Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives

Richard K. Belew, Stefano Forli, David S. Goodsell, T. J. O'Donnell, Arthur J. Olson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We describe ADChemCast, a method for using results from virtual screening to create a richer representation of a target binding site, which may be used to improve ranking of compounds and characterize the determinants of ligand-receptor specificity. ADChemCast clusters docked conformations of ligands based on shared pairwise receptor-ligand interactions within chemically similar structural fragments, building a set of attributes characteristic of binders and nonbinders. Machine learning is then used to build rules from the most informational attributes for use in reranking of compounds. In this report, we use ADChemCast to improve the ranking of compounds in 11 diverse proteins from the Database of Useful Decoys-Enhanced (DUD-E) and demonstrate the utility of the method for characterizing relevant binding attributes in HIV reverse transcriptase.

Original languageEnglish (US)
Pages (from-to)1597-1607
Number of pages11
JournalJournal of Chemical Information and Modeling
Issue number8
StatePublished - Aug 22 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

Fingerprint Dive into the research topics of 'Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives'. Together they form a unique fingerprint.

Cite this