Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions

By Y. Li, Jianyi Yang. 2017

86 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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. David T. Jones
2. Shaun M. Kandathil
3. Joe G. Greener
4. Lewis Moffat

In Molecular Biology

1. Joe G. Greener
2. Shaun M. Kandathil
3. David T. Jones
4. Lewis Moffat
5. Yang Zhang
6. Badri Adhikari
7. Robin Pearce
8. Jianlin Cheng

Citations in the corpus

• 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).”
• A guide to machine learning for biologists (2021, background): “Similar issues affect studies predicting protein–ligand binding affinity [94].”