Jason C. Cole

Molecular Biology · 1999–2022

A researcher in the Science corpus: 9 papers; trusts 11 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 97 citations into it; #7,932 of 17,773 by trust here, #4,020 by citations
• Computational Theory and Mathematics — 92 citations into it; #6,873 of 12,182 by trust here, #396 by citations
• Computational Chemistry & Drug Discovery — 78 citations into it; #250 of 425 by trust here, #172 by citations
• Materials Chemistry — 42 citations into it
• Organic Chemistry — 27 citations into it
• Natural Language Processing — 8 citations into it

Whom Jason C. Cole trusts overall

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

1. Paul D. Lyne
2. Marcel L. Verdonk
3. Renxiao Wang
4. Holger Gohlke
5. Christopher W. Murray
6. J. Willem M. Nissink
7. Yipin Lu
8. Richard A. Friesner
9. Peter Willett
10. Gerhard Klebe

Who trusts Jason C. Cole overall

1. Gerhard Klebe
2. Sérgio Filipe Sousa
3. Elizabeth Yuriev
4. Colin R. Groom
5. Aurélien Grosdidier
6. Shengyou Huang
7. Clare F. Macrae
8. Gregory A. Ross
9. Wijnand T. M. Mooij
10. Marcel L. Verdonk

Papers from Jason C. Cole's perspective

Ordered by how strongly the researchers Jason C. Cole 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 new test set for validating predictions of protein–ligand interaction (Proteins: Structure, Function, and Bioinformatics)
4. EUDOC: a computer program for identification of drug interaction sites in macromolecules and drug leads from chemical databases
5. Evaluation of docking performance: comparative data on docking algorithms. (Journal of Medicinal Chemistry)
6. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes
7. Knowledge-based scoring function to predict protein-ligand interactions.
8. A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities.
9. Comparative evaluation of 11 scoring functions for molecular docking.
10. Assessing scoring functions for protein-ligand interactions. (Journal of Medicinal Chemistry)

Their papers

• Retrieval of Crystallographically-Derived Molecular Geometry Information (2004) — 848 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
• New software for searching the Cambridge Structural Database and visualizing crystal structures. (2002) — 2,432 citations
• A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios (2022) — 24 citations
• Use of molecular docking computational tools in drug discovery. (2021) — 418 citations
• Comparing protein–ligand docking programs is difficult (2005) — 335 citations
• SuperStar: a knowledge-based approach for identifying interaction sites in proteins. (1999)