Leveraging molecular structure and bioactivity with chemical language models for de novo drug design

Michael Moret, Irene Pachon Angona, Leandro Cotos, Shen Yan, Kenneth Atz, Cyrill Brunner, Martin Baumgartner, Francesca Grisoni () and Gisbert Schneider ()
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Michael Moret: ETH Zurich, Department of Chemistry and Applied Biosciences
Irene Pachon Angona: ETH Zurich, Department of Chemistry and Applied Biosciences
Leandro Cotos: ETH Zurich, Department of Chemistry and Applied Biosciences
Shen Yan: University of Zurich, University Children’s Hospital, Children’s Research Center, Pediatric Molecular Neuro-Oncology Research
Kenneth Atz: ETH Zurich, Department of Chemistry and Applied Biosciences
Cyrill Brunner: ETH Zurich, Department of Chemistry and Applied Biosciences
Martin Baumgartner: University of Zurich, University Children’s Hospital, Children’s Research Center, Pediatric Molecular Neuro-Oncology Research
Francesca Grisoni: ETH Zurich, Department of Chemistry and Applied Biosciences
Gisbert Schneider: ETH Zurich, Department of Chemistry and Applied Biosciences

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method’s scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model’s ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.

Date: 2023
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DOI: 10.1038/s41467-022-35692-6

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