GPCR-A17 MAAP:映射调制剂,激动剂和对抗剂,以预测下一个生物活性标
Ana B Caniceiro1,2,3,4, Ana M B Amorim1,2,3,4, Nícia Rosário-Ferreira1,2,3,4
1CNC-UC - Center for Neuroscience and Cell Biology, University of Coimbra, Rua Larga, Ed FMUC, Piso 1, 3004-504, Coimbra, Portugal.
本研究介绍了GPCR-A17调节器,激进分子,对抗分子预测器 (MAAP),这是一个集体机器学习模型,用于预测G蛋白结合受体相互作用. 通过准确识别GPCR-A17标的潜在治疗剂,MAAP加速了药物发现.
科学领域:
- 药理学和生物信息学 药理学和生物信息学
- 计算机化药物发现技术
背景情况:
- G蛋白结合受体 (GPCRs) 对于细胞信号传递至关重要,并代表了重要的药物标.
- GPCR-A17亚家族与各种疾病有关,需要新的治疗策略.
研究的目的:
- 开发一种先进的机器学习模型,用于预测GPCR-A17相互作用中的激动剂,对抗剂和调节剂的功能作用.
- 为加速GPCR-A17相关疾病的药物发现过程.
主要方法:
- 开发了一个集体机器学习模型,GPCR-A17调制器,激进分子,对抗分子预测器 (MAAP).
- MAAP集成了XGBoost,随机森林和LightGBM算法.
- 该模型使用来自多个数据库的3000多个连接体和6900个蛋白质-连接体相互作用的数据集进行训练.
主要成果:
- 该MAAP模型在测试和独立验证数据集上表现出强大的预测性能,高F1分数和AUC值.
- 一个Ki过的互动子集进一步提高了预测准确性.
- 获得的F1分数为0.9179 (测试) 和0.7151 (独立验证),AUC分别为0.9766和0.8591.
结论:
- GPCR-A17 MAAP有效地预测了连接体相互作用,大大帮助了实验验证.
- 该模型加速了针对GPCR-A17标的潜在药物候选者的识别.
- 开发的工具和数据在GitHub上公开提供,以支持进一步的研究.
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