Topology-Aware Generation and Activity-Based Filtering: A Computational-Experimental Framework for Data-Scarce...

Quaternary ammonium compounds (QACs) are widely used antimicrobial disinfectants whose efficacy is threatened by increased bacterial resistance. Artificial intelligence-guided development of novel QACs is constrained by historically sparse structure-activity data and methods to generate novel chemical entities with bioactivity. This paper presents a comparative experimental study of two computational workflows designed to accelerate QAC discovery under data-limited conditions. Both workflows employ a topology-aware variational autoencoder to generate novel candidates. In Workflow 1, generated QAC structures were directly subjected to expert evaluation within a fixed time constraint through the systematic application of chemistry-domain decision criteria. In Workflow 2, generated candidates were first computationally filtered using predictive models trained to anticipate antimicrobial activity, advancing only molecules projected to be highly active against at least one bacterial strain for expert evaluation. This predictive filtering enabled the assessment of a larger, higher-quality candidate pool within the same time constraint. Comparative assessment of the compound sets resulting from the two workflows revealed substantial improvements in candidate quality: compounds deemed synthesis-worthy increased from 9% to 38%, while invalid outputs decreased from 21% to 0%. Experimental characterization of 29 selected compounds across both workflows yielded 11 novel QACs with experimentally validated minimum inhibitory concentrations of 1-32 μM against four bacterial pathogens. These results demonstrate that topology-aware generation coupled with computational prefiltering enables systematic navigation of data-scarce chemical spaces while respecting practical constraints on expert evaluation time.