Extending
            <i>In Silico</i>
            Mechanism-of-Action Analysis by Annotating Targets with Pathways: Application to Cellular Cytotoxicity Readouts

Liggi, Sonia; Drakakis, Georgios; Koutsoukas, Alexios; Cortés-Ciriano, Isidro; Martínez–Alonso, Patricia; Malliavin, Thérèse E.; Velázquez‐Campoy, Adrián; Brewerton, Suzanne; Bodkin, Michael J.; Evans, David A.; Glen, Robert C.; Carrodeguas, José A.; Bender, Andreas

doi:10.4155/fmc.14.137

Cited by 19 publications

(21 citation statements)

References 139 publications

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“…The allocation of a confidence score forms part of the manual curation processes that is applied to ChEMBL, and is used to describe the assay-to-target relationships represented in the database. Recent studies utilizing the ChEMBL database as a bioactivity data source have specified stringent confidence scores of 8 or 9 [ 39 , 46 , 51 ], and in order to explore this parameter more fully we sought to analyse the differences in the data sets that are extracted from the ChEMBL database when a variety of confidence scores are specified (Table 5 ). This parameter is unexplored throughout literature and its influence on resulting training-sets for target prediction is relatively undocumented.…”

Section: Methodsmentioning

confidence: 99%

“…Ligand-based predictions provide a basis for further analysis when attempting to rationalise mechanism-of-action. Liggi et al [ 46 ], extended the predictions from a PRW target prediction algorithm, by annotating enriched targets implicated with a cytotoxic phenotype, using associated pathways from the GO [ 47 , 48 ] and KEGG [ 49 , 50 ] pathway databases. The annotation of enriched targets with pathways gave improved insight into understanding the MOA of the phenotype, underlining the signalling pathways involved in cytotoxicity.…”

Section: Introductionmentioning

confidence: 99%

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Target prediction utilising negative bioactivity data covering large chemical space

et al. 2015

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BackgroundIn silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds.ResultsA Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76.ConclusionsThe inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0098-y) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Target prediction utilising negative bioactivity data covering large chemical space

et al. 2015

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“…Each predicted target was annotated with its full set of pathways from KEGG biological pathways (release 58.1) [ 54 ]. It was possible to annotate 405 out of 477 targets with KEGG biological pathways [ 55 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes

Zobir

Fauzi

Liggi

et al. 2016

Evidence-Based Complementary and Alternative Medicine

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Traditional Chinese medicine (TCM) still needs more scientific rationale to be proven for it to be accepted further in the West. We are now in the position to propose computational hypotheses for the mode-of-actions (MOAs) of 45 TCM therapeutic action (sub)classes from in silico target prediction algorithms, whose target was later annotated with Kyoto Encyclopedia of Genes and Genomes pathway, and to discover the relationship between them by generating a hierarchical clustering. The results of 10,749 TCM compounds showed 183 enriched targets and 99 enriched pathways from Estimation Score ≤ 0 and ≥ 5% of compounds/targets in a (sub)class. The MOA of a (sub)class was established from supporting literature. Overall, the most frequent top three enriched targets/pathways were immune-related targets such as tyrosine-protein phosphatase nonreceptor type 2 (PTPN2) and digestive system such as mineral absorption. We found two major protein families, G-protein coupled receptor (GPCR), and protein kinase family contributed to the diversity of the bioactivity space, while digestive system was consistently annotated pathway motif, which agreed with the important treatment principle of TCM, “the foundation of acquired constitution” that includes spleen and stomach. In short, the TCM (sub)classes, in many cases share similar targets/pathways despite having different indications.

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“…This project has received funding from the European Union's Framework Programme for Research and Innovation Horizon 2020(2014-2020…”

Section: Acknowledgmentsmentioning

confidence: 99%

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account

Drakakis

Cortés-Ciriano

Alexander-Dann

et al. 2019

CP Chemical Biology

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The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. C 2019 by John Wiley & Sons, Inc.Keywords: ChEMBL r cytotoxicity r in silico bioactivity prediction r mechanism of action r polypharmacology r toxicology modeling

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Extending In Silico Mechanism-of-Action Analysis by Annotating Targets with Pathways: Application to Cellular Cytotoxicity Readouts

Abstract: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.

Cited by 19 publications

References 139 publications

Target prediction utilising negative bioactivity data covering large chemical space

Target prediction utilising negative bioactivity data covering large chemical space

Global Mapping of Traditional Chinese Medicine into Bioactivity Space and Pathways Annotation Improves Mechanistic Understanding and Discovers Relationships between Therapeutic Action (Sub)classes

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account

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