An Instructible Chemist-AI Alignment Framework for Generating Quaternary Ammonium Compound Structures

This paper presents a novel Chemist-AI Alignment framework for generating novel structures of quaternary ammonium compounds (QACs), a crucial class of antimicrobial agents. The framework uniquely integrates AI-driven small molecule generation with iterative feedback from chemist experts, leveraging both rapid assessments and comprehensive wet-lab validations to optimize for biological potency and synthetic feasibility. Central to the framework is a hierarchical generative model that captures the QAC hierarchical topology. Extensive experiments highlight the efficacy of the framework in identifying promising QAC candidates, many of which are synthesized and evaluated in the wet laboratory, underscoring the potential of synergistic AI and domain-expert feedback in accelerating the design of novel antimicrobial agents.