A universal framework for accurate and efficient geometric deep learning of molecular systems.
A universal framework for accurate and efficient geometric deep learning of molecular systems.
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DOI：
10.1038/s41598-023-46382-8
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发表时间：
2023-11-06
影响因子：
4.6
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中科院分区：
文献类型：
--
作者：
分子科学解决了涉及不同类型和大小的分子及其复合物的广泛问题。最近，几何深度学习，特别是图神经网络，在分子科学应用中表现出了良好的性能。然而，大多数现有的工作往往强加有针对性的诱导偏差到一个特定的分子系统，并在应用于大分子或大规模的任务时是低效的，从而限制了它们的应用到许多现实世界的问题。为了解决这些挑战，我们提出了PAMNet，这是一个通用框架，用于准确有效地学习任何分子系统中不同大小和类型的三维（3D）分子的表示。受分子力学的启发，PAMNet引入了一种物理信息偏差，以明确地模拟局部和非局部相互作用及其组合效应。因此，PAMNet可以减少昂贵的操作，使其具有时间和内存效率。在广泛的基准研究中，PAMNet在三种不同学习任务（小分子特性、RNA 3D结构和蛋白质-配体结合亲和力）的准确性和效率方面均优于最先进的基线。我们的研究结果突出了PAMNet在广泛的分子科学应用中的潜力。
Molecular sciences address a wide range of problems involving molecules of different types and sizes and their complexes. Recently, geometric deep learning, especially Graph Neural Networks, has shown promising performance in molecular science applications. However, most existing works often impose targeted inductive biases to a specific molecular system, and are inefficient when applied to macromolecules or large-scale tasks, thereby limiting their applications to many real-world problems. To address these challenges, we present PAMNet, a universal framework for accurately and efficiently learning the representations of three-dimensional (3D) molecules of varying sizes and types in any molecular system. Inspired by molecular mechanics, PAMNet induces a physics-informed bias to explicitly model local and non-local interactions and their combined effects. As a result, PAMNet can reduce expensive operations, making it time and memory efficient. In extensive benchmark studies, PAMNet outperforms state-of-the-art baselines regarding both accuracy and efficiency in three diverse learning tasks: small molecule properties, RNA 3D structures, and protein-ligand binding affinities. Our results highlight the potential for PAMNet in a broad range of molecular science applications.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
7.8
作者：
HORNIK, K;STINCHCOMBE, M;WHITE, H
通讯作者：
WHITE, H
影响因子：
8.6
作者：
Chen, Chi;Ye, Weike;Ong, Shyue Ping
通讯作者：
Ong, Shyue Ping
影响因子：
5.8
作者：
Capriotti, Emidio;Norambuena, Tomas;Melo, Francisco
通讯作者：
Melo, Francisco
影响因子：
4.5
作者：
Miao, Zhichao;Adamiak, Ryszard W.;Westhof, Eric
通讯作者：
Westhof, Eric