A frequent problem in drug discovery is that an interesting lead has been identified, but it is unsuitable for optimization
Molecular interactions between bioactive compounds and their target proteins are primarily driven by complementarity of shape and electrostatic potential between the binding partners with high complementarity leading to tight binding.
As a consequence, compounds with high shape and electrostatic similarity to an active “lead molecule” have an increased probability of sharing the biological activity profile of the lead compound. Similar shapes can occur in compounds with very different chemical structures, and in this way 3D searching can identify novel compounds, so called “scaffold hopping”.
Our 3D molecular similarity methods quantify the overall shape and electrostatic similarity of a query to a database molecule. They return a rank-ordered list of the database, with a score of 1.0 indicting an identical structure and lower scores representing less similar compounds. A family of similarity measures are available. These differ in the details of the similarity measure. It is also possible to vary the relative contribution of shape and electrostatics, depending on the nature of the query and target sites.
Example: Zyprexa binds as an antagonist/inverse agonist at multiple G-protein coupled receptors:
To demonstrate the effectiveness of the technique, one needs to search a database of compounds with known activity. In this case we use the ChEMBL database. ChEMBL is a curated chemical database of bioactive molecules with drug-like properties maintained by the European Bioinformatics Institute (EBI), of the European Molecular Biology Laboratory (EMBL).
Some of the top scoring hits are clearly close structural analogues of Zyprexa, but others show little similarity at the 2D level.
|ChEMBL Hit||ChEMBL Targets||3D Similarity||2D Similarity
|CHEMBL339438||Muscarinic M1, M2||0.934||0.530|
|CHEMBL130978||5HT-1, 5HT-2, D2||0.927||0.576|
|CHEMBL336721||5HT-1, 5HT-2, D2||0.915||0.567|
|CHEMBL339852||5HT-1, 5HT-2, D||0.910||0.549|
|CHEMBL132993||5HT-1, 5HT-2, D||0.908||0.493|
|CHEMBL128041||5HT-1, 5HT-2, D||0.907||0.493|
The majority of hits share biological activities with Zyprexa.
Further information on the methods and example applications can be found in the references provided below.
Compounds provided by the client will be used as search queries, with a general protocol as follows:
Exploration of Piperidinols as Potential Antitubercular Agents. Abuhammad A, Fullam E, Bhakta S, Russell AJ, Morris GM, Finn PW and Sim E. Molecules 19, 16274-16290; doi:10.3390/molecules191016274 (2014).
Comparison of ultra-fast 2D and 3D ligand and target descriptors for side-effect prediction and network analysis in polypharmacology. Cortes-Cabrera A, Morris GM, Finn PW, Morreale A, Gago F, British Journal of Pharmacology, Br. J. Pharmacol. 170, 557-67 (2013).
Shape-based similarity searching in chemical databases. Finn PW, Morris GM, WIREs Comput Mol Sci. 2013, 3: 226-241. doi: 10.1002/wcms.1128 (2013),
Improving the accuracy of ultrafast ligand-based screening: incorporating lipophilicity into ElectroShape as an extra dimension. Armstrong MS, Finn PW, Morris GM, Richards WG. J Comput Aided Mol Des. 25, 785-790 (2011).
ElectroShape: fast molecular similarity calculations incorporating shape, chirality and electrostatics. Armstrong MS, Morris GM, Finn PW, Sharma R, Moretti L, Cooper RI, Richards WG. Journal of Computer- Aided Drug Design, 24, 789-801 (2010).
Molecular similarity including chirality. Armstrong MS, Morris GM, Finn PW, Sharma R and Richards WG. Journal of Molecular Graphics and Modelling 28, 368-370 (2009).
Radioreceptor binding profile of the atypical antipsychotic olanzapine. Bymaster FP, Calligaro DO, Falcone JF, Marsh RD, Moore NA, Tye NC, Seeman P, Wong DT. Neuropsychopharmacology 14, 87-96 (1996).
ChEMBL: a large-scale bioactivity database for drug discovery Gaulton A; et al. Nucleic Acids Research 40: D1100–7 (2011).