Big Data in Chemistry

Rezultaty

Open lectures to students of high schools/gymnasia

The lectures to students of higher schools and gymnasium will be provided to disseminate information about scientific topics and stimulate interest of future students in the scientific studies.

Publication of newsletter

The newsletter that will overview project activities to a public audience will be prepared and sent to the related societies

Organisation of Open Days

The organization of Open Days to promote MC EID beyond the scientific community will be provided.

Web site and application system for fellows

The web site and web application system to accept application of fellows is established and ready to accept applications.

Minutes of the kick-off meeting

This deliverable will be report of the results of the kick-off meeting.

3rd Winter school report

"Report about results of ""Virtual and HTS screening"" school organized by LDC and Boehringer Ingelheim Pharma GmbH & Co. KG will be provided."

Comparison of performances of different data sharing approaches

Report will assess the performance of different data sharing strategies.

2nd Winter school report

"The report of the third school ""Computer-Assisted Drug Discovery"" which will be organized by University of Modena."

Review of the developed protocols and their performances on public and in house data

Review of the performance of ligand- and structure-based approaches for drug design and discovery will be provided.

1st Summer school report

"The report about the results of the second School ""Chemical Data Resources"" organized by Uni Bern and Uni Zurich will be provided."

Benchmarking of developed machine learning approaches

The overview of benchmarking of methods for data visualization and modelling based on GTM approach will be provided.

Report of the final closing conference

Report will summarize the results of the final closing conference of the project.

Analysis of target similarity of chemical compounds

Analysis of compound promiscuity and selectivity patterns will be provided.

2nd Summer school report

"Report of ""Chemical Space and ADMETox profiling"" school organized by HMGU and AZ will be provided."

Overview of strategies for data sharing

The report will summarize the strategies for secure sharing of data that will be developed and validated during the project.

Overview of HTS data

The report will summarize HTS data that will be available for development of frequent hitter filters.

Preparation of CDPs

CDP will be prepared for each employed fellow and provided to REA.

Final report of the project and an overview of the awarded PhDs

Report will summarize achievement of the project, overview the awarded PhDs and the scientific output.

Analysis of frequent hitters for screening technologies

We will report about the identified frequent hitters developed for different screening technologies.

1st Winter school report

"The report of the first School ""Introduction to chemoinformatics"" organized by the Uni Bonn will be provided."

Publikacje

Application of Generative Autoencoder in De Novo Molecular Design

Autorzy: Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Opublikowane w: Molecular Informatics, Numer 37/1-2, 2018, Strona(/y) 1700123, ISSN 1868-1743
Wydawca: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.201700123

Virtual Exploration of the Ring Systems Chemical Universe

Autorzy: Ricardo Visini, Josep Arús-Pous, Mahendra Awale, Jean-Louis Reymond
Opublikowane w: Journal of Chemical Information and Modeling, Numer 57/11, 2017, Strona(/y) 2707-2718, ISSN 1549-9596
Wydawca: American Chemical Society
DOI: 10.1021/acs.jcim.7b00457

The rise of deep learning in drug discovery

Autorzy: Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke
Opublikowane w: Drug Discovery Today, Numer 23, 2018, Strona(/y) 1241-1250, ISSN 1359-6446
Wydawca: Elsevier BV
DOI: 10.1016/j.drudis.2018.01.039

Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction

Autorzy: Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
Opublikowane w: ACS Omega, Numer 2/10, 2017, Strona(/y) 6371-6379, ISSN 2470-1343
Wydawca: ACS Omega
DOI: 10.1021/acsomega.7b01079

Chemical Space: Big Data Challenge for Molecular Diversity

Autorzy: Mahendra Awale, Ricardo Visini, Daniel Probst, Josep Arús-Pous, Jean-Louis Reymond
Opublikowane w: CHIMIA International Journal for Chemistry, Numer 71/10, 2017, Strona(/y) 661-666, ISSN 0009-4293
Wydawca: Schweizerische Chemische Gedellschaft
DOI: 10.2533/chimia.2017.661

Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds

Autorzy: Arkadii Lin, Dragos Horvath, Valentina Afonina, Gilles Marcou, Jean-Louis Reymond, Alexandre Varnek
Opublikowane w: ChemMedChem, Numer 13, 2017, Strona(/y) -, ISSN 1860-7179
Wydawca: Wiley - V C H Verlag GmbbH & Co.
DOI: 10.1002/cmdc.201700561

