Virtual Screening

Application Note

Virtual Screening to Identify Calcium Channel Blockers

Virtual Screening

Over the past few years, virtual screening has emerged as a complementary approach to high-throughput screening and has become an important in silico technique in the pharmaceutical industry. It includes ligand- and structure-based methods. Pharmacophore modeling is one such approach where known active molecules are analyzed for common steric and electronic features responsible for drug−receptor interactions. Alternatively, protein−ligand crystal structure complexes can be used to construct specific receptor-based pharmacophore models. In this workflow, a ligand-based approach was selected to quickly identify novel TCCBs using bi-dimensional (2D) pharmacophoric fingerprints (FP) based on the clusters obtained from the Reaxys Medicinal Chemistry data retrieval.

Prior to this, an external molecular database was constructed and maintained. Millions of compounds coming from Reaxys Flat File were loaded into a database. After applying Lipinski drug-like filters, subsequent processing done on the screening database, included salt removal, duplicates suppression and standardization.
Next, by calculating the pharmacophoric fingerprints, the chemical space representation of these molecules was assessed. Ideally, two sets of fingerprints will be computed: ChemAxon’s Pharmacophoric Fingerprints or PF and CCG’s GpiDAPH3 fingerprints implemented in the MOE suite. Particularly for PF, several parameters have to be optimized for FP generation.

The subsequent analysis was limited to the software where the hits had higher chemical diversity. The virtual screening campaign provides 314 hits that are predicted to be active on the Cav3.2 channel. Some parameters were taken into account for compound selection:

  1. Molecular diversity and chemical originality.
  2. Compound availability and pricing.

Finally, based on a visual inspection, a subset of 39 unique molecules was selected for further biological evaluation.

Biological Assay

The subset of 39 unique molecules was tested on HEK293 cells transfected with the human Cav3.2 isoform. The compounds were purchased and tested at 10 µM for their ability to affect the functional activity of recombinant human Cav3.2 and the results are displayed in Figure 7. In total, 15 compounds were found to inhibit the Cav3.2 channel at >50% inhibition and of those 9 displayed very promising activity with >75% inhibition (Figure 8).

The biological screening results for the 39 compounds on Ca | Elsevier Whitepaper
Figure 7. The biological screening results for the 39 compounds on Cav3.2 channels, obtained using an electrophysiology experiment with a single concentration of 10 µM
The chemical structure of the 9 more active compounds (% inhibition >75%) in the Cav3.2 channel | Elsevier Whitepaper
Figure 8. The chemical structure of the 9 more active compounds (% inhibition >75%) in the Cav3.2 channel


In this study, a proposed workflow was described for identifying new TCCBs from a structurally diverse dataset of known active compounds using virtual screening procedures incorporating various bi-dimensional chemical and pharmacophoric fingerprints. Reaxys Medicinal Chemistry was used to easily and efficiently retrieve all relevant chemical and biological information for existing TCCBs and Reaxys Flat File as a source of chemical diversity. During hit selection from the virtual screening, further analysis of the potential ligands can be done, for example, to identify potential drug−drug interactions earlier in the discovery process.

Essential Drug Discovery Solution

Reaxys Medicinal Chemistry is an extensive database containing chemical information linked to in vitro and in vivo biological activities extracted from over 300,000 articles, 90,000 patents and 5,000 journals. More than 6 million chemical compounds are associated with their biological data (> 29 million bioactivity data points) and linked to information on 12,700 pharmacological targets, allowing the scientists to reveal connections between compounds, effects and targets. The data is indexed and normalized for maximum searchability and consistency.


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