Model-based reinforcement learning for biological sequence design

By Christof Angermueller, David Dohan, David Belanger, Ramya Deshpande, Kevin Murphy, Lucy J. Colwell. 2020

166 citations recorded by the source corpus.

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Learning inverse folding from millions of predicted structures (2022, methodology): “Additionally, as an alternative to sequence generation models, model-guided algorithms design sequences based on predictive models as oracles (Yang et al., 2019; Angermueller et al., 2019; Brookes et al., 2019; Sinai et al., 2020).”
Protein sequence design with deep generative models (2021, methodology): “An emerging approach to this optimization problem is to optimize with generative models [38, 39, 40].”
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation (2021, methodology): “…al., 2020; Jin et al., 2020; Luo et al., 2021), MCMC methods (Seff et al., 2019; Xie et al., 2021), RL (Segler et al., 2017; Cao and Kipf, 2018; Popova et al., 2019; Gottipati et al., 2020; Angermueller et al., 2020) and evolutionary methods (Brown et al., 2004; Jensen, 2019; Swersky et al., 2020).”
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design (2020, methodology): “With DyNA-PPO, Angermueller et al. (2020) achieve state-of-the-art results in sequence design tasks following this approach.”
Effective Diversity in Population-Based Reinforcement Learning (2020, background): “This may be used for a multitude of reasons, and one would hope the majority would be positive, such as model-based reinforcement learning for protein folding [2] (with an ensemble).”