Today, pharmacophore has been considered as a critical aspect of design, discovery, and optimization of drug candidates. Also, understanding a pharmacophore is the first and the most important step towards perceiving the interaction between a receptor and a ligand. A central premise of medicinal chemistry illustrates that structurally similar molecules exhibit similar activities on biological properties. With expertise in ligand-based screening services, Creative Biolabs offers advanced pharmacophore similarity search technologies to promote challenging drug projects. Our pharmacophore modeling incorporates geometric and chemical characteristics of targeted binding sites to rationally identify and design novel drug leads.
Pharmacophore modeling makes use of structure-activity relationships of ligands to propose the three-dimensional (3D) properties that are present in a molecule in order for it to possess a certain biological activity of interest. Strategies to identify pharmacophores range from those that purpose bioactive conformations of a suite of molecules given proposed corresponding points in the molecules, to those that purpose both the matching points and relevant conformations of the molecules, to those that also think of the potency of the molecules in the analysis.
A pharmacophore hypothesis can be exploited to design new compounds or search a database for molecules with the required features. Commonly, we apply molecular similarity methods to select ideal candidates in the drug discovery industry because structurally similar molecules are likely to have similar biological properties. These ways are involved in applications such as similarity searching, molecular screening, or molecular clustering.
Pharmacophore models have already proved to be useful for the selection of focused sets of compounds. Two kinds of pharmacophores are present, including ligand-based pharmacophores derived from structures of known active compounds and structure-based pharmacophores directly derived from X-ray results of protein-ligand complexes. There are still a lot of targets that aren’t available for structure-based modeling, thus ligand-based modeling methods remain attractive for scientists.
For ligand-based modeling tools, almost all of them are expensive and commercial. These programs utilize different algorithms for general pharmacophore identification based on clique detection, genetic optimization, and pharmacophore alignment with ranking. At Creative Biolabs, we have built several pharmacophore 2D/3D similarity search strategies for finding new hit molecules.
Rapid in silico selection of target-focused libraries from merchant repositories is a valuable and cost-effective approach. If the structure of an active compound is valid, a rapid 2D similarity search will be performed on multimillion compound databases. We have implemented a combination of the 2D approach with pharmacophore matching to search for inhibitors or antagonists. To demonstrate the power of virtual screening cascades, ligand efficiency indices are calculated as well.
We have developed a new approach to 3D pharmacophore representation for ligand-based modeling that doesn’t require the proposed geometry of active compounds (to be used as a template) or precise alignment of pharmacophores. There’re two strategies of training set selection. One assumes that all active compounds have a similar binding mode, while the other is based on that they have different binding modes. Here, models from strategy II are more accurate in their predictions, but retrieved fewer actives than strategy I, because the former is more complex and covers specific subspace of active compounds.
In modern drug research, pharmacophore concept is of great importance and promotes drug industry development. Owing to its convenience and efficiency, pharmacophore similarity indicates an essential approach for drug discovery, optimization, and development. As a first-class provider in virtual screening and modeling, Creative Biolabs has launched 2D/3D pharmacophore similarity search services and advanced computational methods for customers to predict and harvest ideal drug candidates. If you’re interested in our services, please don’t hesitate to contact us for more information.
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