MTDL-GAN: De novo Design of Multi-Target Directed Ligands for Alzheimer’s Disease from Unpaired Sets of Target-Focused Chemical Library

Alzheimer’s disease (AD), the most common form of dementia, causes memory loss, cognitive decline, and behavioural changes, affecting over 32 million people globally. Current AD treatments that focus on single-target intervention often fail to significantly slow disease progression and may not be effective for all patients. Given AD’s complex nature, a more effective approach may involve targeting multiple pathways simultaneously. This study proposes the use of a cycle-consistent adversarial network to design multi-target directed ligands (MTDL-GAN), drugs designed to inhibit two primary AD target enzymes simultaneously. Our targets of interest are acetylcholinesterase (AChE), beta-secretase 1 (BACE1), and glycogen synthase kinase 3 beta (GSK3), which are known to have significant impacts on the progression and development of AD, each with a different mechanism of action. Inhibitor libraries were curated from ChEMBL27 and characterized to represent each inhibitor domain, resulting in 69 AChE, 572 BACE1, and 246 GSK3 inhibitors. The MTDL-GAN was trained on these unpaired datasets to generate molecules with dual-target properties. Domain similarity metrics and molecular docking simulations were employed to validate the generated molecules’ structural and binding properties. The proposed method successfully transformed molecules from the original inhibitor domains to the target MTDL domain while preserving structural similarity to the original datasets. The generated molecules demonstrated promising in silico dual-binding affinity, with favourable physicochemical properties and synthetic tractability. Notably, several molecules surpassed the binding scores of investigational drugs in phase 2/3 clinical trials. These hit MTDL molecules are publicly available for medicinal chemists’ research.