Big Data in Chemistry

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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."

Pubblicazioni

AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning

Autori: Samuel Genheden, Amol Thakkar, Veronika Chadimová, Jean-Louis Reymond, Ola Engkvist, Esben Bjerrum
Pubblicato in: Journal of Cheminformatics, Numero 12, 2020, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-020-00472-1

Highly Accurate Filters to Flag Frequent Hitters in AlphaScreen Assays by Suggesting their Mechanism

Autori: Dipan Ghosh, Uwe Koch, Kamyar Hadian, Michael Sattler, Igor V. Tetko
Pubblicato in: Molecular Informatics, Numero 41, 2023, ISSN 1868-1743
Editore: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.202100151

From Big Data to Artificial Intelligence: chemoinformatics meets new challenges

Autori: Igor V. Tetko, Ola Engkvist
Pubblicato in: Journal of Cheminformatics, Numero 12, 2020, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-020-00475-y

Automatic Extraction of Reaction Templates for Synthesis Prediction

Autori: Amol Thakkar, Jean-Louis Reymond
Pubblicato in: CHIMIA, Numero 76, 2022, Pagina/e 294, ISSN 0009-4293
Editore: Schweizerische Chemische Gedellschaft
DOI: 10.2533/chimia.2022.294

Memory-assisted reinforcement learning for diverse molecular de novo design

Autori: Thomas Blaschke, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Pubblicato in: Journal of Cheminformatics, Numero 12, 2020, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-020-00473-0

Artificial intelligence and automation in computer aided synthesis planning

Autori: Amol Thakkar, Simon Johansson, Kjell Jorner, David Buttar, Jean-Louis Reymond, Ola Engkvist
Pubblicato in: Reaction Chemistry & Engineering, Numero 6, 2024, Pagina/e 27-51, ISSN 2058-9883
Editore: Royal Society of Chemistry (RSC)
DOI: 10.1039/d0re00340a

Artificial applicability labels for improving policies in retrosynthesis prediction

Autori: Esben Jannik Bjerrum, Amol Thakkar, Ola Engkvist
Pubblicato in: Machine Learning: Science and Technology, Numero 2, 2023, Pagina/e 017001, ISSN 2632-2153
Editore: IOP Publishing
DOI: 10.1088/2632-2153/abcf90

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

Autori: Arkadii Lin, Igor I. Baskin, Gilles Marcou, Dragos Horvath, Bernd Beck, Alexandre Varnek
Pubblicato in: Molecular Informatics, Numero 39, 2023, ISSN 1868-1743
Editore: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.202000009

Exploring Simple Drug Scaffolds from the Generated Database Chemical Space Reveals a Chiral Bicyclic Azepane with Potent Neuropharmacology

Autori: Aline Carrel, Adonis Yiannakas, Jaap-Jan Roukens, Ines Reynoso-Moreno, Markus Orsi, Amol Thakkar, Josep Arus-Pous, Daniele Pellegata, Jürg Gertsch, Jean-Louis Reymond
Pubblicato in: Journal of Medicinal Chemistry, Numero 68, 2025, Pagina/e 9176-9201, ISSN 0022-2623
Editore: American Chemical Society
DOI: 10.1021/acs.jmedchem.4c02549

Fine-tuning of a generative neural network for designing multi-target compounds

Autori: Thomas Blaschke, Jürgen Bajorath
Pubblicato in: Journal of Computer-Aided Molecular Design, Numero 36, 2022, Pagina/e 363-371, ISSN 0920-654X
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10822-021-00392-8

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

Autori: Raquel Rodríguez-Pérez, Jürgen Bajorath
Pubblicato in: Journal of Computer-Aided Molecular Design, Numero 34, 2021, Pagina/e 1013-1026, ISSN 0920-654X
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10822-020-00314-0

