title: Probabilistic Substructure Mining From Small-Molecule Screens.
abstract: Identifying the overrepresented substructures from a set of molecules with similar activity is a common task in chemical informatics. Existing substructure miners are deterministic, requiring the activity of all mined molecules to be known with high confidence. In contrast, we introduce pGraphSig, a probabilistic structure miner, which effectively mines structures from noisy data, where many molecules are labeled with their probability of being active. We benchmark pGraphSig on data from several small-molecule high throughput screens, finding that it can more effectively identify overrepresented structures than a deterministic structure miner.
authors.affiliation: Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA, USA.
authors.forename: Sayan
authors.initials: S
authors.lastname: Ranu
authors.affiliation: Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, School of Medicine, St. Louis, MO, USA.
authors.forename: Bradley T
authors.initials: BT
authors.lastname: Calhoun
authors.affiliation: Department of Computer Science, University of California Santa Barbara, Santa Barbara, CA, USA.
authors.forename: Ambuj K
authors.initials: AK
authors.lastname: Singh
authors.affiliation: Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, Washington University, School of Medicine, St. Louis, MO, USA. swamidass@gmail.com.
authors.forename: S Joshua
authors.initials: SJ
authors.lastname: Swamidass
keywords: Chemoinformatics; Drug discovery; High throughput screening; Virtual screening