<i>De Novo</i> Design of Nurr1 Agonists <i>via</i> Fragment-Augmented Generative Deep Learning in Low-Data Regime-Reference-Cited by-同舟云学术

De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime

Published:2023-05-31 Issue:12 Volume:66 Page:8170-8177
ISSN:0022-2623
Container-title:Journal of Medicinal Chemistry
language:en
Short-container-title:J. Med. Chem.

Author:

Ballarotto Marco¹²^ORCID,Willems Sabine¹^ORCID,Stiller Tanja¹,Nawa Felix¹,Marschner Julian A.¹,Grisoni Francesca³⁴^ORCID,Merk Daniel¹^ORCID

Affiliation:

1. Department of Pharmacy, Ludwig-Maximilians-Universität (LMU) München, 81377 Munich, Germany

2. Department of Pharmaceutical Sciences, Università degli Studi di Perugia, 06123 Perugia, Italy

3. Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, 5612AZ Eindhoven, The Netherlands

4. Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, 3584CB Utrecht, The Netherlands

Funder

Erasmus+

H2020 European Research Council

Publisher

American Chemical Society (ACS)

Subject

Drug Discovery,Molecular Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.3c00485

Reference29 articles.

1. Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space

2. The rise of deep learning in drug discovery

3. Chemical language models for de novo drug design: Challenges and opportunities

4. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

5. Blaschke, T.; Olivecrona, M.; Engkvist, O.; Bajorath, J.; Chen, H. Application of Generative Autoencoder in de Novo Molecular Design. 2017, arXiv:1711.07839.

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