Prediction of Compound Profiling Matrices Using Machine Learning

Autorzy: Raquel Rodríguez-Pérez, Tomoyuki Miyao, Swarit Jasial, Martin Vogt, Jürgen Bajorath
Opublikowane w: ACS Omega, Numer 3/4, 2018, Strona(/y) 4713-4723, ISSN 2470-1343
Wydawca: ACS
DOI: 10.1021/acsomega.8b00462

Selection of protein conformations for structure-based polypharmacology studies

Autorzy: Luca Pinzi, Fabiana Caporuscio, Giulio Rastelli
Opublikowane w: Drug Discovery Today, Numer 23/11, 2018, Strona(/y) 1889-1896, ISSN 1359-6446
Wydawca: Elsevier BV
DOI: 10.1016/j.drudis.2018.08.007

Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability

Autorzy: Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist
Opublikowane w: Journal of Cheminformatics, Numer 11/1, 2019, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-019-0376-1

Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays

Autorzy: Dipan Ghosh, Uwe Koch, Kamyar Hadian, Michael Sattler, Igor V. Tetko
Opublikowane w: Journal of Chemical Information and Modeling, Numer 58/5, 2018, Strona(/y) 933-942, ISSN 1549-9596
Wydawca: American Chemical Society
DOI: 10.1021/acs.jcim.7b00574

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

Autorzy: Raquel Rodríguez-Pérez, Jürgen Bajorath
Opublikowane w: ACS Omega, Numer 3/9, 2018, Strona(/y) 12033-12040, ISSN 2470-1343
Wydawca: ACS
DOI: 10.1021/acsomega.8b01682

Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents

Autorzy: Oliver Laufkötter, Tomoyuki Miyao, Jürgen Bajorath
Opublikowane w: ACS Omega, Numer 4/12, 2019, Strona(/y) 15304-15311, ISSN 2470-1343
Wydawca: ACS
DOI: 10.1021/acsomega.9b02470

Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors

Autorzy: Raquel Rodríguez-Pérez, Jürgen Bajorath
Opublikowane w: ACS Omega, Numer 4/2, 2019, Strona(/y) 4367-4375, ISSN 2470-1343
Wydawca: ACS
DOI: 10.1021/acsomega.9b00298

A Survey of Multi‐task Learning Methods in Chemoinformatics

Autorzy: Sergey Sosnin, Mariia Vashurina, Michael Withnall, Pavel Karpov, Maxim Fedorov, Igor V. Tetko
Opublikowane w: Molecular Informatics, Numer 38/4, 2019, Strona(/y) 1800108, ISSN 1868-1743
Wydawca: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.201800108

Exploring the GDB-13 chemical space using deep generative models

Autorzy: Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Opublikowane w: Journal of Cheminformatics, Numer 11/1, 2019, Strona(/y) 11:20, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-019-0341-z

Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies

Autorzy: Laurianne David, Jarrod Walsh, Noé Sturm, Isabella Feierberg, J. Willem M. Nissink, Hongming Chen, Jürgen Bajorath, Ola Engkvist
Opublikowane w: ChemMedChem, Numer 14/20, 2019, Strona(/y) 1795-1802, ISSN 1860-7179
Wydawca: Wiley - V C H Verlag GmbbH & Co.
DOI: 10.1002/cmdc.201900395

Multi-task generative topographic mapping in virtual screening

Autorzy: Arkadii Lin, Dragos Horvath, Gilles Marcou, Bernd Beck, Alexandre Varnek
Opublikowane w: Journal of Computer-Aided Molecular Design, Numer 33/3, 2019, Strona(/y) 331-343, ISSN 0920-654X
Wydawca: Kluwer Academic Publishers
DOI: 10.1007/s10822-019-00188-x

Diversifying chemical libraries with generative topographic mapping

Autorzy: Arkadii Lin, Bernd Beck, Dragos Horvath, Gilles Marcou, Alexandre Varnek
Opublikowane w: Journal of Computer-Aided Molecular Design, Numer Aug 12, 2019, Strona(/y) -, ISSN 0920-654X
Wydawca: Kluwer Academic Publishers
DOI: 10.1007/s10822-019-00215-x

Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research

Autorzy: Laurianne David, Josep Arús-Pous, Johan Karlsson, Ola Engkvist, Esben Jannik Bjerrum, Thierry Kogej, Jan M. Kriegl, Bernd Beck, Hongming Chen
Opublikowane w: Frontiers in Pharmacology, Numer 10, 2019, ISSN 1663-9812
Wydawca: Frontiers Media S.A.
DOI: 10.3389/fphar.2019.01303

Randomized SMILES strings improve the quality of molecular generative models

Autorzy: Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Opublikowane w: Journal of Cheminformatics, Numer 11/1, 2019, Strona(/y) 3-13, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-019-0393-0

Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis

Autorzy: Thomas Blaschke, Filip Miljković, Jürgen Bajorath
Opublikowane w: ACS Omega, Numer 4/4, 2019, Strona(/y) 6883-6890, ISSN 2470-1343
Wydawca: ACS
DOI: 10.1021/acsomega.9b00492

A de novo molecular generation method using latent vector based generative adversarial network

Autorzy: Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, Hongming Chen
Opublikowane w: Journal of Cheminformatics, Numer 11/1, 2019, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-019-0397-9

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

Autorzy: Raquel Rodríguez-Pérez, Jürgen Bajorath
Opublikowane w: Journal of Medicinal Chemistry, Numer September 12, 2019, 2019, Strona(/y) NA, ISSN 0022-2623
Wydawca: American Chemical Society
DOI: 10.1021/acs.jmedchem.9b01101

Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain

Autorzy: Thakkar, Amol; Kogej, Thierry; Reymond, Jean-Louis; Engkvist, Ola; Bjerrum, Esben Jannik
Opublikowane w: Chemical Science, Numer 3, 2020, Strona(/y) 154–168, ISSN 2041-6539
Wydawca: Royal Society of Chemistry
DOI: 10.1039/c9sc04944d

Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

Autorzy: M. Withnall, E. Lindelöf, O. Engkvist, H. Chen
Opublikowane w: Journal of Cheminformatics, Numer 12/1, 2020, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-019-0407-y

Automating drug discovery

Autorzy: Gisbert Schneider
Opublikowane w: Nature Reviews Drug Discovery, Numer 17/2, 2018, Strona(/y) 97-113, ISSN 1474-1776
Wydawca: Nature Publishing Group
DOI: 10.1038/nrd.2017.232

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

Autorzy: Pavel Karpov, Guillaume Godin, Igor V. Tetko
Opublikowane w: Journal of Cheminformatics, Numer 12/1, 2020, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-020-00423-w

Assessing the information content of structural and protein-ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning

Autorzy: Raquel Rodríguez-Pérez; Filip Miljković; Jürgen Bajorath
Opublikowane w: Journal of Cheminfomatics, Numer 3, 2020, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.5281/zenodo.3759400

Parallel Generative Topographic Mapping: an Efficient Approach for Big Data Handling

Autorzy: Lin, Arkadii; Baskin, Igor I.; Marcou, Gilles; Horvath, Dragos; Beck, Bernd; Varnek, Alexandre
Opublikowane w: Molecular Informatics, Numer 1, 2020, ISSN 1868-1743
Wydawca: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.5281/zenodo.3757811

Activity landscape image analysis using convolutional neural networks

Autorzy: Javed Iqbal; Martin Vogt; Jürgen Bajorath
Opublikowane w: Journal of Cheminformatics, Numer 2, 2020, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.5281/zenodo.3759410

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Autorzy: Igor V. Tetko, Ola Engkvist, Uwe Koch, Jean-Louis Reymond, Hongming Chen
Opublikowane w: Molecular Informatics, Numer 35/11-12, 2016, Strona(/y) 615-621, ISSN 1868-1743
Wydawca: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.201600073

On the Integration of In Silico Drug Design Methods for Drug Repurposing

Autorzy: Eric March-Vila, Luca Pinzi, Noé Sturm, Annachiara Tinivella, Ola Engkvist, Hongming Chen, Giulio Rastelli
Opublikowane w: Frontiers in Pharmacology, Numer 8, 2017, ISSN 1663-9812
Wydawca: Frontiers Media S.A.
DOI: 10.3389/fphar.2017.00298

Does ‘Big Data’ exist in medicinal chemistry, and if so, how can it be harnessed?

Autorzy: Igor V Tetko, Ola Engkvist, Hongming Chen
Opublikowane w: Future Medicinal Chemistry, Numer 8/15, 2016, Strona(/y) 1801-1806, ISSN 1756-8919
Wydawca: Future Science Ltd.
DOI: 10.4155/fmc-2016-0163

Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds

Autorzy: Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
Opublikowane w: Journal of Chemical Information and Modeling, Numer 57/4, 2017, Strona(/y) 710-716, ISSN 1549-9596
Wydawca: American Chemical Society
DOI: 10.1021/acs.jcim.7b00088

Molecular de-novo design through deep reinforcement learning

Autorzy: Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen
Opublikowane w: Journal of Cheminformatics, Numer 9/1, 2017, Strona(/y) 48-59, ISSN 1758-2946
Wydawca: Chemistry Central
DOI: 10.1186/s13321-017-0235-x

Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective

Autorzy: Michael Withnall, Hongming Chen, Igor V. Tetko
Opublikowane w: ChemMedChem, Numer 13, 2017, Strona(/y) 599-606, ISSN 1860-7179
Wydawca: Wiley - V C H Verlag GmbbH & Co.
DOI: 10.1002/cmdc.201700303

Analysis and Modelling of False Positives in GPCR Assays

Autorzy: Dipan Ghosh, Igor Tetko, Bert Klebl, Peter Nussbaumer, Uwe Koch
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 764-770, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_71

Neural Network Guided Tree-Search Policies for Synthesis Planning

Autorzy: Amol Thakkar, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 721-724, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_64

Augmentation Is What You Need!

