A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology is a research paper published in PLoS Computational Biology (2010). On theSindex it has a DataRank of 2.7. It has been cited 76 times, with 56 citing works in its 1-hop citation network.
Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.652
From this paper's citation signal
Citation Network Contribution
2.1
From 51 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 24% comes from its base citations and 76% from the citation network (51 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.