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Poster
in
Workshop: Structured Probabilistic Inference and Generative Modeling

Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences

Minsu Kim · Federico Berto · Sungsoo Ahn · Jinkyoo Park

Keywords: [ biological design ] [ Conditional generative model ] [ offline model based optimization ]


Abstract:

We study the problem of optimizing biological sequences, e.g., proteins, DNA, and RNA, to maximize a black-box score function that is only evaluated in an offline dataset. We propose a novel solution, bootstrapped training of score-conditioned generator (BootGen) algorithm. Our algorithm repeats a two-stage process. In the first stage, our algorithm trains the biological sequence generator with rank-based weights to enhance the accuracy of sequence generation based on high scores. The subsequent stage involves bootstrapping, which augments the training dataset with self-generated data labeled by a proxy score function. Our key idea is to align the score-based generation with a proxy score function, which distills the knowledge of the proxy score function to the generator. After training, we aggregate samples from multiple bootstrapped generators and proxies to produce a diverse design. Extensive experiments show that our method outperforms competitive baselines on biological sequential design tasks.

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