Autorzy: Igor V. Tetko, Pavel Karpov, Eric Bruno, Talia B. Kimber, Guillaume Godin
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 831-835, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_79

Improving Deep Generative Models with Randomized SMILES

Autorzy: Josep Arús-Pous, Simon Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 747-751, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_68

A Transformer Model for Retrosynthesis

Autorzy: Pavel Karpov, Guillaume Godin, Igor V. Tetko
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 817-830, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_78

Attention and Edge Memory Convolution for Bioactivity Prediction

Autorzy: Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 752-757, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_69

Molecular de novo Design Through Deep Generative Models

Autorzy: Engkvist, Ola; Arús-Pous, Josep; Bjerrum, Esben Jannik; Chen, Hongming
Opublikowane w: Artificial Intelligence in Drug Discovery, Numer 1, 2020, Strona(/y) -, ISBN 1788-015479
Wydawca: Royal Society of Chemistry
DOI: 10.5281/zenodo.3628194

Diversify Libraries Using Generative Topographic Mapping

Autorzy: Lin, Arkadii; Beck, Bernd; Horvath, Dragos; Varnek, Alexandre
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 839-341, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.5281/zenodo.3515029

Detection of Frequent-Hitters Across Various HTS Technologies

Autorzy: David, Laurianne; Walsh, Jarrod; Bajorath, Jürgen; Engkvist, Ola
Opublikowane w: Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Numer 11731, 2019, Strona(/y) 842-844, ISBN 978-3-030-30492-8
Wydawca: Springer International Publishing
DOI: 10.5281/zenodo.3515025

'Ring Breaker': Assessing Synthetic Accessibility of the Ring System Chemical Space

Autorzy: Thakkar, Amol; Selmi, Nidhai; Reymond, Jean-Louis; Engkvist, Ola; Bjerrum, Esben Jannik
Opublikowane w: ChemRxiv, Numer 11, 2019, Strona(/y) -
Wydawca: American Chemical Society
DOI: 10.26434/chemrxiv.9938969.v1

Direct Steering of de novo Molecular Generation using Descriptor Conditional Recurrent Neural Networks (cRNNs)

Autorzy: Kotsias, Panagiotis-Christos; Arús-Pous, Josep; Chen, Hongming; Engkvist, Ola; Tyrchan, Christian; Bjerrum, Esben Jannik
Opublikowane w: Nature Machine Intelligence, Numer 126, 2019
Wydawca: American Chemical Society
DOI: 10.26434/chemrxiv.9860906.v2

SMILES-Based Deep Generative Scaffold Decorator for De-Novo Drug Design

Autorzy: Josep Arús-Pous Atanas Patronov Esben Jannik Bjerrum Christian Tyrchan Jean-Louis Reymond Hongming Chen Ola Engkvist
Opublikowane w: ChemRxiv, Numer 1, 2020, Strona(/y) 1, ISSN 2573-2293
Wydawca: American Chemical Society
DOI: 10.26434/chemrxiv.11638383.v1

REINVENT 2.0 – an AI tool for de novo drug design

Autorzy: Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, Atanas Patronov
Opublikowane w: ChemRxiv, Numer 1, 2020, Strona(/y) 1, ISSN 2573-2293
Wydawca: ACS
DOI: 10.26434/chemrxiv.12058026.v2

Cartographie Topographique Générative: un outil puissant pour la visualisation, l'analyse et la modélisation de données chimiques volumineuses

Autorzy: Arkadii Lin
Opublikowane w: PhD thesis, 2019
Wydawca: University of Strasbourg

Machine Learning Methodologies for Interpretable Compound Activity Predictions

Autorzy: Raquel Rodríguez Pérez
Opublikowane w: PhD thesis, 2020
Wydawca: Mathematisch-Naturwissenschaftliche Fakultät, University of Bonn

Exploration of synthetically accessible chemical space by de novo design

Autorzy: Xuejin Zhang
Opublikowane w: PhD thesis, 2019
Wydawca: ETHZ

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