Christopher W. Murray

Molecular Biology · 1997–2013

A researcher in the Science corpus: 13 papers; trusts 6 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 72 citations into it; #2,649 of 17,773 by trust here, #1,833 by citations
• Computational Theory and Mathematics — 68 citations into it; #2,182 of 12,182 by trust here, #160 by citations
• Computational Chemistry & Drug Discovery — 60 citations into it; #55 of 425 by trust here, #45 by citations
• Organic Chemistry — 36 citations into it; #1,272 of 1,495 by trust here, #851 by citations

Whom Christopher W. Murray trusts overall

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

1. Paul D. Lyne
2. Philip J. Hajduk
3. J. Wells
4. A. Hopkins
5. Holger Gohlke
6. Renxiao Wang
7. Marcel L. Verdonk

Who trusts Christopher W. Murray overall

1. Marcel L. Verdonk
2. Shengyou Huang
3. Sérgio Filipe Sousa
4. Gerhard Klebe
5. L. Ferreira
6. Holger Gohlke
7. Volker Schnecke
8. Wijnand T. M. Mooij
9. Pedro H. M. Torres
10. Douglas B. Kitchen

Papers from Christopher W. Murray's perspective

Ordered by how strongly the researchers Christopher W. Murray trusts endorse each paper. Same corpus, a different order for every researcher.

1. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases
2. Consensus scoring for ligand/protein interactions. (Journal of Molecular Graphics and Modelling)
3. A general and fast scoring function for protein-ligand interactions: a simplified potential approach.
4. Ligand efficiency: a useful metric for lead selection. (Drug Discovery Today)
5. Is There a Difference between Leads and Drugs? A Historical Perspective (Journal of chemical information and computer sciences)
6. A new test set for validating predictions of protein–ligand interaction (Proteins: Structure, Function, and Bioinformatics)
7. EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases
8. Evaluation of docking performance: comparative data on docking algorithms. (Journal of Medicinal Chemistry)
9. Improved protein–ligand docking using GOLD
10. Knowledge-based scoring function to predict protein-ligand interactions.

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
• A 'rule of three' for fragment-based lead discovery? (2003) — 1,177 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
• Fragment-based lead discovery (2004) — 777 citations
• The 'rule of three' for fragment-based drug discovery: where are we now? (2013) — 271 citations
• The rise of fragment-based drug discovery. (2009) — 709 citations
• Comparing protein–ligand docking programs is difficult (2005) — 335 citations
• Flexible docking using tabu search and an empirical estimate of binding affinity (1998)
• Empirical scoring functions. II. The testing of an empirical scoring function for the prediction of ligand-receptor binding affinities and the use of Bayesian regression to improve the quality of the model (1998)
• Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes (1997)