Discovery of novel acetylcholinesterase inhibitors through AI-powered structure prediction and high-performance computing-enhanced virtual screening

Abstract

Virtual screening (VS) methodologies have become key in the drug discovery process but are also applicable to other fields including catalysis, material design, and, more recently, insecticide solutions. Indeed, the search for effective pest control agents is a critical industrial objective, driven by the need to meet stringent regulations and address public health concerns. Cockroaches, known vectors of numerous diseases, represent a major challenge due to the toxicity of existing control measures to humans. In this article, we leverage an Artificial Intelligence (AI)-based screening of the Drug Bank (DB) database to identify novel acetylcholinesterase (AChE) inhibitors, a previously uncharacterized target in the American cockroach (Periplaneta americana). Our AI-based VS pipeline starts with the deep-learning-based AlphaFold to predict the previously unknown 3D structure of AChE based on its amino acid sequence. This first step enables the subsequent ligand–receptor VS of potential inhibitors, the development of which is performed using a consensus VS protocol based on two different tools: Glide, an industry-leading solution, and METADOCK 2, a metaheuristic-based tool that takes advantage of GPU acceleration. The proposed VS pipeline is further refined through rescoring to pinpoint the most promising biocide compounds against cockroaches. We show the search space explored by different metaheuristics generated by METADOCK 2 and how this search is more exhaustive, but complementary, than the one offered by Glide. Finally, we applied Molecular Mechanics Generalized Born Surface Area (MMGBSA) to list the most promising compounds to inhibit the AChE enzyme.