Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.

Out-of-Distribution Generalization Transfer Learning +1

A Latent Diffusion Model for Protein Structure Generation

1 code implementation • 6 May 2023 • Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji

Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.

Drug Discovery

A new perspective on building efficient and expressive 3D equivariant graph neural networks

1 code implementation • NeurIPS 2023 • Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, Zhi-Ming Ma

Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.

Molecular Property Prediction Property Prediction

Learning Hierarchical Protein Representations via Complete 3D Graph Networks

1 code implementation • 26 Jul 2022 • Limei Wang, Haoran Liu, Yi Liu, Jerry Kurtin, Shuiwang Ji

In this work, we propose to develop a novel hierarchical graph network, known as ProNet, to capture the relations.

Representation Learning

ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs

1 code implementation • 17 Jun 2022 • Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji

To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood.

Drug Discovery

GOOD: A Graph Out-of-Distribution Benchmark

1 code implementation • 16 Jun 2022 • Shurui Gui, Xiner Li, Limei Wang, Shuiwang Ji

Our GOOD benchmark is a growing project and expects to expand in both quantity and variety of resources as the area develops.

GraphFM: Improving Large-Scale GNN Training via Feature Momentum

1 code implementation • 14 Jun 2022 • Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji

GraphFM-IB applies FM to in-batch sampled data, while GraphFM-OB applies FM to out-of-batch data that are 1-hop neighborhood of in-batch data.

Node Classification

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

1 code implementation • NeurIPS Workshop AI4Scien 2021 • Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji

We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.

Molecular Property Prediction Property Prediction

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

1 code implementation • 23 Mar 2021 • Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.

Benchmarking Graph Generation +1

Spherical Message Passing for 3D Graph Networks

1 code implementation • ICLR 2022 • Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, Shuiwang Ji

Based on such observations, we propose the spherical message passing (SMP) as a novel and powerful scheme for 3D molecular learning.

Drug Discovery Representation Learning

Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery

1 code implementation • 2 Dec 2020 • Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji

Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.

BIG-bench Machine Learning Drug Discovery +2