The PDBbind database: methodologies and updates.

By Renxiao Wang, Xueliang Fang, Yipin Lu, Chao-Yie Yang, Shaomeng Wang. 2005

893 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Pedro J. Ballester
2. John B. O. Mitchell
3. Stefano Forli
4. F. Silva-Jr
5. Paula Jofily
6. Pedro H. M. Torres
7. A. C. Sodero
8. Zhihai Liu

In Materials Chemistry

1. Pedro J. Ballester
2. John B. O. Mitchell
3. Jinbo Xu
4. Gerhard Klebe
5. Stefano Forli
6. F. Silva-Jr
7. Paula Jofily
8. Pedro H. M. Torres

In Molecular Biology

1. Pedro J. Ballester
2. John B. O. Mitchell
3. Charlotte M. Deane
4. Casey S. Greene
5. Yifan Peng
6. Brett K. Beaulieu-Jones
7. Jinbo Xu
8. F. Silva-Jr

In Computational Chemistry & Drug Discovery

1. Pedro J. Ballester
2. John B. O. Mitchell
3. F. Silva-Jr
4. Paula Jofily
5. Pedro H. M. Torres
6. A. C. Sodero
7. Gerhard Klebe
8. Yan Li

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Gomes et al. demonstrate that currently random forest on a 1D feature vector that describes the 3D ligand-target structure generally outperforms neural networks on the same feature vector as well as atomic convolutions and ligand-based neural networks when predicting the continuous-valued inhibition”
• ChEMBL: a large-scale bioactivity database for drug discovery (2011, background): “PDBBind (9), Binding MOAD (10) and AffinDB (11) contain binding affinity information for protein–ligand complexes found in the Protein Data Bank (PDB, 12).”
• BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities (2006, background): “Thus, BindingDB’s data collection differs significantly from those of databases which only collect affinities for protein–ligand complexes in the PDB, notably BindingMOAD (10) which holds 1400 data, PDBBind (11,12) with 1600 data, and AffinDB (13) with 750 data.”
• Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “With the recent advances in predicting structures of proteins [4] and the increasing availability of 3D-structure protein-ligand data [41], there is another hot research area of studying structure-based approaches, which focus on learning from 3D-structure proteinligand complexes to predict binding ”
• Virtual screening strategies in drug discovery: a critical review. (2013, neutral): “Even in annotated ligand databases such as ChEMBLdb, [45] DrugBank [43, 44], BindingDB [145-147], PDBBind [148], MOAD [149, 150], WOMBAT [46], and GLIDA [151] protein–ligand interaction matrices are incomplete [142].”
• SAbDab: the structural antibody database (2013, methodology): “Antibody binding affinity data are primarily obtained from two databases, PDB-Bind (21) and the structure-based benchmark (22).”