Prospective de novo drug design with deep interactome learning

Kenneth Atz, Leandro Cotos, Clemens Isert, Maria Håkansson, Dorota Focht, Mattis Hilleke, David F. Nippa, Michael Iff, Jann Ledergerber, Carl C. G. Schiebroek, Valentina Romeo, Jan A. Hiss, Daniel Merk, Petra Schneider, Bernd Kuhn, Uwe Grether and Gisbert Schneider ()
Additional contact information
Kenneth Atz: Department of Chemistry and Applied Biosciences
Leandro Cotos: Department of Chemistry and Applied Biosciences
Clemens Isert: Department of Chemistry and Applied Biosciences
Maria Håkansson: SARomics Biostructures AB
Dorota Focht: SARomics Biostructures AB
Mattis Hilleke: Department of Chemistry and Applied Biosciences
David F. Nippa: F. Hoffmann-La Roche Ltd.
Michael Iff: Department of Chemistry and Applied Biosciences
Jann Ledergerber: Department of Chemistry and Applied Biosciences
Carl C. G. Schiebroek: Department of Chemistry and Applied Biosciences
Valentina Romeo: F. Hoffmann-La Roche Ltd.
Jan A. Hiss: Department of Chemistry and Applied Biosciences
Daniel Merk: Ludwig-Maximilians-Universität München
Petra Schneider: Department of Chemistry and Applied Biosciences
Bernd Kuhn: F. Hoffmann-La Roche Ltd.
Uwe Grether: F. Hoffmann-La Roche Ltd.
Gisbert Schneider: Department of Chemistry and Applied Biosciences

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the “zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Date: 2024
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DOI: 10.1038/s41467-024-47613-w

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