Name
Abstract | ||
|---|---|---|
Similarity of compound chemical structures often leads to close pharmacological profiles, including binding to the same protein
targets. The opposite, however, is not always true, as distinct chemical scaffolds can exhibit similar pharmacology as well.
Therefore, relying on chemical similarity to known binders in search for novel chemicals targeting the same protein artificially
narrows down the results and makes lead hopping impossible. In this study we attempt to design a compound similarity/distance
measure that better captures structural aspects of their pharmacology and molecular interactions. The measure is based on
our recently published method for compound spatial alignment with atomic property fields as a generalized 3D pharmacophoric
potential. We optimized contributions of different atomic properties for better discrimination of compound pairs with the
same pharmacology from those with different pharmacology using Partial Least Squares regression. Our proposed similarity measure
was then tested for its ability to discriminate pharmacologically similar pairs from decoys on a large diverse dataset of
115 protein–ligand complexes. Compared to 2D Tanimoto and Shape Tanimoto approaches, our new approach led to improvement in
the area under the receiver operating characteristic curve values in 66 and 58% of domains respectively. The improvement was
particularly high for the previously problematic cases (weak performance of the 2D Tanimoto and Shape Tanimoto measures) with
original AUC values below 0.8. In fact for these cases we obtained improvement in 86% of domains compare to 2D Tanimoto measure
and 85% compare to Shape Tanimoto measure. The proposed spatial chemical distance measure can be used in virtual ligand screening. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10822-009-9316-x | Journal of computer-aided molecular design |
Keywords | Field | DocType |
Chemical distance,Chemical similarity,Quantitative structure-property relationship,Internal coordinates mechanics (ICM),Atomic property fields (APF),Spatial alignment | Quantitative structure–activity relationship,Molecular interactions,Similarity measure,Molecule,Partial least squares regression,Computational chemistry,Chemistry,Molecular dynamics,Bioinformatics,Chemical similarity,Always true | Journal |
Volume | Issue | ISSN |
24 | 3 | 1573-4951 |
Citations | PageRank | References |
3 | 0.40 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. V. Grigoryan | 1 | 3 | 0.40 |
| Irina Kufareva | 2 | 61 | 3.96 |
| Maxim Totrov | 3 | 269 | 31.59 |
| Ruben Abagyan | 4 | 430 | 55.44 |