GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation
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Keywords: molecular conformation prediction, molecule modeling, graph neural network, graph transformer
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TL;DR: We propose a Graph-Transformer network which uses a novel self-attention mechanism we developed for molecular structure modeling, achieving SOTA performance in predicting a molecule’s ground-state 3D conformation from its 2D topology graph.
Abstract: The ground-state conformation of a molecule is often decisive for its properties. However, experimental or computational methods, such as density functional theory (DFT), are time-consuming and labor-intensive for obtaining this conformation. Deep learning (DL) based molecular representation learning (MRL) has made significant advancements in molecular modeling and has achieved remarkable results in various tasks. Consequently, it has emerged as a promising approach for directly predicting the ground-state conformation of molecules. In this regard, we introduce GTMGC, a novel network based on Graph-Transformer (GT) that seamlessly predicts the spatial configuration of molecules in a 3D space from their 2D topological architecture in an end-to-end manner. Moreover, we propose a novel self-attention mechanism called Molecule Structural Residual Self-Attention (MSRSA) for molecular structure modeling. This mechanism not only guarantees high model performance and easy implementation but also lends itself well to other molecular modeling tasks. Our method has been evaluated on the Molecule3D benchmark dataset and the QM9 dataset. Experimental results demonstrate that our approach achieves remarkable performance and outperforms current state-of-the-art methods as well as the widely used open-source software RDkit.
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Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Submission Number: 5387
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