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BIGCHEM SIGNED

Big Data in Chemistry

Total Cost €

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EC-Contrib. €

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Partnership

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 BIGCHEM project word cloud

Explore the words cloud of the BIGCHEM project. It provides you a very rough idea of what is the project "BIGCHEM" about.

sustaining    academic    involvement    chemistry    industrial    entire    advent    internationally    practical    complexity    network    bigchem    facilitated    six    faster    structured    era    scientists    schools    scientific    exploration    critically    multidisciplinary    ip    positioned    positions    biochemical    intrinsically    smes    stage    relevance    cornerstones    boost    academia    demand    data    accordingly    multilateral    educate    heterogeneity    international    demonstrated    business    settings    conceptually    big    sharing    rates    transfer    extraction    science    center    leadership    times    interdisciplinary    experts    grows    line    competitive    taught    curriculum    team    informatics    lectures    balanced    specialists    symbiosis    market    computational    economic    industry    components    evaluation    arena    computer    periodic    unprecedented    interfaces    itself    biomedical    collaborations    training    sciences    life    train    policy    chemical   

Project "BIGCHEM" data sheet

The following table provides information about the project.

Coordinator
HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH 

Organization address
address: INGOLSTADTER LANDSTRASSE 1
city: NEUHERBERG
postcode: 85764
website: www.helmholtz-muenchen.de

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 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

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH DE (NEUHERBERG) coordinator 249˙216.00
2    ASTRAZENECA AB SE (SODERTAELJE) participant 703˙091.00
3    BOEHRINGER INGELHEIM PHARMA GMBH &CO KG DE (INGELHEIM) participant 463˙819.00
4    RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAT BONN DE (BONN) participant 443˙051.00
5    UNIVERSITAET BERN CH (BERN) participant 206˙287.00
6    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) participant 132˙613.00
7    UNIVERSITE DE STRASBOURG FR (STRASBOURG) participant 131˙437.00
8    LEAD DISCOVERY CENTER GMBH DE (DORTMUND) participant 124˙608.00
9    UNIVERSITA DEGLI STUDI DI MODENA E REGGIO EMILIA IT (MODENA) participant 86˙020.00
10    CHEMOTARGETS S.L. ES (Barcelona) partner 0.00
11    ENAMINE LIMITED LIABILITY COMPANY,RESEARCH AND PRODUCTION ENTERPRISE UA (KYIV) partner 0.00
12    FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V. DE (MUNCHEN) partner 0.00
13    MedChemica Software Limited UK (Newcastle-Under-Lyme) partner 0.00
14    MOLECULAR NETWORKS GMBH COMPUTERCHEMIE DE (NURNBERG) partner 0.00
15    Next Move Software Limited UK (Cambridge) partner 0.00
16    STICHTING CENTRUM VOOR WISKUNDE EN INFORMATICA NL (AMSTERDAM) partner 0.00
17    TECHNISCHE UNIVERSITAET MUENCHEN DE (MUENCHEN) partner 0.00

Map

 Project objective

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.

 Deliverables

List of deliverables.
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.

 Publications

year authors and title journal last update
List of publications.
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 Rodríguez 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 Rodríguez-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 Rodríguez-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 Rodríguez-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 High‐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 Multi‐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 Rodríguez-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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