Marcel L. Verdonk

Computational Chemistry & Drug Discovery · 1999–2007

A researcher in the Science corpus: 7 papers; trusts 11 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computational Chemistry & Drug Discovery — 54 citations into it; #277 of 425 by trust here, #172 by citations
• Computational Theory and Mathematics — 39 citations into it; #3,584 of 12,182 by trust here, #253 by citations
• Molecular Biology — 38 citations into it; #4,433 of 17,773 by trust here, #2,661 by citations
• Materials Chemistry — 18 citations into it; #9,896 of 11,513 by trust here, #6,817 by citations
• Natural Language Processing — 7 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it; #1,570 of 3,265 by trust here, #524 by citations

Whom Marcel L. Verdonk trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Holger Gohlke
2. Christopher W. Murray
3. J. Willem M. Nissink
4. Manfred Hendlich
5. Gerhard Klebe
6. Jason C. Cole
7. Matthias Rarey
8. Hans-Joachim Böhm
9. Paul D. Lyne
10. Peter Willett

Who trusts Marcel L. Verdonk overall

1. Gerhard Klebe
2. Elizabeth Yuriev
3. Sérgio Filipe Sousa
4. Gregory A. Ross
5. Wijnand T. M. Mooij
6. Pedro H. M. Torres
7. Marta M. Stepniewska-Dziubinska
8. Jason C. Cole
9. Tatu Pantsar
10. Garrett M. Morris

Papers from Marcel L. Verdonk's perspective

Ordered by how strongly the researchers Marcel L. Verdonk trusts endorse each paper. Same corpus, a different order for every researcher.

1. A general and fast scoring function for protein-ligand interactions: a simplified potential approach.
2. A new test set for validating predictions of protein–ligand interaction (Proteins: Structure, Function, and Bioinformatics)
3. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
4. Consensus scoring for ligand/protein interactions. (Journal of Molecular Graphics and Modelling)
5. EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases
6. Knowledge-based scoring function to predict protein-ligand interactions.
7. Improved protein–ligand docking using GOLD
8. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
9. A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities.
10. Molecular properties that influence the oral bioavailability of drug candidates. (Journal of Medicinal Chemistry)

Their papers

• Diverse, high-quality test set for the validation of protein-ligand docking performance. (2007) — 619 citations
• Improved protein–ligand docking using GOLD (2003) — 2,864 citations
• A new test set for validating predictions of protein–ligand interaction (2002) — 415 citations
• Modeling water molecules in protein-ligand docking using GOLD. (2005) — 371 citations
• Virtual Screening Using Protein-Ligand Docking: Avoiding Artificial Enrichment (2004) — 311 citations
• General and targeted statistical potentials for protein–ligand interactions (2005) — 335 citations
• SuperStar: a knowledge-based approach for identifying interaction sites in proteins. (1999)