Revealing Token-Level Importance in Conditional Molecular Design Through Kullback–Leibler Divergence - Cerveira, Arthur; Corrêa, Ulisses B. (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul, 2025)
Conditional molecular design using transformer-based models can accelerate drug discovery, but their black-box nature limits interpretability. We propose a method to explain these models by quantifying the influence of property conditions on the generative process. Our approach uses the Kullback–Leibler Divergence to measure the difference between conditional and unconditional output distributions at each generation step. This allows us to identify the token-level importance of a sequence for specific desired conditions.