Explore the words cloud of the BIGCHEM project. It provides you a very rough idea of what is the project "BIGCHEM" about.
The following table provides information about the project.
Coordinator |
HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH
Organization address contact info |
Coordinator Country | Germany [DE] |
Project website | http://bigchem.eu |
Total cost | 2˙540˙146 € |
EC max contribution | 2˙540˙146 € (100%) |
Programme |
1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers) |
Code Call | H2020-MSCA-ITN-2015 |
Funding Scheme | MSCA-ITN-EID |
Starting year | 2016 |
Duration (year-month-day) | from 2016-01-01 to 2019-12-31 |
Take a look of project's partnership.
The advent of the big data era in chemistry and the life sciences requires the development of new computational analysis methods, which are not only of scientific, but also economic relevance. Currently, the international data market already grows six times faster than the entire IT sector, and growth rates further increase. Achieving and sustaining a leadership positions in the big data arena represent critically important challenges for the EU. The economic developments in the emerging big data field are science-driven. Due to complexity and heterogeneity of biochemical and biomedical data, large-scale data exploration and exploitation are intrinsically interdisciplinary tasks. BIGCHEM positions itself at interfaces between chemistry, computer science, and the life science to provide well-structured multidisciplinary training and educate high-in-demand computational specialists capable of operating in interdisciplinary and international research and business settings. Cornerstones of BIGCHEM’s curriculum include on-line lectures and periodic schools taught by internationally leading experts in chemical and life science informatics, a balanced consortium of academia, SMEs, and large industry, and an unprecedented symbiosis of academic and industrial training and application components. Accordingly, BIGCHEM is well positioned to boost multilateral collaborations between academia and industry and train scientists who are highly competitive in the international big data market. In BIGCHEM’s R&D and training activities, the development and evaluation of conceptually novel methods for large-scale data analysis, knowledge extraction, and information sharing with demonstrated practical application potential take center stage. The network has a clearly defined policy for exploitation of new IP through wide involvement of target users, SMEs, and large industry facilitated by the experienced technology transfer department of the coordinator's team.
Preparation of CDPs | Documents, reports | 2020-04-06 09:33:47 |
2nd Winter school report | Documents, reports | 2020-04-06 09:33:47 |
Publication of newsletter | Websites, patent fillings, videos etc. | 2020-04-06 09:33:47 |
Organisation of Open Days | Websites, patent fillings, videos etc. | 2020-04-06 09:33:47 |
1st Winter school report | Documents, reports | 2020-04-06 09:33:47 |
Overview of HTS data | Documents, reports | 2020-04-06 09:33:47 |
Minutes of the kick-off meeting | Documents, reports | 2020-04-06 09:33:47 |
Overview of strategies for data sharing | Documents, reports | 2020-04-06 09:33:47 |
Web site and application system for fellows | Websites, patent fillings, videos etc. | 2020-04-06 09:33:47 |
1st Summer school report | Documents, reports | 2020-04-06 09:33:47 |
Take a look to the deliverables list in detail: detailed list of BIGCHEM deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2020 |
Molecular generative models trained with small sets of molecules represented as SMILES strings are able to generate large regions of the chemical space. Unfortunately, due to the sequential nature of SMILES strings, these models are not able to generate molecules given a scaffold (i.e. partially-built molecules with explicit attachment points). Herein we report a new SMILES-based molecular generat https://chemrxiv.org/articles/SMILES-Based_Deep_Generative_Scaffold_Decorator_for_De-Novo_Drug_Design/11638383 published pages: 1, ISSN: 2573-2293, DOI: 10.26434/chemrxiv.11638383.v1 |
ChemRxiv 1 | 2020-04-06 |
2020 |
Withnall, Michael; Lindelöf, Edvard; Engkvist, Ola; Chen, Hongming Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction published pages: -, ISSN: 1758-2946, DOI: 10.26434/chemrxiv.9873599.v1 |
Journal of Chemoinformatics 12 | 2020-04-06 |
2019 |
Arkadii Lin Cartographie Topographique Générative: un outil puissant pour la visualisation, l\'analyse et la modélisation de données chimiques volumineuses published pages: , ISSN: , DOI: |
PhD thesis | 2020-04-06 |
2020 |
Raquel RodrÃguez Pérez Machine Learning Methodologies for Interpretable Compound Activity Predictions published pages: , ISSN: , DOI: |
PhD thesis | 2020-04-06 |
2019 |
