Automated design of ligands to polypharmacological profiles

Abstract

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand–target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology. An automated approach designing drug ligands to multi-target profiles (with a 75% prediction success rate) is experimentally validated by the invention of novel ligands tailored to the complex and physiologically-relevant goal of identifying drugs that can specifically target profiles of multiple proteins. This paper describes a new approach to the problem of designing drugs that interact with multiple targets, which may be desirable either to achieve exquisite selectivity over other drug targets, or to obtain a drug with a particular polypharmacological profile. The authors have developed an automated, adaptive design approach to the generation of analogues and prioritizing them against a set of objectives. They tested experimentally 800 ligand–target predictions of prospectively designed ligands; 75% were confirmed correct, and the predicted target engagement was confirmed in vivo.