3 citations · 4 across the 9 of their papers we have counts for
9 papers
InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames
Haorui Li, Weitao Du, Yuqiang Li +2
Transformer-based autoregressive models have emerged as a unifying paradigm across modalities such as text and images, but their extension to 3D molecule generation remains underex…
Flow Along the K-Amplitude for Generative Modeling
Weitao Du, Shuning Chang, Jiasheng Tang +3
In this work, we propose a novel generative learning paradigm, K-Flow, an algorithm that flows along the -amplitude. Here, is a scaling parameter that organizes frequency ba…
GDiffRetro: Retrosynthesis Prediction with Dual Graph Enhanced Molecular Representation and Diffusion Generation
Shengyin Sun, Wenhao Yu, Yuxiang Ren +5
Retrosynthesis prediction focuses on identifying reactants capable of synthesizing a target product. Typically, the retrosynthesis prediction involves two phases: Reaction Center I…
Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design
Shengchao Liu, Divin Yan, Weitao Du +6
Artificial intelligence models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overl…
Sculpting Molecules in Text-3D Space: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
Kaiwei Zhang, Yange Lin, Guangcheng Wu +5
The integration of deep learning, particularly AI-Generated Content, with high-quality data derived from ab initio calculations has emerged as a promising avenue for transforming t…
A quatum inspired neural network for geometric modeling
Weitao Du, Shengchao Liu, Xuecang Zhang
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance. In pa…
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
Shengchao Liu, Weitao Du, Hannan Xu +8
In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities, estimating transport properties, and ex…
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
Weitao Du, Jiujiu Chen, Xuecang Zhang +2
Recently, artificial intelligence for drug discovery has raised increasing interest in both machine learning and chemistry domains. The fundamental building block for drug discover…
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
Tianyu Zhang, Yuxiang Ren, Wenzheng Feng +2
Unsupervised graph representation learning is a non-trivial topic. The success of contrastive methods in the unsupervised representation learning on structured data inspires simila…