`OMICS' techniques have deeply changed the drug discovery process. The availability of many different potential druggable genes, generated by these new techniques, have exploited the complexity of new lead compounds screening. `Virtual screening', based on the integration of different analytical tools on high performance hardware platforms, has speeded up the search for new chemical entities suitable for experimental validation. Docking is a key step in the screening process. The aim of this paper is the evaluation of binding differences due to solvation. We have compared two commonly used software, one of which takes into account solvation, on a set of small molecules (Morpholines, flavonoids and imidazoles) which are able to target the RAC1 protein - a cardiovascular target. We have evaluated the degree of agreement between the two different programs using a machine learning approach combined with statistical test. Our analysis, on a sample of small molecules, has pointed out that 35% of the molecules seem to be sensitive to solvation. This result, even though quite preliminary, stresses the need to combine different algorithms to obtain a more reliable filtered set of ligands.
Drug design for cardiovascular disease: The effect of solvation energy on Rac1-ligand interactions
Arrigo P;
2011
Abstract
`OMICS' techniques have deeply changed the drug discovery process. The availability of many different potential druggable genes, generated by these new techniques, have exploited the complexity of new lead compounds screening. `Virtual screening', based on the integration of different analytical tools on high performance hardware platforms, has speeded up the search for new chemical entities suitable for experimental validation. Docking is a key step in the screening process. The aim of this paper is the evaluation of binding differences due to solvation. We have compared two commonly used software, one of which takes into account solvation, on a set of small molecules (Morpholines, flavonoids and imidazoles) which are able to target the RAC1 protein - a cardiovascular target. We have evaluated the degree of agreement between the two different programs using a machine learning approach combined with statistical test. Our analysis, on a sample of small molecules, has pointed out that 35% of the molecules seem to be sensitive to solvation. This result, even though quite preliminary, stresses the need to combine different algorithms to obtain a more reliable filtered set of ligands.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
