322 citations · 484 across the 7 of their papers we have counts for
7 papers
Decomposed Direct Preference Optimization for Structure-Based Drug Design
Xiwei Cheng, Xiangxin Zhou, Yuwei Yang +2
Diffusion models have achieved promising results for Structure-Based Drug Design (SBDD). Nevertheless, high-quality protein subpocket and ligand data are relatively scarce, which h…
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
Xiangxin Zhou, Xiwei Cheng, Yuwei Yang +3
Recently, 3D generative models have shown promising performances in structure-based drug design by learning to generate ligands given target binding sites. However, only modeling t…
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang +6
Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignor…
Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Zhilin Huang, Ling Yang, Zaixi Zhang +6
Structure-based drug design (SBDD) aims to generate 3D ligand molecules that bind to specific protein targets. Existing 3D deep generative models including diffusion models have sh…
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking
Yuchi Liu, Zhongdao Wang, Xiangxin Zhou +1
Association, aiming to link bounding boxes of the same identity in a video sequence, is a central component in multi-object tracking (MOT). To train association modules, e.g., para…
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Xiaohan Ding, Guiguang Ding, Xiangxin Zhou +3
Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning i…
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou +2
This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convoluti…