General and targeted statistical potentials for protein–ligand interactions

By Wijnand T. M. Mooij, Marcel L. Verdonk. Proteins: Structure, Function, and Bioinformatics, 2005

335 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/c87055be089163ffb98f22a9b82523df75a9b94e

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. Stefano Moro
2. Veronica Salmaso
3. Luca Pinzi
4. Giulio Rastelli

In Molecular Biology

1. Stefano Moro
2. Veronica Salmaso
3. Luca Pinzi
4. Giulio Rastelli

In Physical and Theoretical Chemistry

1. Stefano Moro
2. Veronica Salmaso

Citations in the corpus

• Advances and Challenges in Protein-Ligand Docking (2010, neutral): “Examples include DrugScore [138,139], SMoG [140,141], BLEEP [142,143], GOLD/ASP [144], MScore [145], and KScore[146].”
• A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking (2010, neutral): “Three scoring functions in the GOLD software version 3.2 (Jones et al., 1995, 1997): GoldScore, ChemScore (Baxter, 1998; Eldridge, 1997) and ASP (Mooij and Verdonk, 2005).”
• Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview (2018, background): “DrugScore (Gohlke et al., 2000; Velec et al., 2005) and GOLD/ASP (Mooij and Verdonk, 2005) are examples of knowledge-based scoring functions.”