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Home Discovery CRO Virtual Screening

Virtual Screening

Virtual screening (VS) is a computational approach integral to drug discovery, serving as a cost-effective and efficient method for identifying potential hits. By simulating molecular interactions in silico, virtual screening allows researchers to prioritize and filter through numerous compounds, significantly reducing the number of candidates that need to be experimentally tested [1, 2, 3]. There are two main approaches inside VS: structure-based VS (SBVS) and ligand-based VS (LBVS) [3]. At Chemspace we offer numerous services for both approaches to accelerate your Drug Discovery projects.

Structure-Based Virtual Screening (SBVS)

SBVS or target-based VS (TBVS) attempts to predict the binding orientation of ligand (organic small molecule) and target molecule (protein or other biomacromolecule). The main requirement for this technique is presenting of good enough 3D structure of the target molecule. For SBVS we offer:
SBVS schema
Molecular Docking
Molecular docking is a computational method to predict the preferred orientation and ligand-target interactions, thus enabling scoring the molecules on how well they fit into the pocket of the target protein. Top molecules can be identified by the docking score and used in other computational methods or screened in the lab. We use ICM-Pro by MolSoft to complete the docking projects. MolSoft software performed well and outperformed a range of other methods in the numerous blinded modeling competitions and docking challenges including covalent docking, predicting protein-protein complexes [4].
4D Docking (Ensemble Docking)
Using structure assembles for docking computations we can run 4D docking. This protocol works twice as fast as regular docking in the number of structures separately and opens the opportunity to consider the flexibility of the target [4, 5].
3D target-performed pharmacophore modeling
By adding the excluded volumes based on information about ligand surroundings in the ligand-target complex we improve the classical pharmacophore. This approach outperforms pharmacophore screening, helps to avoid clashes, and gets more accurate results [6, 7].
SBVS schema
SBVS schema

Ligand-Based Virtual Screening (LBVS)

LBVS uses ligands with known biological activity aiming to identify molecules with similar structures. The main principle is that structurally similar molecules may exhibit comparable biological activity. To create a ligand-based model for screening the 1D (molecular weight, solubility, or other physicochemical properties), 2D (molecular topology) or 3D (shape) descriptors could be used. For LBVS we offer:
LBVS schema
Pharmacophore modeling
We support 2D and 3D pharmacophore models using the ligand structure information or 3D coordinates. By utilizing the common chemical features presented only in the most active compounds we enable the search for similar molecules in the chemical spaces [6].
2D/3D QSAR
Creation of 2D/3D QSAR models based on various methods of linear and nonlinear regressions as well as using ML tools [4, 8].
Shape-based screening
RIDE is a fast 3D molecular similarity search method based on Atomic Property Fields (APF) and enabled for Enamine REAL Database (5.6B molecules) and Chemspace Screening collection (7.2M molecules). Atomic Property Fields (APF) is a grid 3D pharmacophore potential that is generated from one or more high-affinity scaffolds with seven properties assigned from empiric physico-chemical components. These properties include hydrogen bond donors, acceptors, Sp2 hybridization, lipophilicity, size, electropositive/negative, and charge. APF has also been extended to multiple flexible ligand alignments using an iterative procedure [4]. Real-time ultrafast shape recognition with pharmacophoric constraints (USRCAT) - is the new fingerprint for 3D similarity search. We created a workflow that aims to find compounds with similar molecular 3D shapes to the reference ones, and at the same time to expand chemical diversity and identify new and potentially active scaffolds [9].
LBVS schema
LBVS schema

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References
  1. Slater, O.; Kontoyianni, M. The Compromise of Virtual Screening and Its Impact on Drug Discovery. Expert Opinion on Drug Discovery 2019, 14 (7), 619-637. https://doi.org/10.1080/17460441.2019.1604677
  2. Kontoyianni, M. Library Size in Virtual Screening: Is It Truly a Number's Game? Expert Opinion on Drug Discovery 2022, 17 (11), 1177-1179. https://doi.org/10.1080/17460441.2022.2130244
  3. Lavecchia, A.; Giovanni, C. Virtual Screening Strategies in Drug Discovery: A Critical Review. CMC 2013, 20 (23), 2839-2860. https://doi.org/10.2174/09298673113209990001
  4. https://www.molsoft.com/
  5. Amaro, R. E.; Baudry, J.; Chodera, J.; Demir, Ö.; McCammon, J. A.; Miao, Y.; Smith, J. C. Ensemble Docking in Drug Discovery. Biophysical Journal 2018, 114 (10), 2271-2278. https://doi.org/10.1016/j.bpj.2018.02.038
  6. Giordano, D.; Biancaniello, C.; Argenio, M. A.; Facchiano, A. Drug Design by Pharmacophore and Virtual Screening Approach. Pharmaceuticals 2022, 15 (5), 646. https://doi.org/10.3390/ph15050646
  7. Schaller, D.; Šribar, D.; Noonan, T.; Deng, L.; Nguyen, T. N.; Pach, S.; Machalz, D.; Bermudez, M.; Wolber, G. Next Generation 3D Pharmacophore Modeling. WIREs Comput Mol Sci 2020, 10 (4), e1468. https://doi.org/10.1002/wcms.1468
  8. Kwon, S.; Bae, H.; Jo, J.; Yoon, S. Comprehensive Ensemble in QSAR Prediction for Drug Discovery. BMC Bioinformatics 2019, 20 (1), 521. https://doi.org/10.1186/s12859-019-3135-4
  9. Kyrylchuk, A.; Kravets, I.; Cherednichenko, A.; Tararina, V.; Kapeliukha, A.; Dudenko, D.; Protopopov, M. Creation of Targeted Compound Libraries Based on 3D Shape Recognition. Mol Divers 2023, 27 (2), 939-949. https://doi.org/10.1007/s11030-022-10447-z