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Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond

2021-10-06 02:41:50
Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

Abstract

Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box sequence design, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous drug discovery approaches can be "reinvented" in our framework, and further propose new probabilistic sequence design algorithms. Extensive experiments illustrate the benefits of the proposed methodology.

Abstract (translated)

URL

https://arxiv.org/abs/2110.03372

PDF

https://arxiv.org/pdf/2110.03372.pdf


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