Fig. 5: Features of dual ligands designed by the chemical language models (CLM). | Nature Communications

Fig. 5: Features of dual ligands designed by the chemical language models (CLM).

From: Automated design of multi-target ligands by generative deep learning

Fig. 5

ac Predicted binding modes of the most active dual ligands 1, 6 and 7. (FXR: protein data bank (pdb) ID 6a6053, sEH: pdb ID 5ali52, THRβ: pdb ID 1nax54, PPARδ: pdb ID 5y7x). d Structural and pharmacophore comparison of the dual FXR/THRβ ligand 6 and the dual PPAR/sEH modulator 9 with the most similar ligands of the targets of interest annotated in ChEMBL (pretraining set). Similarity refers to Tanimoto similarity computed on Morgan fingerprints34. Common substructures in the designs and most similar ligands are highlighted (FXR—red, THRβ—blue, violet—both and yellow—sEH, green—PPARδ, orange—both). e Violin plots of quantitative estimation of drug-likeness (QED)38 and synthetic accessibility scores of the CLM designs were favorable and resembled the fine-tuning molecules. Stars represent the fine-tuning molecules and designed dual ligands, respectively, the lines represent the 1st and 3rd quartiles and the mean. Numbers of the ligands/designs for PPARδ: 5, sEH: 6, PPARδ/sEH: 12. f Violin plots of basic chemical features of the CLM designs resembling the fine-tuning molecules. Stars represent the fine-tuning molecules and designed dual ligands, respectively, the lines represent the 1st and 3rd quartiles and the mean. Numbers of the ligands/designs for PPARδ: 5, sEH: 6, PPARδ/sEH: 12. Source data are provided as a Source Data file.

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