In Silico Elucidation of the Molecular Mechanism Defining the Adverse Effect of Selective Estrogen Receptor Modulators is a research paper published in PLoS Computational Biology (2007). On theSindex it has a DataRank of 4.0. It has been cited 83 times, with 63 citing works in its 1-hop citation network.
Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein-drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein-ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein-ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.665
From this paper's citation signal
Citation Network Contribution
3.3
From 63 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 17% comes from its base citations and 83% from the citation network (63 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.
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