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APObind: A Dataset of Ligand Unbound Protein Conformations for Machine
  Learning Applications in De Novo Drug Design

APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design

23 August 2021
Rishal Aggarwal
Akash Gupta
U. Priyakumar
ArXivPDFHTML

Papers citing "APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design"

3 / 3 papers shown
Title
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion
  Bridge
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Yufei Huang
Odin Zhang
Lirong Wu
Cheng Tan
Haitao Lin
Zhangyang Gao
Siyuan Li
Stan. Z. Li
DiffM
38
8
0
18 Feb 2024
Pre-Training on Large-Scale Generated Docking Conformations with
  HelixDock to Unlock the Potential of Protein-ligand Structure Prediction
  Models
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
Lihang Liu
Shanzhuo Zhang
Donglong He
Xianbin Ye
Jingbo Zhou
...
Fan Wang
Jingzhou He
Liang Zheng
Yonghui Li
Xiaomin Fang
AI4CE
35
9
0
21 Oct 2023
A Systematic Survey in Geometric Deep Learning for Structure-based Drug
  Design
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design
Zaixin Zhang
Jiaxian Yan
Qi Liu
Enhong Chen
Marinka Zitnik
55
1
0
20 Jun 2023
1