eTRANSAFE: data science to empower translational safety assessment. Sanz F, Pognan F, Steger-Hartmann T, et al. Nat. Rev. Drug. Discov. 2023 Aug 22(8):605-606.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia. Rodríguez-Belenguer P, Kopańska K, Llopis-Lorente J, et al. Comput. Methods Programs Biomed. 2023 Mar;230:107345.

Genomic and proteomic biomarker landscape in clinical trials. Piñero J, Rodriguez Fraga P, Valls-Margarit J, et al. Comput. Struct. Biotechnol J. 2023 Mar 16;21:2110-2118.

Identifying multiscale translational safety biomarkers using a network-based systems approach. Callegaro G, Schimming J, Piñero J, et al. Sci. 2023 Jan 31;26(3):106094.

Making in silico predictive models for toxicology FAIR. Cronin M, Belfield S, Briggs K. Regul Toxicol Pharmacol. 2023 May;140:105385.

The evolving role of investigative toxicology in the pharmaceutical industry. Pognan F, Beilmann M, Boonen H, et al. Nat. Rev. Drug. Discov. 2023 Apr;22(4):317-335.

Benchmarking post-GWAS analysis tools in major depression: Challenges and implications. Pérez-Granado J, Piñero J, Furlong L. Front. Genet. 2022 Oct 5;13:1006903.

Using Jupyter Notebooks for re-training machine learning models. Smajić A, Grandits M, Ecker G. J Cheminform. 2022 Aug 13;14(1):54.

Unraveling the effect of intra- and intercellular processes on acetaminophen-induced liver injury. Heldring M, Shaw A, Beltma J. NPJ Syst Biol Appl. 2022 Aug 6;8(1):27.

Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules.  Naga D, Muster W, Musvasva E, Ecker G. CJ Cheminform. 2022 May 7;14(1):27.

Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry. Pastor M, Sanz F, Bringezu F. Methods Mol Biol. 2022;2425:119-131.

eTRANSAFE: Building a sustainable framework to share reproducible drug safety knowledge with the public domain. Sarntivijai S, Blomberg N, Lauer KB, Briggs K, Steger-Hartmann T, Van der Lei J, et al.  F1000Research. 2022;11(ELIXIR):287.

Mapping the cellular response to electron transport chain inhibitors reveals selective signaling networks triggered by mitochondrial perturbation. Van der Stel W, Yang H, Vrijenhoek N, Schimming J, Callegaro G, Carta G, et al. Arch. Toxicol. 2022;96(1):259-285.

Ensemble Prediction of Mitochondrial Toxicity Using Machine Learning Technology.  Bringezu F, Gómez-Tamayo J, Pastor M. Comput. Toxicol. 2021;20:100189.

The human hepatocyte TXG-MAPr: WGCNA transcriptomic modules to support mechanism-based risk assessment. Callegaro G, Kunnen S, Trairatphisan P, Grosdidier S, Niemeijer M, den Hollander W, et al. Arch Toxicol. 2021;95(12):3745-3775.

The DisGeNET cytoscape app: Exploring and visualizing disease genomics data. Piñero J, Saüch J, Sanz F, Furlong L.  Comput. Struct. Biotechnol. J. 2021;19:2960-2967.

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment. Rathman J, Yang C, Ribeiro J, Mostrag A, Thakkar S, Tong W, et al. Chem. Res. Toxicol.  2021;34(2):601–615.

Flame: an open source framework for model development, hosting, and usage in production environments. Pastor M, Gómez-Tamayo JC, Sanz F. J. Cheminformatics. 2021;13:31.

An ensemble learning approach for modeling the systems biology of drug-induced injury. Aguirre-Plans J, Souza T, Piñero J, Callegaro G, Kunnen SJ, Sanz F, et al. Biol. Direct. 2021;16:5.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives. Pognan F, Steger-Hartmann T, Díaz C, Blomberg N, Bringezu F, Briggs K, et al. Pharmacol. 2021;14(3):237.

Guidelines for FAIR sharing of preclinical safety and off-target pharmacology data. Briggs K, Bosc N. Camara T, Diaz C, Drew P, Drewe W, et al.T. Altex. 2021;8(2):187-197.

The DisGeNET knowledge platform for disease genomics: 2019 update. Piñero P, Ramírez-Anguita J, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. Nucleic Acids Res. 2020;48(D1):D845–D855.

In silico Toxicology: from structure-activity relationships towards deep learning and adverse outcome pathways.  Hemmerich J, Ecker G. WIREs Comput. Mol. Sci. 2020;10:e1475.

