Consensus Predictive Model for Human K562 Cell Growth Inhibition through Enalos Cloud Platform. (30th January 2018)

• Record Type: Journal Article
• Authors: Afantitis, Antreas; Leonis, Georgios; Gambari, Roberto; Melagraki, Georgia
• Is Part Of: ChemMedChem. Volume 13:Number 6(2018)
• Page Start: 555
• Page End: 563
• Publication Date: 2018-01-30
• Subjects: cheminformatics -- drug discovery -- hemoglobin -- inhibitors -- predictive modeling; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.1002/cmdc.201700675
• Languages: English
• ISSNs: 1860-7179

Abstract

Abstract: β‐Thalassemia is an inherited hematologic disorder caused by various mutations of the β‐globin gene, thus resulting in a significant decrease in adult hemoglobin (HbA) production. An increase in fetal hemoglobin (HbF) levels by drug molecules is considered of great potential in β‐thalassemia treatment and is expected to counterbalance the impaired production of HbA. In this work, based on a set of 129 experimentally tested biological inhibitors, we developed and validated a computational model for the prediction of K562 functional inhibition, possibly associated with HbF induction. To facilitate future advancements in the field, we incorporated our model into Enalos Cloud Platform, which enabled online access to our computational scheme (http://enalos.insilicotox.com/K562 ) through a user‐friendly interface. This web service is offered to the wider community to promote in silico drug discovery through fast and reliable predictions. Abstract : Predictive model : A large dataset of K562 biological inhibitors is computationally treated to identify compounds that possibly have therapeutic action against β‐thalassemia. A predictive computational model for K562 inhibition is developed and validated. The model facilitates fast and reliable virtual screening of new molecules and is freely available online.