Explanation of the clusters shown in Fig 4.

<p>Clusters with 20 or fewer members are not described in the table in the interest of space.</p

Selected dependency paths and representative sentences.

<p>The drug and gene names flanking each path are bolded. Some key abbreviations are listed here: <i>appos</i>: appositional modifier, <i>amod</i>: adjectival modifier, <i>prep</i>: prepositional modifier (if <i>prep_of</i>, the specific preposition used is “of”, if <i>prep_to</i>, it’s “to”, if <i>prep_for</i>, it’s “for”), <i>nsubjpass</i>: passive nominal subject, <i>agent</i>: complement of passive verb, <i>dobj</i>: direct object of active verb, <i>nsubj</i>: noun subject of active verb.</p><p>Selected dependency paths and representative sentences.</p

Dendrogram illustrating the semantic relationships among 3514 drug-gene pairs.

<p>In this dendrogram, the leaves represent 3514 drug-gene pairs that co-occur in Medline sentences at least 5 times, and we have cut the dendrogram at various levels (illustrated by the red lines in the interior of the dendrogram) to produce the colored clusters shown around the edges. Drug-gene pairs that are known drug-target relationships from DrugBank are denoted by blue dots, and those that are known PGx relationships from PharmGKB are denoted by orange dots. The heights of the turquoise bars are proportional to how often the corresponding drug-gene pairs co-occur in Medline sentences (a proxy for how well-documented they are).</p

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side-effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in <50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs, and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly

Example of a dependency graph for a Medline 2013 sentence.

<p>(a) The raw sentence. (b) The complete dependency graph for the sentence. (c) The dependency path connecting the gene CYP3A4 with the drug rifampicin. (d) A more compact representation of the dependency path.</p

Top 20 predictions of new drug-target relationships for DrugBank.

<p>*** indicates that the drug has been shown experimentally to have modified the activity of the gene/protein</p><p>** means that the interaction is known to DrugBank but is listed under an alternate drug or gene name</p><p>* means the interaction has been studied and is unlikely; P refers to a particular type of parser error in which the ligand of a receptor is mistaken for that receptor; L refers to a lexicon error (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004216#sec008" target="_blank">Discussion</a>).</p><p>Top 20 predictions of new drug-target relationships for DrugBank.</p

Classifier performance at the task of recognizing (a) PGx associations (dense matrix), (b) drug-target associations (dense matrix), (c) PGx associations (sparse matrix) and (d) drug-target associations (sparse matrix).

<p>Classifier performance at the task of recognizing (a) PGx associations (dense matrix), (b) drug-target associations (dense matrix), (c) PGx associations (sparse matrix) and (d) drug-target associations (sparse matrix).</p

Le contrôle judiciaire du testament olographe lors de l'envoi en possession de l'article 1008 du Code civil

<p>A) Fragment 1049 and the microenvironments from the query aPKC structure associated with the fragment prediction. B) PDB ligand C58 and an alternate structure of aPKC bound to C58. Fragment 1049 substructure of C58 is in pink. C) Example nearest neighbor microenvironment from GSK3β. Fragment 1049 of the bound PDB ligand 0KD is in pink. The percent sequence identity between GSK3β and aPKC is in parentheses. D) Fragment 241 and the microenvironments from the query aPKC structure associated with the fragment prediction. E) PDB ligand BI1 and an alternate structure of aPKC bound to BI1. Fragment 241 substructure of BI1 is in purple. F) Example nearest neighbor microenvironment from GSK3β. Fragment 241 of the bound PDB ligand 679 is in purple. The percent sequence identity between GSK3β and aPKC is in parentheses. Proteins are shown in cartoon representation with microenvironments as semi-transparent spheres. Microenvironment color scheme is arbitrary but consistent between panels. Side chains corresponding to microenvironments are shown in stick representation. Ligands are also drawn in stick representation.</p

Top 20 predictions of new drug-gene relationships for PharmGKB, and whether a PGx relationship has been documented in the literature.

<p>*** indicates that an association has been demonstrated experimentally between changes in the expression/activity of the gene/protein and the efficacy of the drug</p><p>** indicates that such an association is likely, but has not yet been studied</p><p>* indicates that the association has been studied experimentally, and the experiment refuted the association. Here we include only associations between pharmaceutical compounds and single genes; predicted associations involving endogenous compounds and/or groups of genes are included in the supplement, however.</p><p>Top 20 predictions of new drug-gene relationships for PharmGKB, and whether a PGx relationship has been documented in the literature.</p

FragFEATURE performance on the validation ligands.

<p>A) Chemical structure (heavy atoms) of each validation ligand. B) FragFEATURE recall and precision on each validation ligand.</p