Using machine learning methods and structural alerts for prediction of mitochondrial toxicity. Hemmerich J, Troger F, Füzi B, Ecker G. Mol. Inform. 2020;39:2000005.

Image based liver toxicity prediction. Asilar E, Hemmerich J, Ecker GF. J. Chem. Inf. Model. 2020;60(3):1111–1121.

Cover: Conformational Oversampling as Data Augmentation for Molecules. Hemmerich J, Asilar E, Ecker GF. J. of Cheminformatics. 2020;12(1):18.

Introducing the Concept of Virtual Control Groups into Preclinical Toxicology Testing. Steger-Hartmann T, Kreuchwig A, Vaas L, Wichard J, Bringezu F, Amberg A, et al. Altex. 2020;37(3):343-349.

GUILDify v2. 0: a tool to identify molecular networks underlying human diseases, their comorbidities and their druggable targets. Aguirre-Plans J, Piñero P, Sanz F, Furlong L, Fernandez-Fuentes N, Oliva B, et al. J. Mol. Biol. 2019;431(13):2477-2484.

High-throughput confocal imaging of differentiated 3D liver-like spheroid cellular stress response reporters for identification of drug-induced liver injury liability.  Hiemstra S, Ramaiahgari S, Wink S, Callegaro G, Coonen M, Meerman J, et al. Arch. Toxicol . 2019;93(10):2895-2911.

Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury. Copple I, den Hollander W, Callegaro G, Mutter F, Maggs J, Schofield A, et al. Arch Toxicol. 2019;93(2):385-399.

Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery. Bosc N, Atkinson F, Félix E, Gaulton A, Hersey A, Leach AR. J. Cheminformatics. 2019;11(1):4.

Reply to “Missed opportunities in large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery”. Bosc N, Atkinson F, Félix E, Gaulton A, Hersey A, Leach AR. J. Cheminformatics. 2019;11(1):64.

Computational approaches for Drug-induced liver injury (DILI) prediction: state of the art and challenges. Béquignon O, Pawar G, van de Water B, Cronin M, van Westen G. Biomed. SCI. 2019;2:308-329.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Pawar G, Madden J, Ebbrell D, Cronin M. Front. Pharmacol. 2019;10:561.

PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts. Armengol-Estapé J, Soares F, Marimon M, Krallinger M. Genomics Inform. 2019;17(2):e15. 

Curation and analysis of clinical pathology parameters and histopathologic findings from eTOXsys, a large database project (eTOX) for toxicologic studies. Pinches M, Thomas R, Porter R, Camidge L, Briggs K. Regul. Toxicol. Pharm. 2019;107:104396.

In Silico models in drug development: where we are. Piñero J, Furlong L, Sanz F. Curr. Opin. Pharmacol. 2018;42:111-121.

Beyond SEND: Leveraging non-clinical data to drive translational research forward. Briggs K, Friedman B, Ferrara K, Pinches M, Drewe W, Riel C, et al. DDW. 2018. 

The eTOX Consortium: To Improve the Safety Assessment of New Drug Candidates. Steger-Hartmann T, Pognan F.l.  Basic. Clin. Pharmacol. Toxicol. 2018 Sep;123 Suppl 5:29-36.

Improving the Safety Assessment of Chemicals and Drug Candidates by the Integration of Bioinformatics and Chemoinformatics Data. Steger-Hartmann T, Pognan F. Basic Clin. Pharmacol. Toxicol. 2018;123:29–36

Development of an Infrastructure for the Prediction of Biological Endpoints in Industrial Environments. Lessons Learned at the eTOX Project. Pastor M, Quintana J, Sanz F. Front. Pharmacol. 2018;9:1147.

Network, Transcriptomic and Genomic Features Differentiate Genes Relevant for Drug Response Piñero J, Gonzalez-Perez A, Guney E, Aguirre-Plans J, Sanz F, Oliva B, et al. Front. Genet. 2018;9:412.

Generating modelling data from repeat-dose toxicity reports. López-Massaguer O, Pinto-Gil K, Sanz F, Amberg A, Anger L, Stolte M, et al.  Toxicol. SCI 2018;162(1):287-300.

Legacy data sharing to improve drug safety assessment: the eTOX project. Sanz F, Pognan F, Steger-Hartmann T, Díaz C, Cases M, Pastor M, et alNat. Rev. Drug. Discov. 2017;16:811-812.


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