GEMS -- Guided Evolutionary Molecule Design for Sustainable Chemicals

TL;DR

GEMS is an interactive visual analytics tool that allows domain experts to collaboratively guide evolutionary molecule design for sustainable chemicals, integrating expert knowledge without programming or ML expertise.

Contribution

The paper introduces GEMS, a novel tool enabling expert-guided evolutionary molecule design through interactive modification of scoring functions and populations.

Findings

GEMS facilitates expert collaboration in molecule design.

The tool supports sustainable antioxidant development.

Feedback indicates usefulness in real-world applications.

Abstract

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.