2. Academic Validation
  3. De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime

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

  • J Med Chem. 2023 Jun 22;66(12):8170-8177. doi: 10.1021/acs.jmedchem.3c00485.
Marco Ballarotto 1 2,  Sabine Willems 1,  Tanja Stiller 1,  Felix Nawa 1,  Julian A Marschner 1,  Francesca Grisoni 3 4,  Daniel Merk 1
Affiliations

Affiliations

  • 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.
PMID: 37256819 DOI: 10.1021/acs.jmedchem.3c00485
Abstract

Generative neural networks trained on SMILES can design innovative bioactive molecules de novo. These so-called chemical language models (CLMs) have typically been trained on tens of template molecules for fine-tuning. However, it is challenging to apply CLM to orphan targets with few known ligands. We have fine-tuned a CLM with a single potent Nurr1 agonist as template in a fragment-augmented fashion and obtained novel Nurr1 agonists using sampling frequency for design prioritization. Nanomolar potency and binding affinity of the top-ranking design and its structural novelty compared to available Nurr1 ligands highlight its value as an early chemical tool and as a lead for Nurr1 agonist development, as well as the applicability of CLM in very low-data scenarios.

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