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Drug2ways: Reasoning over causal paths in biological networks for drug discovery

: Rivas-Barragan, Daniel; Mubeen, Sarah; Guim Bernat, Francesc; Hofmann-Apitius, Martin; Domingo-Fernández, Daniel

Volltext urn:nbn:de:0011-n-6185668 (1.3 MByte PDF)
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Erstellt am: 17.12.2020

PLoS Computational Biology. Online journal 16 (2020), Nr.12, Art. e1008464, 21 S.
ISSN: 1553-7358
ISSN: 1553-734X
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()
Networks; systems biology; Causal Reasoning; drug discovery

Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multi-modal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single- target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.