Irreversible acetylcholinesterase inhibitors (IAIs) are a class of acutely cardinal biomolecules, with numerous political and industrial applications ranging from to biocidal deployment to infirmity ministration. However, multitudinous avenues of resistance against such compounds have been contrived, leading to a global necessitation for novel IAIs with augmented activity. The employment of artificial intelligence has also been evinced to be a highly efficacious tool for the de novo design of pharmaceutical compounds, enabling large compound libraries to be rapidly generated and screened. Hence, in this paper we report the effectual utilisation of AI based resources to design and screen large libraries of IAIs (>105.candidate conformers) to evaluate their inhibitory propensity and overall toxicity. Seed compound structures were employed to generate correlative compounds via an in silico bioisosteric replacement system (MolOpt) which utilises an AI generative model to elucidate candidate compounds. The compound library was then screened for binding affinity with human acetylcholinesterase using the screening webserver Pharmit to ascertain their effectiveness as irreversible acetylcholinesterase inhibitors. Finally, the overall acute toxicity of the candidate compounds was screened with the platform AD-METlab 2.0, which accorded the relative toxicity factors of each candidate. Analysis of the candidate compounds’ properties indicated that five compounds were prognosticated that possessed a higher acetylcholinesterase binding affinity and overall acute toxicity than all existing IAIs, which alludes to a higher potency.