The intersection of next-generation artificial intelligence and organic chemistry is vital to the discovery of novel antimicrobials in combatting evolving populations of antimicrobial-resistant strains of bacteria. In this study, artificial intelligence was pursued in two phases, a learning phase and a generation phase, to analyze quaternary ammonium compounds (QACs) for biological efficacy and ultimately predict new structures. A dataset of over 700 published quaternary ammonium compounds were processed in ChemDraw® from their skeletal structure to a Simplified Molecular-Input Line-Entry System (SMILES) string. The SMILES string was inputted to CorrVAE, a deep generative model, which correlated properties of bioactivity data, such as chain length, molecular size, and overall charge to the organic structure. As a result, the antimicrobial ability of QACs can be predicted before synthesis and biological testing.

Incorporation of machine learning strategies in the development of quaternary ammonium compounds

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