在连续隐性空间中使用变异自编码器预测药物-标绑定亲和力
概括
这项研究引入了一种新的深度学习方法,通过在连续空间中建模来预测药物标结合亲和力 (DTA). 我们的方法提高了DTA预测的准确性,提高了药物发现效率.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 准确的药物标结合亲和力 (DTA) 预测对于有效的药物发现至关重要.
- 对于DTA,现有的深度学习模型通常在向量空间中表示分子和蛋白质.
- 需要更细致的建模方法来捕捉输入样本的多样性.
研究的目的:
- 为DTA预测提出一种在连续空间中运行的新型深度学习模型.
- 通过改进DTA预测,提高药物发现过程的准确性和效率.
- 共同学习使用高斯分布的药物和目标的隐藏表示.
主要方法:
- 使用简化分子输入线输入系统 (SMILES) 编码药物.
- 通过预训练的语言模型对目标序列进行表征.
- 使用剩余封闭卷积神经网络提取相关信息.
- 共同学习药物和目标隐藏的表示作为高斯分布.
主要成果:
- 拟议的连续空间模型与最先进的矢量表示方法相比,表现出更高的性能.
- 对基准数据集的实验评估验证了高斯分布方法的有效性.
- 该方法在DTA预测中获得了更高的精度.
结论:
- 在连续空间中建模药物-标相互作用,与传统的基于矢量方法相比,具有优势.
- 作为高斯分布的药物和目标表示的联合学习提高了DTA预测的准确性.
- 这种方法有可能为更高效,更精确的药物发现管道做出重大贡献.
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