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

This paper demonstrates the use of hybrid chemical language models (CLMs) for de novo drug design, leveraging both molecular structure and bioactivity information. A generative CLM created a virtual library of PI3Kγ ligands, which was then refined using a CLM-based classifier trained on patented structures and PI3Kγ inhibitors. Several generated molecules were commercially available and tested, revealing a new sub-micromolar PI3Kγ ligand. Synthesis and testing of top-ranked molecules confirmed their potent activity, demonstrating the method's potential for scaffold hopping and hit-to-lead optimization. The most potent compounds inhibited PI3K-dependent Akt phosphorylation in a medulloblastoma cell model.

Michael Moret, Irene Pachon Angona, Leandro Cotos, Shen Yan, Kenneth Atz, Cyrill Brunner, Martin Baumgartner, Francesca Grisoni, Gisbert Schneider