Xuejin Zhang Exploration of synthetically accessible chemical space by de novo design published pages: , ISSN: , DOI: |
PhD thesis | 2020-04-06 |
2018 |
Gisbert Schneider Automating drug discovery published pages: 97-113, ISSN: 1474-1776, DOI: 10.1038/nrd.2017.232 |
Nature Reviews Drug Discovery 17/2 | 2020-04-06 |
2019 |
Kotsias, Panagiotis-Christos; Arús-Pous, Josep; Chen, Hongming; Engkvist, Ola; Tyrchan, Christian; Bjerrum, Esben Jannik Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs) published pages: , ISSN: , DOI: 10.26434/chemrxiv.9860906.v2 |
ChemRxiv 126 | 2020-04-06 |
2019 |
Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, Hongming Chen A de novo molecular generation method using latent vector based generative adversarial network published pages: , ISSN: 1758-2946, DOI: 10.1186/s13321-019-0397-9 |
Journal of Cheminformatics 11/1 | 2020-04-06 |
2020 |
Thakkar, Amol; Kogej, Thierry; Reymond, Jean-Louis; Engkvist, Ola; Bjerrum, Esben Jannik Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain published pages: 154–168, ISSN: 2041-6539, DOI: 10.1039/c9sc04944d |
Chemical Science 3 | 2020-04-06 |
2019 |
Raquel RodrÃguez-Pérez, Jürgen Bajorath Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values published pages: NA, ISSN: 0022-2623, DOI: 10.1021/acs.jmedchem.9b01101 |
Journal of Medicinal Chemistry September 12, 2019 | 2020-04-06 |
2019 |
Oliver Laufkötter, Tomoyuki Miyao, Jürgen Bajorath Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents published pages: 15304-15311, ISSN: 2470-1343, DOI: 10.1021/acsomega.9b02470 |
ACS Omega 4/12 | 2020-04-06 |
2019 |
Arkadii Lin, Dragos Horvath, Gilles Marcou, Bernd Beck, Alexandre Varnek Multi-task generative topographic mapping in virtual screening published pages: 331-343, ISSN: 0920-654X, DOI: 10.1007/s10822-019-00188-x |
Journal of Computer-Aided Molecular Design 33/3 | 2020-04-06 |
2019 |
Arkadii Lin, Bernd Beck, Dragos Horvath, Gilles Marcou, Alexandre Varnek Diversifying chemical libraries with generative topographic mapping published pages: , ISSN: 0920-654X, DOI: 10.1007/s10822-019-00215-x |
Journal of Computer-Aided Molecular Design | 2020-04-06 |
2019 |
Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, Ola Engkvist Exploring the GDB-13 chemical space using deep generative models published pages: 11:20, ISSN: 1758-2946, DOI: 10.1186/s13321-019-0341-z |
Journal of Cheminformatics 11/1 | 2020-04-06 |
2019 |
Raquel RodrÃguez-Pérez, Jürgen Bajorath Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors published pages: 4367-4375, ISSN: 2470-1343, DOI: 10.1021/acsomega.9b00298 |
ACS Omega 4/2 | 2020-04-06 |
2018 |
Raquel RodrÃguez-Pérez, Jürgen Bajorath Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data published pages: 12033-12040, ISSN: 2470-1343, DOI: 10.1021/acsomega.8b01682 |
ACS Omega 3/9 | 2020-04-06 |
2019 |
Laurianne David, Jarrod Walsh, Noé Sturm, Isabella Feierberg, J. Willem M. Nissink, Hongming Chen, Jürgen Bajorath, Ola Engkvist Identification of Compounds That Interfere with Highâ€Throughput Screening Assay Technologies published pages: 1795-1802, ISSN: 1860-7179, DOI: 10.1002/cmdc.201900395 |
ChemMedChem 14/20 | 2020-04-06 |
2019 |
Sergey Sosnin, Mariia Vashurina, Michael Withnall, Pavel Karpov, Maxim Fedorov, Igor V. Tetko A Survey of Multiâ€task Learning Methods in Chemoinformatics published pages: 1800108, ISSN: 1868-1743, DOI: 10.1002/minf.201800108 |
Molecular Informatics 38/4 | 2020-04-06 |
2019 |
Laurianne David, Josep Arús-Pous, Johan Karlsson, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej, Jan M. Kriegl, Bernd Beck, Hongming Chen Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research published pages: , ISSN: 1663-9812, DOI: 10.3389/fphar.2019.01303 |
Frontiers in Pharmacology 10 | 2020-04-06 |
2018 |
Dipan Ghosh, Uwe Koch, Kamyar Hadian, Michael Sattler, Igor V. Tetko Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays published pages: 933-942, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.7b00574 |
Journal of Chemical Information and Modeling 58/5 | 2020-04-06 |
2019 |
Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability published pages: , ISSN: 1758-2946, DOI: 10.1186/s13321-019-0376-1 |
Journal of Cheminformatics 11/1 | 2020-04-06 |
2018 |
Luca Pinzi, Fabiana Caporuscio, Giulio Rastelli Selection of protein conformations for structure-based polypharmacology studies published pages: 1889-1896, ISSN: 1359-6446, DOI: 10.1016/j.drudis.2018.08.007 |
Drug Discovery Today 23/11 | 2020-04-06 |
2018 |
Raquel RodrÃguez-Pérez, Tomoyuki Miyao, Swarit Jasial, Martin Vogt, Jürgen Bajorath Prediction of Compound Profiling Matrices Using Machine Learning published pages: 4713-4723, ISSN: 2470-1343, DOI: 10.1021/acsomega.8b00462 |
ACS Omega 3/4 | 2020-04-06 |
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The information about "BIGCHEM" are provided by the European Opendata Portal: CORDIS opendata.