The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

计算生物学在膜蛋白靶标药物开发中的应用得益于深度学习驱动的结构预测、结构解析速度和分辨率的提高、基于机器学习结构的设计和大数据评估的最新发展。最近基于机器学习工具的蛋白质结构预测为水溶性和膜蛋白提供了令人惊讶的可靠结果，但对靶向膜蛋白的药物的开发存在限制。膜蛋白的结构转变在跨膜信号传导过程中起着核心作用，并且经常受到治疗化合物的影响。解决动态跨膜信号网络的结构和功能基础，特别是在天然膜或细胞环境中，仍然是药物开发的核心挑战。应对这一挑战需要实验和计算工具之间的相互作用，例如用于量化细胞信号网络的分子相互作用及其通过潜在药物的调节的超分辨率光学显微镜，用于确定细胞中蛋白质结构转变的冷冻电子显微镜。天然细胞膜和整个细胞，以及用于数据分析和预测细胞信号网络结构和功能的计算工具，以及生成有前途的候选药物。