  Generative molecular design for drug discovery has very recently achieved a
wave of experimental validation, with language-based backbones being the most
common architectures employed. The most important factor for downstream success
is whether an in silico oracle is well correlated with the desired end-point.
To this end, current methods use cheaper proxy oracles with higher throughput
before evaluating the most promising subset with high-fidelity oracles. The
ability to directly optimize high-fidelity oracles would greatly enhance
generative design and be expected to improve hit rates. However, current models
are not efficient enough to consider such a prospect, exemplifying the sample
efficiency problem. In this work, we introduce Saturn, which leverages the
Augmented Memory algorithm and demonstrates the first application of the Mamba
architecture for generative molecular design. We elucidate how experience
replay with data augmentation improves sample efficiency and how Mamba
synergistically exploits this mechanism. Saturn outperforms 22 models on
multi-parameter optimization tasks relevant to drug discovery and may possess
sufficient sample efficiency to consider the prospect of directly optimizing
high-fidelity oracles.
