利用数据进行AC预测.
研究的目的:
- 开发一种新型深度学习模型,
- 增强具有较小结构差异的类似化合物的分子功率的预测准确性.
- 在药物发现和优化早期改进现有数据的利用.
主要方法:
- 在面部识别中常用的三重损失, 具有预训练策略.
- 开发了针对活动悬崖的ACtriplet预测模型.
- 在30个基准数据集上与多个基线深度学习模型进行了广泛的比较.
主要成果:
- 与没有预训练的深度学习模型相比,ACtriplet表现显著优越.
- 探索和分析预训练对数据表示的影响.
- 一个案例研究证实了可解释性模块能够合理地解释预测结果.
结论:
- 通过ACtriplet模型,可以更好地利用现有的药物发现数据.
- 这种方法可以推动深度学习在早期药物发现和优化中的潜力,特别是当数据有限时.
- 该模型的可解释性增强了对人工智能驱动的分子设计的信任和理解.
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