<i>De Novo</i> Design of Nurr1 Agonists <i>via</i> Fragment-Augmented Generative Deep Learning in Low-Data Regime
Marco Ballarotto, Sabine Willems, Tanja Stiller, Felix Nawa, Julian A. Marschner, Francesca Grisoni, Daniel Merk
Abstract
High Resolution Image Download MS PowerPoint Slide 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.