These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Mapping algorithms for molecular similarity analysis and ligand-based virtual screening: design of DynaMAD and comparison with MAD and DMC.
    Author: Eckert H, Vogt I, Bajorath J.
    Journal: J Chem Inf Model; 2006; 46(4):1623-34. PubMed ID: 16859294.
    Abstract:
    Here, we introduce the DynaMAD algorithm that is designed to map database compounds to combinations of activity-class-dependent descriptor value ranges in order to identify novel active molecules. The method combines and extends key features of two previously developed algorithms, MAD and DMC. These methods were first described as compound-mapping algorithms for large-scale virtual screening applications. DynaMAD and DMC operate in chemical spaces of stepwise increasing dimensionality. However, in contrast to DMC, which utilizes binary transformed descriptors, DynaMAD uses unmodified descriptor value distributions. The performance of these mapping methods was compared in detail in virtual screening trials on 24 different compound activity classes against a background of about 2 million database compounds. In these calculations, all three approaches produced results of considerable predictive value, and the enrichment of active molecules in small selection sets consisting of only about 20 or fewer database compounds emerged as a common feature. Furthermore, mapping methods were capable of recognizing remote molecular similarity relationships. Overall, DynaMAD performed better than MAD and DMC, producing average hit and recovery rates of 55% and 33%, respectively, over all 24 classes. Taken together, our findings suggest that dynamic compound mapping to combinations of activity-class-selective descriptor settings has significant potential for molecular similarity analysis and ligand-based virtual screening.
    [Abstract] [Full Text] [Related] [New Search]