Relating protein pharmacology by ligand chemistry

By Michael J. Keiser, Bryan L. Roth, Blaine N. Armbruster, P. Ernsberger, John J. Irwin, Brian K. Shoichet. 2007

2,137 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Molecular graph convolutions: moving beyond fingerprints (2016, methodology): “The similarity ensemble approach (SEA) builds upon traditional ﬁn-gerprint similarity methods for protein target iden-tiﬁcation, relating proteins by the similarity of their ligands (Keiser et al., 2007).”
Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, background): “Those chemical motifs characterize the protein binding site, and proteins can be related based on the chemical similarity of their ligands (13, 14).”
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (2018, background): “Currently, the structure based [2], ligand-similarity based [3] and machine learning based meth-ods [4, 5] are three main classes of prediction approaches in computational aided drug design.”
ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation (2015, methodology): “A method to predict protein targets based on chemical similarity of their ligands was proposed by Keiser et al. [18]. using Daylight-type topological fingerprints and extended-connectivity finger-prints.”
ChemProt-3.0: a global chemical biology diseases mapping (2016, positive): “The Similarity Ensemble Approach (SEA) (35) has been re-compiled on the ChemProt server and updated according to the novel zChemProt data and integrated into ChemProt 3.0.”
Molecular Systems Biology Peer Review Process File Indi: a Computational Framework for Inferring Drug Interactions and Their Associated Recommendations Transaction Report (neutral): “Drug side effects were obtained from SIDER (Kuhn et al, 2010).”
Analysis of chemical and biological features yields mechanistic insights into drug side effects. (2013, background): “This assumption has permitted to relate targets showing similar pharmacological profiles (Garcia-Serna et al., 2010; Keiser et al., 2007; Mestres et al., 2006) into clusters of seemingly disparate proteins, providing new insights into the function of the targets (Hillenmeyer et al.”
Docking & Chemoinformatic Screens for New Ligands and Targets (2009, methodology): “Drawing on a systematic map relating drug targets by their ligands, using BLAST-derived algorithms to normalize for random chemical similarity, we found that targets related by ligands quantitatively differed from those related by sequence [50].”
Network analyses in systems pharmacology (2009, background): “The authors use this score to construct a network of receptors connected together if they bind structurally similar ligands (Keiser et al., 2007).”
SuperPred: update on drug classification and target prediction (2014, background): “Moreover, the similarity ensemble approach (16) has been proposed.”