Pharmacophore-based Virtual Screening Service
Virtual screening (VS) uses computer simulations to screen chemical compound libraries, identifying compounds most likely to bind to a specific target. Employing pharmacophore models as query criteria, it extracts target molecules with desired properties by searching compound libraries. CD ComputaBio provides professional pharmacophore-based virtual screening services, accelerating your drug discovery process.
Introduction to Pharmacophore-based Virtual Screening
Pharmacophore-based methods are widely used in drug discovery projects, not only for virtual screening but also for scaffold hopping, lead optimization, ligand analysis, target identification, multi-target drug design, and de novo drug design. Among these applications, pharmacophore-based virtual screening employs pharmacophore models, which describe the essential functional groups in a molecule related to bioactivity and their spatial arrangement, as the query to identify target molecules with similar pharmacophore features from large compound libraries.
Fig.1 The identification of new potential HDAC3 inhibitors. (Lanka G, et al., 2023)
Tools for Pharmacophore-based Virtual Screening
| Software |
Description |
References |
| ZINCPharmer |
An online interface for searching the purchasable compounds of the ZINC database using the Pharmer pharmacophore search technology. |
Koes et al. (2012) |
| Pharmit |
An online, interactive environment for the virtual screening of large compound databases using pharmacophores, molecular shape and energy minimization. |
Sunseri et al. (2016) |
| PharmacoNet |
The first deep-learning framework for pharmacophore modeling toward ultra-fast virtual screening. |
Seo et al. (2024) |
Our Services
In the process of drug development, identifying active compounds that interact with specific targets is crucial. Leveraging advanced computational tools and extensive database resources, CD ComputaBio provides professional pharmacophore-based virtual screening services to identify potentially active compounds from large compound libraries.
Pharmacophore Model Construction
CD ComputaBio builds high-quality pharmacophore models by analyzing the binding sites of target proteins and extracting key pharmacophore features based on target information or active ligand data provided by clients, providing a reliable basis for subsequent screening.
Compound Library Screening
Based on the pharmacophore model, we perform large-scale virtual screening of compound libraries to identify potential active compounds with similar pharmacophore features. We provide detailed screening reports covering activity prediction, structural information, and matching degree analysis.
Types of Molecules
CD ComputaBio supports virtual screening for various molecule types, including small molecules and peptides, by constructing pharmacophore models.
Peptide Drugs
PROTAC Drugs
Methods for Pharmacophore-based Virtual Screening
- Structure-based Pharmacophore Modelling
Utilizing the structural information of the macromolecular target (including the target protein itself or its complex structure bound with a ligand), the binding sites are analyzed, and ligand complementary features for binding to the target are derived and selected to construct pharmacophore models.
- Ligand-based Pharmacophore Modelling
Firstly, a training set consisting of known active ligand structures is constructed. Conformational isomers are generated for each ligand, and key pharmacophore features are extracted. By analyzing these features, common features of different pharmacophore models are derived, ultimately obtaining models that can be used for practical applications.
Are you looking for an efficient and precise virtual screening solution? CD ComputaBio provides customized services leveraging professional pharmacophore modeling and virtual screening technologies. Contact us now to learn more about our pharmacophore-based virtual screening services.
References:
- Lanka, G.; et al. Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors[J]. Computers in Biology and Medicine. 2023, 166: 107481.
- Koes, D R.; Camacho, C J. ZINCPharmer: pharmacophore search of the ZINC database[J]. Nucleic acids research. 2012, 40(W1): W409-W414.
- Sunseri, J.; Koes, D R. Pharmit: interactive exploration of chemical space[J]. Nucleic acids research. 2016, 44(W1): W442-W448.
- Seo, S.; Kim, W Y. PharmacoNet: deep learning-guided pharmacophore modeling for ultra-large-scale virtual screening[J]. Chemical Science. 2024, 15(46): 19473-19487.
* For Research Use Only.
Related Services
