Deep Multi-attribute Graph Representation Learning on Protein Structures. - Tian Xia 006; Wei-Shinn Ku (CoRR, 2020)

Geometric Graph Representation Learning on Protein Structure Prediction. - Tian Xia 006; Wei-Shinn Ku (KDD, 2021)

APIP: Attention-based Protein Representation Learning for Protein-Ligand Interface Prediction. - Tian Xia 006; Bo Hui 001; Wei-Shinn Ku (IEEE Big Data, 2022)

A Localized Geometric Method to Match Knowledge in Low-dimensional Hyperbolic Space. - Bo Hui 001; Tian Xia 006; Wei-Shinn Ku (EMNLP, 2022)

GBPNet: Universal Geometric Representation Learning on Protein Structures. - Sarp Aykent; Tian Xia 006 (KDD, 2022)

Constrained Market Share Maximization by Signal-Guided Optimization. - Bo Hui 001; Yuchen Fang 001; Tian Xia 006; Sarp Aykent; Wei-Shinn Ku (AAAI, 2023)

SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning. - Sarp Aykent; Tian Xia 006 (NeurIPS, 2023)

Extrinsic-Intrinsic Representation Learning Framework for Drug Discovery. - Tian Xia 006; Sarp Aykent; Wei-Shinn Ku (SDM, 2023)

SO(3) Equivariant Framework for Spatial Networks. - Tian Xia 006; Sarp Aykent (IEEE Big Data, 2024)

SE(3) Equivariant Neural Network for 3D Graphs. - Sarp Aykent; Tian Xia 006 (IEEE Big Data, 2024)

GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks. - Sarp Aykent; Tian Xia 006 (ICLR, 2025)