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, Gisbert Schneider
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...
April 22, 2024: Nature Communications