Application of Generative Autoencoder in De Novo Molecular Design

Autori: Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Pubblicato in: Molecular Informatics, Numero 37/1-2, 2018, Pagina/e 1700123, ISSN 1868-1743
Editore: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.201700123

Virtual Exploration of the Ring Systems Chemical Universe

Autori: Ricardo Visini, Josep Arús-Pous, Mahendra Awale, Jean-Louis Reymond
Pubblicato in: Journal of Chemical Information and Modeling, Numero 57/11, 2017, Pagina/e 2707-2718, ISSN 1549-9596
Editore: American Chemical Society
DOI: 10.1021/acs.jcim.7b00457

The rise of deep learning in drug discovery

Autori: Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, Thomas Blaschke
Pubblicato in: Drug Discovery Today, Numero 23, 2018, Pagina/e 1241-1250, ISSN 1359-6446
Editore: 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

Autori: Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
Pubblicato in: ACS Omega, Numero 2/10, 2017, Pagina/e 6371-6379, ISSN 2470-1343
Editore: ACS Omega
DOI: 10.1021/acsomega.7b01079

Chemical Space: Big Data Challenge for Molecular Diversity

Autori: Mahendra Awale, Ricardo Visini, Daniel Probst, Josep Arús-Pous, Jean-Louis Reymond
Pubblicato in: CHIMIA International Journal for Chemistry, Numero 71/10, 2017, Pagina/e 661-666, ISSN 0009-4293
Editore: Schweizerische Chemische Gedellschaft
DOI: 10.2533/chimia.2017.661

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

Autori: Arkadii Lin, Dragos Horvath, Valentina Afonina, Gilles Marcou, Jean-Louis Reymond, Alexandre Varnek
Pubblicato in: ChemMedChem, Numero 13, 2017, Pagina/e -, ISSN 1860-7179
Editore: Wiley - V C H Verlag GmbbH & Co.
DOI: 10.1002/cmdc.201700561

Prediction of Compound Profiling Matrices Using Machine Learning

Autori: Raquel Rodríguez-Pérez, Tomoyuki Miyao, Swarit Jasial, Martin Vogt, Jürgen Bajorath
Pubblicato in: ACS Omega, Numero 3/4, 2018, Pagina/e 4713-4723, ISSN 2470-1343
Editore: ACS
DOI: 10.1021/acsomega.8b00462

Selection of protein conformations for structure-based polypharmacology studies

Autori: Luca Pinzi, Fabiana Caporuscio, Giulio Rastelli
Pubblicato in: Drug Discovery Today, Numero 23/11, 2018, Pagina/e 1889-1896, ISSN 1359-6446
Editore: Elsevier BV
DOI: 10.1016/j.drudis.2018.08.007

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

Autori: Oliver Laufkötter, Noé Sturm, Jürgen Bajorath, Hongming Chen, Ola Engkvist
Pubblicato in: Journal of Cheminformatics, Numero 11/1, 2019, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-019-0376-1

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

Autori: Dipan Ghosh, Uwe Koch, Kamyar Hadian, Michael Sattler, Igor V. Tetko
Pubblicato in: Journal of Chemical Information and Modeling, Numero 58/5, 2018, Pagina/e 933-942, ISSN 1549-9596
Editore: 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

Autori: Raquel Rodríguez-Pérez, Jürgen Bajorath
Pubblicato in: ACS Omega, Numero 3/9, 2018, Pagina/e 12033-12040, ISSN 2470-1343
Editore: ACS
DOI: 10.1021/acsomega.8b01682

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

Autori: Oliver Laufkötter, Tomoyuki Miyao, Jürgen Bajorath
Pubblicato in: ACS Omega, Numero 4/12, 2019, Pagina/e 15304-15311, ISSN 2470-1343
Editore: ACS
DOI: 10.1021/acsomega.9b02470

Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors

Autori: Raquel Rodríguez-Pérez, Jürgen Bajorath
Pubblicato in: ACS Omega, Numero 4/2, 2019, Pagina/e 4367-4375, ISSN 2470-1343
Editore: ACS
DOI: 10.1021/acsomega.9b00298

A Survey of Multi‐task Learning Methods in Chemoinformatics

Autori: Sergey Sosnin, Mariia Vashurina, Michael Withnall, Pavel Karpov, Maxim Fedorov, Igor V. Tetko
Pubblicato in: Molecular Informatics, Numero 38/4, 2019, Pagina/e 1800108, ISSN 1868-1743
Editore: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.201800108

Exploring the GDB-13 chemical space using deep generative models

Autori: Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Pubblicato in: Journal of Cheminformatics, Numero 11/1, 2019, Pagina/e 11:20, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-019-0341-z

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

Autori: Laurianne David, Jarrod Walsh, Noé Sturm, Isabella Feierberg, J. Willem M. Nissink, Hongming Chen, Jürgen Bajorath, Ola Engkvist
Pubblicato in: ChemMedChem, Numero 14/20, 2019, Pagina/e 1795-1802, ISSN 1860-7179
Editore: Wiley - V C H Verlag GmbbH & Co.
DOI: 10.1002/cmdc.201900395

Multi-task generative topographic mapping in virtual screening

Autori: Arkadii Lin, Dragos Horvath, Gilles Marcou, Bernd Beck, Alexandre Varnek
Pubblicato in: Journal of Computer-Aided Molecular Design, Numero 33/3, 2019, Pagina/e 331-343, ISSN 0920-654X
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10822-019-00188-x

“Ring Breaker”: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

Autori: Amol Thakkar, Nidhal Selmi, Jean-Louis Reymond, Ola Engkvist, Esben Jannik Bjerrum
Pubblicato in: Journal of Medicinal Chemistry, Numero 63, 2023, Pagina/e 8791-8808, ISSN 0022-2623
Editore: American Chemical Society
DOI: 10.1021/acs.jmedchem.9b01919

Diversifying chemical libraries with generative topographic mapping

Autori: Arkadii Lin, Bernd Beck, Dragos Horvath, Gilles Marcou, Alexandre Varnek
Pubblicato in: Journal of Computer-Aided Molecular Design, Numero Aug 12, 2019, Pagina/e -, ISSN 0920-654X
Editore: 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

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

Randomized SMILES strings improve the quality of molecular generative models

Autori: Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Pubblicato in: Journal of Cheminformatics, Numero 11/1, 2019, Pagina/e 3-13, ISSN 1758-2946
Editore: 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

Autori: Thomas Blaschke, Filip Miljković, Jürgen Bajorath
Pubblicato in: ACS Omega, Numero 4/4, 2019, Pagina/e 6883-6890, ISSN 2470-1343
Editore: ACS
DOI: 10.1021/acsomega.9b00492

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

Autori: Oleksii Prykhodko, Simon Viet Johansson, Panagiotis-Christos Kotsias, Josep Arús-Pous, Esben Jannik Bjerrum, Ola Engkvist, Hongming Chen
Pubblicato in: Journal of Cheminformatics, Numero 11/1, 2019, ISSN 1758-2946
Editore: 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

Autori: Raquel Rodríguez-Pérez, Jürgen Bajorath
Pubblicato in: Journal of Medicinal Chemistry, Numero September 12, 2019, 2019, Pagina/e NA, ISSN 0022-2623
Editore: American Chemical Society
DOI: 10.1021/acs.jmedchem.9b01101

Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks

Autori: Panagiotis-Christos Kotsias, Josep Arús-Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, Esben Jannik Bjerrum
Pubblicato in: Nature Machine Intelligence, Numero 2, 2022, Pagina/e 254-265, ISSN 2522-5839
Editore: Springer Science and Business Media LLC
DOI: 10.1038/s42256-020-0174-5

