Follow me on Twitter
I agree that content from Twitter will be displayed to me.
Head of Group
Director of Corporate Program
His main research interests are ligand- and structure-based drug design with focus on transmembrane transport proteins, prediction of on- and off-kinetics, as well as semantic data integration.
Publications
Showing entries 1 - 5 out of 311
Digles, D, Ingles-Prieto, A, Dvorak, V, Mocking, TAM, Goldmann, U, Garofoli, A, Homan, EJ, Di Silvio, A, Azzollini, L, Sassone, F, Fogazza, M, Bärenz, F, Pommereau, A, Zuschlag, Y, Ooms, JF, Tranberg-Jensen, J, Hansen, JS, Stanka, J, Sijben, HJ, Batoulis, H, Bender, E, Martini, R, IJzerman, AP, Sauer, DB, Heitman, LH, Manolova, V, Reinhardt, J, Ehrmann, A, Leippe, P, Ecker, GF, Huber, KVM, Licher, T, Scarabottolo, L, Wiedmer, T & Superti-Furga, G 2024, 'Advancing drug discovery through assay development: a survey of tool compounds within the human solute carrier superfamily', Frontiers in Pharmacology, vol. 15, 1401599. https://doi.org/10.3389/fphar.2024.1401599
Granulo, N, Sosnin, S, Digles, D & Ecker, GF 2024, 'The macrocycle inhibitor landscape of SLC‐transporter', Molecular Informatics, vol. 43, no. 5, e202300287. https://doi.org/10.1002/minf.202300287
Huang, J, Osthushenrich, T, MacNamara, A, Mälarstig, A, Brocchetti, S, Bradberry, S, Scarabottolo, L, Ferrada, E, Sosnin, S, Digles, D, Superti-Furga, G & Ecker, GF 2024, 'ProteoMutaMetrics: machine learning approaches for solute carrier family 6 mutation pathogenicity prediction', Rsc advances, vol. 14, no. 19, 00748, pp. 13083-13094. https://doi.org/10.1039/d4ra00748d
Ferrada, E, Wiedmer, T, Wang, WA, Frommelt, F, Steurer, B, Klimek, C, Lindinger, S, Osthushenrich, T, Garofoli, A, Brocchetti, S, Bradberry, S, Huang, J, MacNamara, A, Scarabottolo, L, Ecker, GF, Malarstig, A & Superti-Furga, G 2024, 'Experimental and Computational Analysis of Newly Identified Pathogenic Mutations in the Creatine Transporter SLC6A8', Journal of Molecular Biology, vol. 436, no. 2, 168383. https://doi.org/10.1016/j.jmb.2023.168383
Helmke, P, Füzi, B & Ecker, GF 2024, 'Bioactivity descriptors for in vivo toxicity prediction: now and the future', Expert Opinion on Drug Metabolism and Toxicology, vol. 20, no. 7, pp. 541-543. https://doi.org/10.1080/17425255.2024.2334308
Activities
Showing entries 11 - 15 out of 202
From unique protein identifiers to a selected pool of structures: Knime used as a mean to retrieve, analyze, interactively select and download protein 3D shapes
Riccardo Martini
,
Giulia Banci
,
Gerhard Ecker
KNIME Spring Summit 2019
Conference,
Poster presentation
20.3.2019 - 20.3.2019
Upsampling of Molecules to create datasets large enough for Deep learning in toxicity prediction
Jennifer Hemmerich
,
Ece Asilar
,
Gerhard Ecker
German Conference on Chemoinformatics
Conference,
Talk or oral contribution
12.11.2018 - 12.11.2018
DEEPHUNT: HEPATOTOXICITY USING NEURAL NETWORKS.
Ece Asilar
,
Jennifer Hemmerich
,
Gerhard Ecker
22nd EuroQSAR
Conference,
Talk or oral contribution
20.9.2018 - 20.9.2018
Exploring the use of neural networks for prediction of toxicities
Jennifer Hemmerich
,
Ece Asilar
,
Gerhard Ecker
Advanced Course on Data Science & Machine Learning
Summer/Winter school,
Talk or oral contribution
21.7.2018 - 21.7.2018
Predicting ligand residence time - it's all about how long you stay
Gerhard Ecker
European School of Medicinal Chemistry
Summer/Winter school,
Talk or oral contribution
4.7.2018 - 4.7.2018