Generating 3D Molecules for Target Protein Binding-猫眼课题宝

DOI：

10.48550/arxiv.2204.09410

发表时间：

2022-04

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Meng Liu;Youzhi Luo;Kanji Uchino;Koji Maruhashi;Shuiwang Ji

药物发现中的一个基本问题是设计与特定蛋白质结合的分子。为了利用机器学习方法解决这个问题，我们提出了一个新颖而有效的框架，称为GraphBP，通过将特定类型和位置的原子逐个放置到给定的结合位置来生成与给定蛋白质结合的3D分子。特别是，在每个步骤中，我们首先使用3D图神经网络从中间上下文信息中获得几何感知和化学信息的表示。这样的上下文包括在先前步骤中放置的给定结合部位和原子。其次，为了保持期望的等方差特性，我们根据设计的辅助分类器选择一个局部参考原子，然后构造一个局部球面坐标系。最后，为了放置一个新原子，我们生成了它的原子类型和相对位置w.r.t.。通过流动模型构建了局部坐标系。我们还考虑按顺序生成感兴趣的变量，以捕获它们之间的潜在依赖关系。实验证明，我们的GraphBP可以有效地生成具有靶向蛋白质结合位点的结合能力的3D分子。我们的实施可在https://github.com/divelab/GraphBP.上获得

A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind to given proteins by placing atoms of specific types and locations to the given binding site one by one. In particular, at each step, we first employ a 3D graph neural network to obtain geometry-aware and chemically informative representations from the intermediate contextual information. Such context includes the given binding site and atoms placed in the previous steps. Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system. Finally, to place a new atom, we generate its atom type and relative location w.r.t. the constructed local coordinate system via a flow model. We also consider generating the variables of interest sequentially to capture the underlying dependencies among them. Experiments demonstrate that our GraphBP is effective to generate 3D molecules with binding ability to target protein binding sites. Our implementation is available at https://github.com/divelab/GraphBP.

Generating 3D Molecules for Target Protein Binding

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