诱导 - 关联式元学习管道与人类认知模式用于看不见的药物目标相互作用预测
Xiaoqing Lian1, Tianxu Lv1, Jie Zhu1
1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, 214122, China.
概括
生物桥通过从有限的数据中学习可转移的结合原理来提高药物向相互作用的预测. 这种方法改善了对新药标对的预测,特别是对于未见的蛋白质.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 现有的药物向相互作用 (DTI) 模型与蛋白质结构变异作斗争,限制了它们的概括性.
- 当前的模型通常依赖于广泛的预先学习的约束原则或详细的注释,这些并不总是可用的.
研究的目的:
- 开发一个新的计算框架,BioBridge,用于预测药物向相互作用 (DTI).
- 通过利用弱相关引用和有限序列数据的见解来克服现有的DTI模型的局限性.
主要方法:
- 生物桥利用一种由科学专业知识启发的感应-关联管道.
- 结合多级编码器与对抗性培训,学习可转移的约束原则.
- 采用一个动态的原型元学习框架,用于从相关注释中关联见解.
主要成果:
- 生物桥显著优于现有模型,特别是在预测与看不见的蛋白质相互作用方面.
- 在表皮生长因子受体和腺素受体的虚拟查中表现出强大的有效性.
- 使用有限的序列数据和弱相关的注释,实现有效的预测.
结论:
- 生物桥提供了一个强大的和可通用的方法来预测新的药物向相互作用.
- 该模型学习可转移原则的能力提高了其在药物发现中的适用性,特别是对于数据有限的目标.
- 生物桥显示出加速识别新药候选者的巨大潜力.
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