Geometry-complete perceptron networks for 3D molecular graphs
Geometry-complete perceptron networks for 3D molecular graphs
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DOI：
10.1093/bioinformatics/btae087
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发表时间：
2022-11
期刊：
影响因子：
5.8
通讯作者：
Alex Morehead;Jianlin Cheng
中科院分区：
文献类型：
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作者：
Alex Morehead;Jianlin Cheng
摘要 动机 几何深度学习领域最近对蛋白质结构预测和设计等多个科学领域产生了深远的影响，导致了传统机器学习领域内外的方法论进步。本着这种精神，在这项工作中，我们引入了 GCPNet，一种新的手性感知 SE(3) 等变图神经网络，专为 3D 生物分子图的表示学习而设计。我们表明，与之前的 3D 生物分子表示学习方法不同，GCPNet 广泛适用于生物分子结构上的各种不变或等变节点级、边缘级和图级任务，同时能够（1）学习 3D 分子的重要手性特性和（2）检测外力场。结果 在四个不同的分子几何任务中，我们证明了 GCPNet 对蛋白质-配体结合亲和力的预测 (1) 达到了 0.608 的统计显着相关性，超过 5%，高于当前最先进的方法； (2) 对于蛋白质结构排名，目标-局部和数据集-全局相关性分别达到统计显着性 0.616 和 0.871； (3)对于牛顿多体系统建模，任务平均均方误差小于0.01，比现有方法好15%以上； (4) 在分子手性识别方面，达到了 98.7% 的最先进预测精度，优于迄今为止任何其他机器学习方法。可用性和实施用于训练新模型或重现我们结果的源代码、数据和说明可在 https://github.com/BioinfoMachineLearning/GCPNet 上免费获取。
Abstract Motivation The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. Results Across four distinct molecular-geometric tasks, we demonstrate that GCPNet’s predictions (1) for protein–ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. Availability and implementation The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.