GEMS -- Guided Evolutionary Molecule Design for Sustainable Chemicals
For chemists and environmental scientists, GEMS addresses the challenge of sparse data and the need for expert intuition in designing sustainable chemicals, but the evaluation is limited to interviews and a usage scenario.
GEMS is an interactive visual analytics tool that allows domain experts to guide a genetic algorithm for molecule design by modifying scoring functions and populations without programming. It was applied to design sustainable antioxidant alternatives, with positive feedback from domain scientists.
Designing safe and sustainable chemicals is critical to combat chemical pollution in our environment. Machine learning (ML) methods have been developed to aid with de novo molecule design. However, data on the environmental impacts of chemical compounds are sparse, resulting in low-fidelity ML oracles and unreliable candidate proposals. Furthermore, generative ML models rely on numerical scoring functions that cannot fully capture the nuanced chemical intuition of expert scientists required for real-world molecular design. We present GEMS-an interactive visual analytics tool that enables domain experts to directly collaborate with a genetic algorithm for molecule design. Users can integrate their expert knowledge to guide the evolutionary process by modifying the scoring function and molecule population without programming knowledge or ML developer support. A usage scenario demonstrates the system's application in designing sustainable antioxidant alternatives. In an interview session with domain scientists, we collected feedback on the usefulness of GEMS.