CC BY 4.0Rivas-Barragan, DanielDanielRivas-BarraganMubeen, SarahSarahMubeenGuim Bernat, FrancescFrancescGuim BernatHofmann-Apitius, MartinMartinHofmann-ApitiusDomingo-Fernández, DanielDanielDomingo-Fernández2022-03-0617.12.20202020https://publica.fraunhofer.de/handle/publica/26547110.1371/journal.pcbi.1008464Elucidating 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.enNetworkssystems biologyCausal Reasoningdrug discovery003570005006518journal article