57 citations · 215 across the 16 of their papers we have counts for
16 papers
Geometry Informed Tokenization of Molecules for Language Model Generation
Xiner Li, Limei Wang, Youzhi Luo +5
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Towards Symmetry-Aware Generation of Periodic Materials
Youzhi Luo, Chengkai Liu, Shuiwang Ji
We consider the problem of generating periodic materials with deep models. While symmetry-aware molecule generation has been studied extensively, periodic materials possess differe…
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo +3
We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such re…
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Haiyang Yu, Meng Liu, Youzhi Luo +4
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as d…
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
Xiner Li, Shurui Gui, Youzhi Luo +1
Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution. With rising application demands and i…
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
Shurui Gui, Meng Liu, Xiner Li +2
We tackle the problem of graph out-of-distribution (OOD) generalization. Existing graph OOD algorithms either rely on restricted assumptions or fail to exploit environment informat…
3D Molecular Geometry Analysis with 2D Graphs
Zhao Xu, Yaochen Xie, Youzhi Luo +7
Ground-state 3D geometries of molecules are essential for many molecular analysis tasks. Modern quantum mechanical methods can compute accurate 3D geometries but are computationall…
Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino +2
A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and ef…
Automated Data Augmentations for Graph Classification
Youzhi Luo, Michael McThrow, Wing Yee Au +4
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations tha…
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Zhao Xu, Youzhi Luo, Xuan Zhang +7
Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies…
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
Meng Liu, Cong Fu, Xuan Zhang +7
Molecular property prediction is gaining increasing attention due to its diverse applications. One task of particular interests and importance is to predict quantum chemical proper…
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
Qi Qi, Youzhi Luo, Zhao Xu +2
Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common metrics for evaluating classification performance for imbalanced problems. Compared with AUROC, AUPRC is a mo…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, Shuiwang Ji
We consider the problem of molecular graph generation using deep models. While graphs are discrete, most existing methods use continuous latent variables, resulting in inaccurate m…
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Zhengyang Wang, Meng Liu, Youzhi Luo +8
Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predict…