2014
DOI: 10.1021/ci500276x
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Target-Independent Prediction of Drug Synergies Using Only Drug Lipophilicity

Abstract: Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values… Show more

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Cited by 28 publications
(23 citation statements)
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“…The lack of a clear clustering between the top synergistic compound structures in either datasets demonstrates the difficulty in selection of compounds to screen simply via structural similarity alone. In addition to the observation that synergy is more commonly observed for drugs targeting the same processes (Brochado et al, 2018 ), the relationship between compound structure-related properties and synergistic interaction has been shown previously [such as lipophilicity and synergy in the case of anti-fungals (Yilancioglu et al, 2014 )]. Overall, the inference of complex relationships, such as these on a scale that may quickly explode to intractable proportions is a task highly applicable to machine learning.…”
Section: Resultsmentioning
confidence: 87%
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“…The lack of a clear clustering between the top synergistic compound structures in either datasets demonstrates the difficulty in selection of compounds to screen simply via structural similarity alone. In addition to the observation that synergy is more commonly observed for drugs targeting the same processes (Brochado et al, 2018 ), the relationship between compound structure-related properties and synergistic interaction has been shown previously [such as lipophilicity and synergy in the case of anti-fungals (Yilancioglu et al, 2014 )]. Overall, the inference of complex relationships, such as these on a scale that may quickly explode to intractable proportions is a task highly applicable to machine learning.…”
Section: Resultsmentioning
confidence: 87%
“…Computational approaches have been investigated as a means to predict the synergistic interaction of compounds previously, with methods that utilize networks of pathways and simulation (Lehár et al, 2007 ; Nelander et al, 2008 ; Miller et al, 2013 ; Huang et al, 2014 ; Patel et al, 2014 ; Zhang et al, 2014 ), relationships between physicochemical properties (Yilancioglu et al, 2014 ), chemogenomics approaches (Bansal et al, 2014 ; Wildenhain et al, 2015 ; KalantarMotamedi et al, 2018 ), and single agent efficacies (Gayvert et al, 2017 ) and/or combinations (Menden et al, 2018 ) measured across multiple cell lines (for recent reviews of compound combination modeling and perspectives, see Bulusu et al, 2016 ; Weinstein et al, 2017 ; Tsigelny, 2018 ). A disadvantage to many of these approaches is that they often require experimental knowledge of underlying biological interactions between drugs and disease, or chemogenomic or phenotypic readouts (Jansen et al, 2009 ; Bansal et al, 2014 ; Wildenhain et al, 2015 ; Menden et al, 2018 ).…”
Section: Introductionmentioning
confidence: 99%
“…The degree of synergy for each combination was indicated by the difference in growth inhibition observed by experiment from that predicted under the Bliss Independence model [ 22 ]. Other all-pairs combinatorial datasets include a 90 compound set (consisting of drugs and probes) assayed against the HCT116 colon cancer cell line [ 11 ], a set of 11 anticancer drugs tested also tested against HCT116 [ 23 ], a set 31 antifungal compounds assayed against S. cerevisiae [ 24 , 25 ], and an assay of 22 antibiotics against Escherichia coli [ 16 ]. Each of these datasets measure dose response surfaces [ 5 ], and derive synergy metrics from those surfaces (see original papers for examples).…”
Section: Introductionmentioning
confidence: 99%
“…Network representation (see Fig. 2 ) for all pairs combination data is also popular [ 3 , 15 , 16 , 24 , 25 , 32 ]—nodes correspond to compounds, and edges to combinations, connecting their components. Edges may be coloured according to sign, and weighted according to degree of synergy.…”
Section: Introductionmentioning
confidence: 99%
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