Predicting new molecular targets for known drugs

By Michael J. Keiser, V. Setola, John J. Irwin, Christian Laggner, Atheir I. Abbas, S. Hufeisen, Niels H. Jensen, M. Kuijer, R. C. Matos, T. Tran and 7 more. 2009

1,590 citations recorded by the source corpus.

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, neutral): “promising new therapeutic strategies[15-20].”
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).”
Identify drug repurposing candidates by mining the Protein Data Bank (2011, methodology): “[8], by computing a pairwise similarity metric between ligand sets of various targets.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “In the other projection of drug–target networks, nodes are the drug targets, which are connected, if they both bind the same drug (Keiser et al., 2007, 2009; Ma'ayan et al., 2007; Yildirim et al., 2007; Hert et al., 2008; Yamanishi et al., 2008; Bleakley & Yamanishi, 2009; van Laarhoven et al.,…”
Computational approaches in target identification and drug discovery (2016, background): “To name a few, chemical structure similarity searching [8], data mining/ machine learning [9], panel docking [10], and bioactivity spectra based algorithms [11] have been routinely and successfully implemented [12,13].”
Deep learning with feature embedding for compound-protein interaction prediction (2016, background): “Identifying compound-protein (or drug-target) interactions is crucial in drug discovery and development in that it provides valuable insights into understanding efﬁcacy and off-target adverse side-effects of drugs [1] [2].”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, background): “Such negative instances might include potential drug–target interactions, which may be a possible reason why some concepts, such as polypharmacology or drug promiscuity, have been highlighted recently [1, 66].”
Computational Drug Repositioning: From Data to Therapeutics (2013, methodology): “49 An approach that uses knowledge of both compound and protein structure is molecular docking, 50 which estimates the physicochemical strength of ligand–protein interactions.”
Accurate and efficient target prediction using a potency-sensitive influence-relevance voter (2015, positive): “This approach was extensively studied by Shoichet et al. [2] and ranks molecules identically to the method they ultimately propose.”
Large Scale Prediction and Testing of Drug Activity on Side-Effect Targets (2012, methodology): “Here we present a large-scale, prospective evaluation of safety target prediction using one such method, the similarity ensemble approach (SEA) 25–27 .”