Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
Keywords: generative molecular design, small molecule, sample efficiency, drug discovery, reinforcement learning
TL;DR: sample-efficient generative molecular design using mamba architecture massive data augmentation
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.
Submission Number: 15
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