Sphingosine-1-phosphate receptor 1 (S1PR1, S1P receptor 1, S1P1, endothelial differentiation gene 1, EDG1) is a G-protein-coupled receptor which binds the bioactive signaling molecule sphingosine 1-phosphate (S1P). S1PR1 has an important role in regulating endothelial cell cytoskeletal structure, migration, vascular maturation and capillary-like network formation. Also S1PR1 signaling is important in the regulation of lymphocyte maturation, migration and trafficking. Abnormal functioning of S1PR1 is associated with cancer and multiple sclerosis. Agonists and antagonists of this receptor may have therapeutic potential in the treatment of such diseases.

OTAVAchemicals offers Sphingosine-1-phosphate receptor 1 (S1PR1) Targeted Libraries with predicted agonist and antagonist activity against S1PR1:

• S1PR1 Agonist Library - 582 compounds
• S1PR1 Antagonist Library - 1076 compounds

The libraries have been carefully designed with application of machine learning (artificial neural networks, Bayesian statistics and k-nearest neighbors algorithm (k-NN)).

For the libraries design known S1PR1 agonists were clustered into four clusters and antagonists into three clusters. Then compounds of each cluster were worked out by k-NN based on molecular fingerprints to achieve more homogeneous sets and randomly divided into training and test sets. The training sets were used for development and parameterization of Bayesian and artificial neural networks models. Both methods were based on different molecular descriptors - number of rings, number of hydrogen donors and acceptors, LogP, molecular weight, number of rotatable bonds, PSA, topological descriptors, fingerprints and other. Also the training sets were used as a template for compounds selection with k-NN based on different fingerprints. The test sets were used for validation of Bayesian models and neural networks. Top-scored compounds obtained by application of different machine learning methods were crossed and visually analyzed. The combination of artificial neural networks, Bayesian statistics and k-NN should allow increasing the number of active compounds identified during screening.

The designed Sphingosine-1-phosphate receptor 1 Targeted Libraries comprise only drug-like compounds (PAINS compounds are filtered off). They provide a perfect basis for drug discovery projects related with cancer and multiple sclerosis.

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