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

Autori: Thakkar, Amol; Kogej, Thierry; Reymond, Jean-Louis; Engkvist, Ola; Bjerrum, Esben Jannik
Pubblicato in: Chemical Science, Numero 3, 2020, Pagina/e 154–168, ISSN 2041-6539
Editore: Royal Society of Chemistry
DOI: 10.1039/c9sc04944d

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

Autori: M. Withnall, E. Lindelöf, O. Engkvist, H. Chen
Pubblicato in: Journal of Cheminformatics, Numero 12/1, 2020, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-019-0407-y

Automating drug discovery

Autori: Gisbert Schneider
Pubblicato in: Nature Reviews Drug Discovery, Numero 17/2, 2018, Pagina/e 97-113, ISSN 1474-1776
Editore: Nature Publishing Group
DOI: 10.1038/nrd.2017.232

SMILES-based deep generative scaffold decorator for de-novo drug design

Autori: Josep Arús-Pous, Atanas Patronov, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Pubblicato in: Journal of Cheminformatics, Numero 12, 2021, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-020-00441-8

Transformer-CNN: Swiss knife for QSAR modeling and interpretation

Autori: Pavel Karpov, Guillaume Godin, Igor V. Tetko
Pubblicato in: Journal of Cheminformatics, Numero 12/1, 2020, ISSN 1758-2946
Editore: 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

Autori: Raquel Rodríguez-Pérez; Filip Miljković; Jürgen Bajorath
Pubblicato in: Journal of Cheminfomatics, Numero 3, 2020, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.5281/zenodo.3759400

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

Autori: Lin, Arkadii; Baskin, Igor I.; Marcou, Gilles; Horvath, Dragos; Beck, Bernd; Varnek, Alexandre
Pubblicato in: Molecular Informatics, Numero 1, 2020, ISSN 1868-1743
Editore: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.5281/zenodo.3757811

Activity landscape image analysis using convolutional neural networks

Autori: Javed Iqbal; Martin Vogt; Jürgen Bajorath
Pubblicato in: Journal of Cheminformatics, Numero 2, 2020, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.5281/zenodo.3759410

REINVENT 2.0: An AI Tool for De Novo Drug Design

Autori: Thomas Blaschke, Josep Arús-Pous, Hongming Chen, Christian Margreitter, Christian Tyrchan, Ola Engkvist, Kostas Papadopoulos, Atanas Patronov
Pubblicato in: Journal of Chemical Information and Modeling, Numero 60, 2023, Pagina/e 5918-5922, ISSN 1549-9596
Editore: American Chemical Society
DOI: 10.1021/acs.jcim.0c00915

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Autori: Igor V. Tetko, Ola Engkvist, Uwe Koch, Jean-Louis Reymond, Hongming Chen
Pubblicato in: Molecular Informatics, Numero 35/11-12, 2016, Pagina/e 615-621, ISSN 1868-1743
Editore: Wiley - VCH Verlag GmbH & CO. KGaA
DOI: 10.1002/minf.201600073

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

Autori: Eric March-Vila, Luca Pinzi, Noé Sturm, Annachiara Tinivella, Ola Engkvist, Hongming Chen, Giulio Rastelli
Pubblicato in: Frontiers in Pharmacology, Numero 8, 2017, ISSN 1663-9812
Editore: 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?

Autori: Igor V Tetko, Ola Engkvist, Hongming Chen
Pubblicato in: Future Medicinal Chemistry, Numero 8/15, 2016, Pagina/e 1801-1806, ISSN 1756-8919
Editore: 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

Autori: Raquel Rodríguez-Pérez, Martin Vogt, Jürgen Bajorath
Pubblicato in: Journal of Chemical Information and Modeling, Numero 57/4, 2017, Pagina/e 710-716, ISSN 1549-9596
Editore: American Chemical Society
DOI: 10.1021/acs.jcim.7b00088

