Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025
Geometry Informed Tokenization of Molecules for Language Model Generation.
CoRR, 2024
3D Molecular Geometry Analysis with 2D Graphs.
Proceedings of the 2024 SIAM International Conference on Data Mining, 2024
Graph Structure Extrapolation for Out-of-Distribution Generalization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization.
CoRR, 2023
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Towards Symmetry-Aware Generation of Periodic Materials.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction.
Proceedings of the International Conference on Machine Learning, 2023
Automated Data Augmentations for Graph Classification.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Learning Fair Graph Representations via Automated Data Augmentations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Advanced graph and sequence neural networks for molecular property prediction and drug discovery.
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Bioinform., 2022
Frontiers of Graph Neural Networks with DIG.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Generating 3D Molecules for Target Protein Binding.
Proceedings of the International Conference on Machine Learning, 2022
An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch.
Proceedings of the Tenth International Conference on Learning Representations, 2022
DIG: A Turnkey Library for Diving into Graph Deep Learning Research.
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J. Mach. Learn. Res., 2021
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs.
CoRR, 2021
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks.
CoRR, 2021
Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence.
CoRR, 2021
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
GraphDF: A Discrete Flow Model for Molecular Graph Generation.
Proceedings of the 38th International Conference on Machine Learning, 2021
MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery.
CoRR, 2020