Badapple: Bioassay data associative promiscuity pattern learning engine
Badapple: promiscuity patterns from noisy evidence, Jeremy J. Yang, Oleg Ursu, Christopher A. Lipinski, Larry A. Sklar, Tudor I. Oprea & Cristian G. Bologa, Journal of Cheminformatics volume 8, Article number: 29 (2016)
Badapple is a method for rapidly identifying likely promiscuous compounds via associated scaffolds. Badapple generates a score associated with a pragmatic, empirical definition of promiscuity, with the overall goal to identify “false trails” and streamline workflows. Unlike methods reliant on expert curation of chemical substructure patterns, Badapple is fully evidence-driven, automated, self-improving via integration of additional data, and focused on scaffolds. Badapple is robust with respect to noise and errors, and skeptical of scanty evidence.