Multi-objective molecular de novo design by adaptive fragment prioritization

Abstract

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure-activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.

Keywords: GPCR; computer-assisted drug design; machine learning; polypharmacology; reductive amination.