The prevalence of quaternary ammonium compounds (QACs) as common disinfecting agents for the past century has led bacteria to develop resistance to such compounds. Given the alarming increase in resistant strains, new strategies are required to combat this rise in resistance. Recent efforts to probe and combat bacterial resistance have focused on studies of multiQACs. Relatively unexplored, however, have been changes to the primary atom bearing positive charge in these antiseptics. Here we review the current state of the field of both phosphonium and sulfonium amphiphilic antiseptics, both of which hold promise as novel means to address bacterial resistance.

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Thirty‐six biscationic quaternary ammonium compounds were efficiently synthesized in one step to examine the effect of molecular geometry of two‐carbon linkers on antimicrobial activity. The synthesized compounds showed strong antimicrobial activity against a panel of both Gram‐positive and Gram‐negative bacteria, including methicillin‐resistant Staphylococcus aureus (MRSA). While the linker geometry showed only a modest correlation with antimicrobial activity, several of the synthesized bisQACs are promising potential antiseptics due to good antimicrobial activity (MIC≤2 μM) and their higher therapeutic indices compared to previously reported QACs.

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advancement of deep generative models is limited by challenges to generate objects that possess multiple desired properties: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under various manners simultaneously is hard and under-explored. We address these challenges by proposing a novel deep generative framework, CorrVAE, that recovers semantics and the correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handling correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating data with desired properties. The code of CorrVAE is available at https://github.com/shi-yu-wang/CorrVAE.

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