Sentence examples for based on compound structures from inspiring English sources

Using the Parzen-Rosenblatt Window method, predictive models of protein target associations were constructed based on compound structures.

Recently, a variety of in silico methods for compound target prediction have been proposed in the context of chemogenomics, where target prediction is based on compound structures and protein sequences as well as pre-existing knowledge from databases about known compound protein interactions [ 2– 7].

Current activities can be grouped into (1) quantitative structure-activity-relationship (QSAR) methods, computational models based on compound structure and focused on potential interactions of small molecules with major classes of proteins such as drug metabolizing enzymes [ 10- 15], transporters [ 16] and receptors [ 16- 18].

Other factors could be computationally analyzed based on compounds structures.

The underlying idea is that similar ligands are likely to interact with similar proteins, and the prediction is performed based on compound chemical structures, protein sequences and the currently known compound protein interactions.

The key idea of the chemogenomic approach is that chemically similar compounds are likely to interact with similar proteins, and the prediction is performed based on compound chemical structures and/or protein sequences [ 2- 7].

The chemical structure similarity is calculated based on compounds' MOLPRINT 2D descriptors and Tanimoto coefficient [ 21].

Molecular Descriptors generated by E-DRAGON[55, 56]- E-DRAGON is the free online version of DRAGON and it generates a matrix of 1666 molecular descriptors for the dataset based on the compounds' structure [13].

The full descriptor set for each molecule of the training and independent validation sets is provided as supplementary material (Additional files 3 and 4): Molecular Descriptors generated by E-DRAGON[55, 56]- E-DRAGON is the free online version of DRAGON and it generates a matrix of 1666 molecular descriptors for the dataset based on the compounds' structure [13].

This was done by assigning each compound to one of the three Cramer classes based on the compound's structures, assuming an uncertainty factor of 100, and using the point of departures established for each of the three Cramer classes [9].

Based on the structure, compound 9 is considered to be one of the pan-assay interference compounds (PAINS), which show activity across a range of assay platforms and against a range of proteins23.