Title |
Searching for bioactive conformations of drug-like ligands with current force fields: how good are we?
|
---|---|
Published in |
Journal of Cheminformatics, May 2017
|
DOI | 10.1186/s13321-017-0216-0 |
Pubmed ID | |
Authors |
Oya Gürsoy, Martin Smieško |
Abstract |
Drug-like ligands obtained from protein-ligand complexes deposited in the Protein Databank were subjected to conformational searching using various force fields and solvation settings. For each ligand, the resulting conformer pool was examined for the presence of the bioactive (crystal pose-like) conformation. Similarity of conformers toward the crystal-pose was quantified as the best achievable root mean squared deviation (RMSD, heavy atoms only). Analyzing the conformer pools generated by various force fields revealed only small differences in the likelihood of finding a crystal pose-like conformation. However, employing different solvents in the conformational search was found to be very important for achieving RMSDs below 1.0 Å. The best statistical values of likelihood were observed with a recently released force field covering a large portion of dihedral angles occurring in drug-like compounds in combination with the water as solvent. In order to enable computational chemists and modelers to efficiently use available software tools, we have additionally performed several focused analyses on ligands, grouped according to descriptors most relevant for the rational drug design. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 31% |
Turkey | 1 | 8% |
Chile | 1 | 8% |
Comoros | 1 | 8% |
Israel | 1 | 8% |
India | 1 | 8% |
Unknown | 4 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 6 | 46% |
Members of the public | 6 | 46% |
Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 76 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 19 | 25% |
Researcher | 14 | 18% |
Student > Master | 7 | 9% |
Student > Bachelor | 4 | 5% |
Professor > Associate Professor | 4 | 5% |
Other | 11 | 14% |
Unknown | 17 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 33 | 43% |
Agricultural and Biological Sciences | 9 | 12% |
Biochemistry, Genetics and Molecular Biology | 3 | 4% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 4% |
Chemical Engineering | 2 | 3% |
Other | 4 | 5% |
Unknown | 22 | 29% |