Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
Keywords: generative design, drug discovery, sample efficiency, language models, reinforcement learning, scaling
TL;DR: state-of-the-art sample efficiency using mamba for generative molecular design
Abstract: 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.
Supplementary Material: pdf
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 1831
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