Combining de novo molecular design with semiempirical protein–ligand binding free energy calculation
Michael Iff, Kenneth Atz, Clemens Isert, Irène Pachón-Angona, Leandro Cotos, Mattis Hilleke, Jan A. Hiss, Gisbert Schneider · RSC Advances · 2024
design with SMILES and SELFIES representations, respectively. These libraries were computationally evaluated for synthesizability, novelty, and predicted biological activity. The candidate molecules were subjected to molecular docking to identify hypothetical binding poses, which were further refined using Gibbs free energy calculations. The structurally novel top-ranked molecule was chemically synthesized and biologically tested, demonstrating moderate micromolar activity against AChE. Our findings highlight the potential and certain limitations of integrating deep learning-based molecular generation with semi-empirical quantum chemistry-based activity prediction for structure-based drug design.