De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime.
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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… read more here.
Keywords: nurr1 agonists; low data; novo design; fragment augmented ... See more keywords