Search Results for author:
Found 18 papers, 11 papers with code
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties such as drug-like properties (e. g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites.
Geometry Informed Tokenization of Molecules for Language Model Generation
We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Towards Symmetry-Aware Generation of Periodic Materials
In addition, SyMat employs a score-based diffusion model to generate atom coordinates of materials, in which a novel symmetry-aware probabilistic model is used in the coordinate diffusion process.
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization
Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where test distribution shifts from training distribution.
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.
Ranked #2 on
Formation Energy
on JARVIS-DFT
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
In this work, we propose to simultaneously incorporate label and environment causal independence (LECI) to fully make use of label and environment information, thereby addressing the challenges faced by prior methods on identifying causal and invariant subgraphs.
3D Molecular Geometry Analysis with 2D Graphs
Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs.
Generating 3D Molecules for Target Protein Binding
Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system.
Automated Data Augmentations for Graph Classification
In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on
3D Geometry Prediction
on Molecule3D val
An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch
We consider the problem of generating 3D molecular geometries from scratch.
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
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.
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
Compared with AUROC, AUPRC is a more appropriate metric for highly imbalanced datasets.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
GraphDF: A Discrete Flow Model for Molecular Graph Generation
We consider the problem of molecular graph generation using deep models.
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
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.
