Structural feature-driven pattern analysis for multitarget modulator landscapes

Bioinformatics. 2022 Feb 7;38(5):1385-1392. doi: 10.1093/bioinformatics/btab832.


Motivation: Multitargeting features of small molecules have been of increasing interest in recent years. Polypharmacological drugs that address several therapeutic targets may provide greater therapeutic benefits for patients. Furthermore, multitarget compounds can be used to address proteins of the same (or similar) protein families for their exploration as potential pharmacological targets. In addition, the knowledge of multitargeting features is of major importance in the drug selection process; particularly in ultra-large virtual screening procedures to gain high-quality compound collections. However, large-scale multitarget modulator landscapes are almost non-existent.

Results: We implemented a specific feature-driven computer-aided pattern analysis (C@PA) to extract molecular-structural features of inhibitors of the model protein family of ATP-binding cassette (ABC) transporters. New molecular-structural features have been identified that successfully expanded the known multitarget modulator landscape of pan-ABC transporter inhibitors. The prediction capability was biologically confirmed by the successful discovery of pan-ABC transporter inhibitors with a distinct inhibitory activity profile.

Availability and implementation: The multitarget dataset is available on the PANABC web page ( and its use is free of charge.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • ATP-Binding Cassette Transporters* / chemistry
  • ATP-Binding Cassette Transporters* / metabolism
  • Humans


  • ATP-Binding Cassette Transporters