基于GcForest的化合物-蛋白相互作用预测模型及其在发现针对CD47的小分子药物的应用
Wenying Shan1,2, Lvqi Chen1, Hao Xu3,4
1Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing, China.
Frontiers in chemistry
|November 6, 2023
概括
我们开发了一个人工智能模型用于化合物-蛋白相互作用 (CPI) 预测,在有限的数据上表现出色. 这种方法确定了两个新的CD47抑制剂,证明了其在药物发现中的效率.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 化合物-蛋白相互作用 (CPI) 的预测对于药物发现至关重要.
- 机器学习 (ML) 和深度学习 (DL) 越来越多地用于CPI预测.
- 传统的机器学习方法在特定目标的有限数据上扎.
研究的目的:
- 提出一种新的虚拟选模型,用于CPI预测.
- 为了应对在CPI预测中小数据集的挑战.
- 为了确定新的CD47.7抑制剂.
主要方法:
- 使用word2vec用于低维嵌入化合物SMILES和蛋白质序列.
- 采用改造的多粒级布森林 (gcForest) 作为分类器.
- 开发了一种能够处理原始数据和调整小数据集复杂性的模型.
主要成果:
- 拟议的模型优于现有的CPI预测方法,特别是在具有挑战性的小数据集上.
- 成功预测了针对CD47-SIRPα相互作用的两个新型小分子抑制剂.
- 鉴定的抑制剂显示IC50值为3.57和4.79微米.
结论:
- 开发的AI工具对于CPI预测是高效和强大的,即使数据有限.
- 该模型证明了在识别潜在药物候选人的能力.
- 这些发现突出了人工智能在加速药物发现的潜力,用于几乎没有已知的抑制剂的目标.
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