2 citations · 5 across the 4 of their papers we have counts for
5 papers
Molexar: A Unified Multimodal Molecular Foundation Model for Drug Design
Haoyu Lin, Yiyan Liao, Jinmei Pan +3
Molecular generation is a central challenge in drug discovery, requiring models that explore vast chemical space while satisfying diverse design constraints. We present Molexar, a…
SynCraft: Guiding Large Language Models to Predict Edit Sequences for Molecular Synthesizability Optimization
Junren Li, Luhua Lai
Generative artificial intelligence has revolutionized the exploration of chemical space, yet a critical bottleneck remains that a substantial fraction of generated molecules is syn…
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
Haoyu Lin, Shiwei Wang, Jintao Zhu +3
Protein (receptor)--ligand interaction prediction is a critical component in computer-aided drug design, significantly influencing molecular docking and virtual screening processes…
Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models
Weixin Xie, Jianhang Zhang, Qin Xie +4
Deep generative models have gained significant advancements to accelerate drug discovery by generating bioactive chemicals against desired targets. Nevertheless, most generated com…
DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking
Jintao Zhu, Zhonghui Gu, Jianfeng Pei +1
Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurat…