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 Targeting protein protein interaction (PPI) is a new challenge to current drug discovery. Due to growing interest in PPI inhibitors we represent focused compound libraries covering chemical space of PPI inhibitors.
The iPPI focused libraries designed by OTAVAchemicals comprise compounds that are selected on the basis of recently published PPI inhibitor models as well as similarity search.
iPPI Tree Library with 1176 compounds was designed with decision tree algorithm based on several molecular shape and functional group descriptors. This algorithm provides efficient partitioning between PPI inhibitors and non-PPI inhibitors [1]. In the resulted small library, more than 99% of compounds are detected as potential PPI inhibitors according to another model based on RDF070m and Ui descriptors [2].
iPPI Bayesian Library with 2508 compounds was prepared using Bayesian modeling (ECFP6 and FCFP6 fingerprints) based on known active PPI inhibitors (IC50<100 nM) taken from TIMBAL database. These PPI inhibitors were divided in two compound sets (RO4 and non-RO4 compliant). A number of physico-chemical descriptors (MW, HBD, HBA, LOGP, RB, No. of Rings) have been considered in each model. Final compound selection was carried out with Bayesian similarity score cut-off. Compounds with reactive groups were removed from the library.
These libraries were filtered by Lipinski rules but taking into account higher MW and LogP of known PPI inhibitors, MW constraints were set between 300 and 700 Da and LogP was set between 1 and 6 accordingly to distribution of these parameters showed in [1]. Number of hydrogen bond acceptors is between 0 and 10 and number of hydrogen bond donors is from 0 to 5. In addition, compounds with reactive groups and promiscuous inhibitors were removed from bothlibraries according to [3].
Properties of the iPPI focused libraries:
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Library
|
Average cLogP |
Average MW |
Average No of H bond acceptors |
Average No of H bond donors |
Average No of rotatable bonds |
Average No of rings |
Average PSA |
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iPPI Tree Library
|
4.1 |
497.1 |
5.7 |
1.1 |
7.9 |
4.5 |
99,9 |
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iPPI Bayesian Library non-RO4 compliant set
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2.4 |
354.5 |
4.3 |
1.9 |
5.4 |
3.1 |
89 |
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iPPI Bayesian Library RO4 compliant set
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4.1 |
449.1 |
4.9 |
1.4 |
6.4 |
4 |
94.5 |
All compounds are in stock, cherry-picking is available. If your research is dedicated to PPI inhibitors, you also might be interested in our Fragment Library and Peptidomimethics Library.
OTAVAchemicals Targeted Libraries Collection
| Name |
Number |
| Kinases |
69 |
| Proteases |
17 |
| GPCRs |
51 |
| Ion channels |
5 |
| Disease-based libraries |
74 |
| Epigenetic targets |
8 |
| Protein-Protein Interaction |
2 |
| Peptidomimetic |
2 |
| Nuclear Receptors |
12 |
| Glycomimetic |
1 |
| RNA binding |
1 |
| SH2 binding |
1 |
| General Activities |
128 |
| Other |
93 |
The libraries (SDF, XLS, PDF format) are available on request. Feel free to contact us or use on-line form below to send an inquiry if you are interested to obtain these libraries or if you need more information.
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Neugebauer A., Hartmann R.W., Klein C.D. Prediction of protein-protein interaction inhibitors by chemoinformatics and machine learning methods. J Med Chem. 2007 Sep 20;50(19):4665-8.
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Sperandio O., Reynès C.H., Camproux A.C., Villoutreix B.O. Rationalizing the chemical space of protein-protein interaction inhibitors. Drug Discov Today. 2010 Mar;15(5-6):220-9.
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Baell J. B. and Holloway G. A. New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays J. Med. Chem., 2010, 53 (7), pp 2719–2740
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