2025
MING: A Functional Approach to Learning Molecular Generative Models
AISTATS 2025poster
Traditional molecule generation methods often rely on sequence- or graph-based representations, which can limit their expressive power or require complex permutation-equivariant architectures. This paper introduces a novel paradigm for learning molecule generative models based on functional represen…