5 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…
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 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…