Molecular de-novo design through deep reinforcement learning

Autori: Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, Hongming Chen
Pubblicato in: Journal of Cheminformatics, Numero 9/1, 2017, Pagina/e 48-59, ISSN 1758-2946
Editore: Chemistry Central
DOI: 10.1186/s13321-017-0235-x

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

Autori: Michael Withnall, Hongming Chen, Igor V. Tetko
Pubblicato in: ChemMedChem, Numero 13, 2017, Pagina/e 599-606, ISSN 1860-7179
Editore: Wiley - V C H Verlag GmbbH & Co.
DOI: 10.1002/cmdc.201700303

Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning

Autori: Amol Thakkar, Veronika Chadimová, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond
Pubblicato in: Chemical Science, Numero 12, 2024, Pagina/e 3339-3349, ISSN 2041-6520
Editore: Royal Society of Chemistry
DOI: 10.1039/d0sc05401a

State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

Autori: Igor V. Tetko, Pavel Karpov, Ruud Van Deursen, Guillaume Godin
Pubblicato in: Nature Communications, Numero 11, 2022, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-020-19266-y

Modelling false positives in high throughput assays

Autori: Dipan Ghosh
Pubblicato in: Doctoral thesis, 2021
Editore: TUM

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

Autori: Arkadii Lin
Pubblicato in: PhD thesis, 2019
Editore: University of Strasbourg

Machine Learning Methodologies for Interpretable Compound Activity Predictions

Autori: Raquel Rodríguez Pérez
Pubblicato in: PhD thesis, 2020
Editore: Mathematisch-Naturwissenschaftliche Fakultät, University of Bonn

Exploration of synthetically accessible chemical space by de novo design

Autori: Xuejin Zhang
Pubblicato in: PhD thesis, 2019
Editore: ETHZ

Computer Aided Synthesis Prediction to Enable Augmented Chemical Discovery and Chemical Space Exploration

Autori: Amol Vijay Thakkar
Pubblicato in: PhD thesis, 2022
Editore: University of Bern

Analysis and Modelling of False Positives in GPCR Assays

Autori: Dipan Ghosh, Igor Tetko, Bert Klebl, Peter Nussbaumer, Uwe Koch
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 764-770, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_71

Neural Network Guided Tree-Search Policies for Synthesis Planning

Autori: Amol Thakkar, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 721-724, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_64

Augmentation Is What You Need!

Autori: Igor V. Tetko, Pavel Karpov, Eric Bruno, Talia B. Kimber, Guillaume Godin
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 831-835, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_79

Improving Deep Generative Models with Randomized SMILES

Autori: Josep Arús-Pous, Simon Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 747-751, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_68

A Transformer Model for Retrosynthesis

Autori: Pavel Karpov, Guillaume Godin, Igor V. Tetko
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 817-830, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_78

Attention and Edge Memory Convolution for Bioactivity Prediction

Autori: Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 752-757, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-30493-5_69

Chapter 13. Molecular <i>De Novo</i> Design Through Deep Generative Models

Autori: Ola Engkvist, Josep Arús-Pous, Esben Jannik Bjerrum, Hongming Chen
Pubblicato in: Drug Discovery, Artificial Intelligence in Drug Discovery, 2024, Pagina/e 272-300, ISBN 1788-015479
Editore: Royal Society of Chemistry
DOI: 10.1039/9781788016841-00272

Diversify Libraries Using Generative Topographic Mapping

Autori: Lin, Arkadii; Beck, Bernd; Horvath, Dragos; Varnek, Alexandre
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 839-341, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.5281/zenodo.3515029

Detection of Frequent-Hitters Across Various HTS Technologies

Autori: David, Laurianne; Walsh, Jarrod; Bajorath, Jürgen; Engkvist, Ola
Pubblicato in: 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, Numero 11731, 2019, Pagina/e 842-844, ISBN 978-3-030-30492-8
Editore: Springer International Publishing
DOI: 10.5281/zenodo.3515025

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