title: Integrated QSAR study for inhibitors of Hedgehog Signal Pathway against multiple cell lines:a collaborative filtering method.
abstract: BACKGROUND
  The Hedgehog Signaling Pathway is one of signaling pathways that are very important to embryonic development. The participation of inhibitors in the Hedgehog Signal Pathway can control cell growth and death, and searching novel inhibitors to the functioning of the pathway are in a great demand. As the matter of fact, effective inhibitors could provide efficient therapies for a wide range of malignancies, and targeting such pathway in cells represents a promising new paradigm for cell growth and death control. Current research mainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bind specifically to the Smo protein, and can be used for cancer therapy. While quantitatively structure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction of activity values of new compounds. In this study, we proposed a novel collaborative QSAR model for inhibitors of the Hedgehog Signaling Pathway by integration the information from multiple cell lines. Such a model is expected to substantially improve the QSAR ability from single cell lines, and provide useful clues in developing clinically effective inhibitors and modifications of parent lead compounds for target on the Hedgehog Signaling Pathway.
  
  
  RESULTS
  In this study, we have presented: (1) a collaborative QSAR model, which is used to integrate information among multiple cell lines to boost the QSAR results, rather than only a single cell line QSAR modeling. Our experiments have shown that the performance of our model is significantly better than single cell line QSAR methods; and (2) an efficient feature selection strategy under such collaborative environment, which can derive the commonly important features related to the entire given cell lines, while simultaneously showing their specific contributions to a specific cell-line. Based on feature selection results, we have proposed several possible chemical modifications to improve the inhibitor affinity towards multiple targets in the Hedgehog Signaling Pathway.
  
  
  CONCLUSIONS
  Our model with the feature selection strategy presented here is efficient, robust, and flexible, and can be easily extended to model large-scale multiple cell line/QSAR data. The data and scripts for collaborative QSAR modeling are available in the Additional file 1.
authors: College of Life Science and Biotechnology, Tongji University, Shanghai 200092, China.
authors: Jun
authors: J
authors: Gao
authors: Dongsheng
authors: D
authors: Che
authors: Vincent W
authors: VW
authors: Zheng
authors: Ruixin
authors: R
authors: Zhu
authors: Qi
authors: Q
authors: Liu
mesh_terms: D000970:Antineoplastic Agents / Q000737:chemistry* / Q000494:pharmacology*; D045744:Cell Line, Tumor; D049109:Cell Proliferation / Q000187:drug effects; D005260:Female; D053823:Hedgehog Proteins / Q000037:antagonists & inhibitors*; D006801:Humans; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D043562:Receptors, G-Protein-Coupled / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects; D000072086:Smoothened Receptor; D014704:Veratrum Alkaloids / Q000737:chemistry*
chemical_list: D000970:Antineoplastic Agents; D053823:Hedgehog Proteins; D043562:Receptors, G-Protein-Coupled; C491023:SMO protein, human; D000072086:Smoothened Receptor; D014704:Veratrum Alkaloids; C000541:cyclopamine