基于边界选择的负采样策略和多级图神经网络的神经网络的HCAR1抗剂查
Mengmeng Fan1, Dakuo He2, Qian Liu1
1College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China.
Computer methods and programs in biomedicine
|February 3, 2026
概括
研究人员开发了一种AI框架,以发现用于癌症治疗的HCAR1抗剂. 一个新的多GNN模型成功识别了一种有前途的HCAR1抑制剂,推动了瘤免疫治疗药物发现.
科学领域:
- 在瘤学瘤学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 碳酸受体1 (HCAR1) 或乳酸受体,与癌症进展有关,是癌症治疗的标.
- 对于HCAR1抗剂的传统药物查受到不平衡的数据和不完整的分子表示的阻碍,限制了治疗选择.
研究的目的:
- 开发HCAR1抗剂的准确预测模型,以帮助瘤免疫治疗.
- 克服传统药物查方法的局限性,以识别潜在的HCAR1向癌症治疗方法.
主要方法:
- 使用负采样构建了一个平衡的HCAR1目标活动数据集.
- 开发了一种集指纹,分子图形和碎片特征用于活动预测的多GNN模型.
- 使用多GNN模型,物理化学过和分子对接,选了数百万种化合物.
主要成果:
- 多GNN模型在预测HCAR1目标活动方面超过了八种最先进的方法.
- 从选大约一千万个分子中确定了五种候选化合物.
- 通过体外测试发现了一种有前途的HCAR1抑制剂,IC50为22.39μM.
结论:
- 介绍了一种基于人工智能的新型框架,用于针对HCAR1的药物发现.
- 突出了潜在的化合物,用于进一步开发癌症治疗.
- 证明了多GNN方法在识别新药候选药物的有效性.
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