An AI-Driven Framework for Discovery of BACE1 Inhibitors for Alzheimer’s Disease

Abstract

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects over 51 million individuals globally. The β-secretase (BACE1) enzyme is responsible for the production of amyloid beta (Aβ) plaques in the brain. The accumulation of Aβ plaques leads to neuronal death and the impairment of cognitive abilities, both of which are fundamental symptoms of AD. Thus, BACE1 has emerged as a promising therapeutic target for AD. Previous BACE1 inhibitors have faced various issues related to molecular size and blood-brain barrier permeability, preventing any of them from maturing into FDA-approved AD drugs. In this work, a generative AI framework is developed as the first AI application to the de novo generation of BACE1 inhibitors. Through a simple, robust, and accurate molecular representation, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), and a Genetic Algorithm (GA), the framework generates and optimizes over 1,000,000 candidate inhibitors that improve upon the bioactive and pharmacological properties of current BACE1 inhibitors. Then, the molecular docking simulation models the candidate inhibitors and identifies 14 candidate drugs that exhibit stronger binding interactions to the BACE1 active site than previous candidate BACE1 drugs from clinical trials. Overall, the framework successfully discovers BACE1 inhibitors and candidate AD drugs, accelerating the developmental process for a novel AD treatment.