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Shaoning Li

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2 papers

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NeurIPS Conference 2024 Conference Paper

Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs

Yusong Wang
Chaoran Cheng
Shaoning Li
Yuxuan Ren
Bin Shao
Ge Liu
Pheng-Ann Heng
Nanning Zheng

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems. To address this challenge, we introduce **Neural P$^3$M**, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P$^3$M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https: //github. com/OnlyLoveKFC/Neural_P3M.

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NeurIPS Conference 2023 Conference Paper

Geometric Transformer with Interatomic Positional Encoding

Yusong Wang
Shaoning Li
Tong Wang
Bin Shao
Nanning Zheng
Tie-Yan Liu

The widespread adoption of Transformer architectures in various data modalities has opened new avenues for the applications in molecular modeling. Nevertheless, it remains elusive that whether the Transformer-based architecture can do molecular modeling as good as equivariant GNNs. In this paper, by designing Interatomic Positional Encoding (IPE) thatparameterizes atomic environments as Transformer's positional encodings, we propose Geoformer, a novel geometric Transformer to effectively model molecular structures for various molecular property prediction. We evaluate Geoformer on several benchmarks, including the QM9 dataset and the recently proposed Molecule3D dataset. Compared with both Transformers and equivariant GNN models, Geoformer outperforms the state-of-the-art (SoTA) algorithms on QM9, and achieves the best performance on Molecule3D for both random and scaffold splits. By introducing IPE, Geoformer paves the way for molecular geometric modeling based on Transformer architecture. Codes are available at https: //github. com/microsoft/AI2BMD/tree/Geoformer.

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