OLB-AC:通过深度图形学习和活动悬崖来优化连接体生物活性
Yueming Yin1,2, Haifeng Hu1, Jitao Yang1
1School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Bioinformatics (Oxford, England)
|June 18, 2024
概括
这项研究引入了一种新的深度图形学习方法,以优化活动悬崖附近的药物分子,改善生物活性预测并产生新的有效化合物. 该方法通过识别具有改进性质的优化配体来增强药物发现.
科学领域:
- 计算化学和化学信息学
- 药物的发现和开发.
- 在化学领域的机器学习和人工智能.
背景情况:
- 深度图形学习 (DGL) 对于基于连接体的虚拟选至关重要.
- 活动悬崖 (ACs) 是一个挑战,因为微小的分子变化会大大改变生物活性.
- 现有的DGL模型改善了在AC附近的预测,但优化机会仍未得到充分探索.
研究的目的:
- 开发一种新的深度图形学习方法,用于同时预测和优化活动悬崖附近的联体生物活性.
- 引入一种直接优化连接物分子的方法,为增强生物活性提供参考.
- 探索活动悬崖在药物发现中优化连接体生物活性的潜力.
主要方法:
- 提出了一种名为OLB-AC (优化活动悬崖附近的基生物活性) 的新方法,利用深度图形学习.
- 开发了一个细心的图形重建神经网络来重建和优化连接体.
- 采用从生物活性预测梯度获得的对抗性表示来进行连接物优化.
主要成果:
- OLB-AC成功优化了667个分子,在训练数据集之外识别了49个已知的高活性/抑制剂/无毒配体.
- 产生了27个新的分子对,其中的转换在训练集中不存在.
- 在生物活性预测方面取得了最先进的表现,在27/33数据集上显示了最佳的皮尔森相关系数 (r2),改善了7.2%-22.9%.
结论:
- OLB-AC方法有效地优化了活动悬崖附近的联体生物活性,在药物发现方面显示出显著的潜力.
- 这种方法产生了新的分子转换,并提高了生物活性预测的准确性.
- 代码和数据集是公开的,这有助于进一步研究DGL用于药物优化.
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