26 citations · 37 across the 4 of their papers we have counts for
5 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…
Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling
Yuwei Yang, Siqi Ouyang, Xueyu Hu +3
Structure-based drug design aims at generating high affinity ligands with prior knowledge of 3D target structures. Existing methods either use conditional generative model to learn…
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…