Generalization of Saturn to high-fidelity oracles with rough optimization landscapes

Determine whether Saturn’s sample-efficiency and its “hop-and-locally-explore” sampling behavior persist when directly optimizing high-fidelity physics-based molecular property oracles characterized by rough optimization landscapes, such as QM/MM and binding free-energy perturbation simulations. Specifically, assess whether this local exploration behavior is advantageous or detrimental in these settings and characterize the conditions under which performance carries over beyond docking-based objectives.

Background

Saturn combines the Augmented Memory reinforcement learning algorithm with a Mamba state space model backbone to achieve superior sample efficiency in goal-directed molecular generation tasks, including multi-parameter docking objectives. The authors find that Mamba synergistically exploits SMILES augmentation and experience replay, inducing a “hop-and-locally-explore” behavior that improves optimization under docking oracles.

High-fidelity physics-based oracles (e.g., QM/MM and free-energy simulations) are more correlated with experimental endpoints but typically present rougher optimization landscapes and substantially higher computational costs. While Saturn performs well on docking tasks, the authors explicitly acknowledge uncertainty about whether its sampling behavior and performance will transfer to these more stringent settings, motivating further investigation.