Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.

By Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, Renxiao Wang. 2017

502 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology, 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. Tommi S. Jaakkola
2. Regina Barzilay
3. Bowen Jing
4. Gabriele Corso
5. Jian Wang
6. Octavian-Eugen Ganea
7. Andreas Krause
8. Charlotte Bunne

In Molecular Biology

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Hannes Stärk
4. Gabriele Corso
5. Bowen Jing
6. Jian Wang
7. Andreas Krause
8. Charlotte Bunne

In Pharmacology

1. Regina Barzilay
2. N. V. Dokholyan
3. Tommi S. Jaakkola

In Computational Chemistry & Drug Discovery

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Gabriele Corso
4. Bowen Jing
5. Hannes Stärk
6. Vignesh Ram Somnath
7. Marta M. Stepniewska-Dziubinska
8. Andreas Krause

Citations in the corpus

• DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, methodology): “We use the molecular complexes in PDBBind [12] that were extracted from the Protein Data Bank (PDB) [13].”
• EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “Due to the differences in resolution and the average ligand size (32 heavy atoms in PDBBind versus 24 in the core set), the complexes of the core set can be considered easier to predict than the average complex.”
• Multi-Scale Representation Learning on Proteins (2022, methodology): “The PDB BIND database (version 2019) [Liu et al., 2017] is a collection of the experimentally measured binding affinity data for all types of biomolecular complexes deposited in the Protein Data Bank [Berman et al., 2000].”
• Development and evaluation of a deep learning model for protein–ligand binding affinity prediction (2017, methodology): “The network was implemented with TensorFlow (Abadi et al. , 2015) using Python API and trained on the PDBbind database (Liu et al. , 2017).”
• Protein-Ligand Interaction Graphs: Learning from Ligand-Shaped 3D Interaction Graphs to Improve Binding Affinity Prediction (2022, methodology): “For training and testing, we used the PDBbind database ([21, 22]), a curated set of 3D-structures of protein-ligand complexes and their corresponding experimentally determined binding affinity, obtained from the Protein Data Bank (PDB, [23]).”
• Structure-based drug design with equivariant diffusion models (2022, unknown): “This hypothesis is supported by less favorable Vina scores of reference molecules from the Liu et al. 17 used autoregressive models to generate atoms sequentially and incorporate angles during the generation process.”
• Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023, methodology): “The test set is collected from PDBBind [23].”
• COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking (2018, unknown): “The third is the development of scoring functions for the estimation of the binding affinity (14,15).”
• MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints (2019, methodology): “The generation of the dataset is based on the refined set of the PDBbind 2017 database 26,27 , which provides a comprehensive collection of experimentally measured binding affinity data for the biomolecule complexes in the Protein Data Bank 28 (PDB).”