Fragment-based Lead Preparation in Drug Discovery and Development

Expert-driven In Silico Drug Discovery Solutions
9 December 2021
Svitlana Kondovych
Senior Researcher

Fragment-based drug discovery (FBDD) is a powerful approach in drug design [1-4], which allows going beyond the conventional high-throughput screening (HTS). The fact of the matter is, small fragments bind to more sites of a target protein, thus producing a larger number of favorable interactions – hits. To identify the fragment hits, FBDD employs various screening methods based on advanced biophysical, biochemical, and bioinformatics techniques [1,2,5]. However, once the set of initial fragments is selected, a new challenge emerges: turning a fragment-based hit into a lead-like compound with further potential for drug development. At this stage, researchers aim at transforming the promising fragments into larger ligands with higher affinity and activity against the target. This hit-to-lead optimization can be realized via several strategies [1-3, 5-7]: fragment growing, merging, or linking (Fig. 1).

Fragment optimization approaches

Figure 1. Fragment optimization approaches [5] with examples of application [7]: a) fragment growing, b) fragment merging, and c) fragment linking.

Fragment growing (Fig. 1a) is the most straightforward approach to getting a functional compound. Within this strategy, additional chemical groups are attached to the fragment-based hit to increase its molecular weight and enhance the potency of ligand-receptor interaction. Such a “smart building” of fragment-based compounds brings results: Fig. 2 [1, 8-11] shows the examples of approved drugs obtained by fragment growing.

Approved drugs obtained with FBDD (picture from [1]).

Figure 2. Approved drugs obtained with FBDD (picture from [1]).

Fragment merging (also called scaffold hopping) is a practicable strategy when two fragments bind to overlapping regions of the same enzyme (Fig. 1b). In this case the structural similarity of the initial hits allows merging them into one compound with higher potency. The fragment concatenation occurs through the fusion of the overlapping parts; along with that, it is important to retain the binding properties of the initial fragments and their positions with respect to the target. The use of fragment merging for optimization may significantly increase the functionality of the resulting compound, as was achieved for several targets [12-13].

Fragment linking (Fig. 1c) aims to combine two or more fragments that bind to adjacent regions of a target protein. Compared to merging, linking is more challenging as it requires an extra component – a linker – that connects the selected fragments to provide optimal geometry and functionality of the resulting lead. The suitable linker should be a “puzzle detail” that keeps the favorable orientation of the initial fragments and has no negative effect on the compound activity. Despite the complexity of this strategy, it is a powerful way of fragment optimization and consequent drug design due to the striking increase of potency in case of success [1,3,7]. 

The hit-to-lead optimization can be performed using each of the strategies mentioned above, or their combination, and in parallel with other approaches such as HTS. In this way, the advanced methods of FBDD are boosting the complex process of finding and verifying the drug candidates.

To contribute to a successful development  of the FBDD programs, Life Chemicals has designed a variety of Fragment Libraries based on its proprietary Fragment-like Compound Collection (examples Fig.3). Our experienced R&D team can assist you in design and synthesis of compound libraries based on your hits and scaffolds, SAR exploration and hit-to-lead optimization. Read more about our custom synthesis services.

Please, contact us at for any additional information and price quotations.

Please, visit our Website for more information and download SD files with compound structures in the Downloads section. Custom compound selection based on specific parameters can be performed on request, with competitive pricing and the most convenient terms provided.


Figure 3. Representative fragment-like molecules with experimental solubility data from Life Chemicals Fragment Collection


  1. Li, Q. (2020). Application of Fragment-Based Drug Discovery to Versatile Targets. Frontiers in Molecular Biosciences, 7, 180. doi:10.3389/fmolb.2020.00180
  2. Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W., & Jhoti, H. (2016). Twenty years on: the impact of fragments on drug discovery. Nature Reviews Drug Discovery, 15(9), 605–619. doi:10.1038/nrd.2016.109
  3. Kirsch, P., Hartman, A. M., Hirsch, A. K. H., & Empting, M. (2019). Concepts and Core Principles of Fragment-Based Drug Design. Molecules, 24(23), 4309. doi:10.3390/molecules24234309
  4. Hann, M. M., Leach, A. R., & Harper, G. (2001). Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery. Journal of Chemical Information and Computer Sciences, 41(3), 856–864. doi:10.1021/ci000403i
  5. De Souza Neto L. R., Moreira-Filho J. T., Neves B. J., et al. (2020). In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery. Front. Chem. 8:93. doi: 10.3389/fchem.2020.00093
  6. Velvadapu, V., Farmer, B.T., Reitz, A.B. (2015) Chapter 7: Fragment-Based Drug Discovery. The Practice of Medicinal Chemistry. doi:10.1016/B978-0-12-417205-0.00007-9
  7. Ferreira L.G. and Andricopulo A.D. (2017). From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery, Current Topics in Medicinal Chemistry, 17(20). doi:10.2174/1568026617666170224113437
  8. Zhang, C., Ibrahim, P. N., Zhang, J., et al. (2013). Design and pharmacology of a highly specific dual FMS and KIT kinase inhibitor. Proc. Natl. Acad. Sci. U.S.A. 110, 5689–5694. doi: 10.1073/pnas.1219457110
  9. Tsai, J., Lee, J. T., Wang, W., et al. (2008). Discovery of a selective inhibitor of oncogenic B-Raf kinase with potent antimelanoma activity. Proc. Natl. Acad. Sci. U.S.A. 105, 3041–3046. doi: 10.1073/pnas.0711741105
  10. Souers, A. J., Leverson, J. D., Boghaert, E. R., et al. (2013). ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat. Med. 19, 202–208. doi: 10.1038/nm.3048
  11. Murray, C. W., Newell, D. R., and Angibaud, P. (2019). A successful collaboration between academia, biotech and pharma led to discovery of erdafitinib, a selective FGFR inhibitor recently approved by the FDA. Med Chem Comm 10, 1509–1511. doi: 10.1039/C9MD90044F
  12. Li, Q., Meng, L., Zhou, S., et al. (2019). Rapid generation of novel benzoic acid–based xanthine derivatives as highly potent, selective and long acting DPP-4 inhibitors: scaffold-hopping and prodrug study. Eur. J. Med. Chem. 180, 509–523. doi: 10.1016/j.ejmech.2019.07.045
  13. Zhang, P., Jia, L., Tian, Y., et al. (2020). Discovery of potential Toxoplasma gondii CDPK1 inhibitors with new scaffolds based on the combination of QSAR and scaffold-hopping method with in-vitro validation. Chem. Biol. Drug Design 95, 476–484. doi: 10.1111/cbdd.13603


9 December 2021, 14:29 Svitlana Kondovych Computational Chemistry

Comments ()

    This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used. By using our website, you accept our conditions of use of cookies to track data and create content (including advertising) based on your interest. Accept