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Structured Q-learning For Antibody Design

Alexander I. Cowen-Rivers; Philip John Gorinski; Aivar Sootla; Asif Khan; Furui Liu; Jun Wang; Jan Peters; Haitham Bou-Ammar
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2209.04698, Pages 0-10, arXiv, 2022.


Optimizing combinatorial structures is core to many real-world problems, such as those encountered in life sciences. For example, one of the crucial steps involved in antibody design is to find an arrangement of amino acids in a protein sequence that improves its binding with a pathogen. Combinatorial optimization of antibodies is difficult due to extremely large search spaces and non-linear objectives. Even for modest antibody design problems, where proteins have a sequence length of eleven, we are faced with searching over 2.05 x 10^14 structures. Applying traditional Reinforcement Learning algorithms such as Q-learning to combinatorial optimization results in poor performance. We propose Structured Q-learning (SQL), an extension of Q-learning that incorporates structural priors for combinatorial optimization. Using a molecular docking simulator, we demonstrate that SQL finds high binding energy sequences and performs favourably against baselines on eight challenging antibody design tasks, including designing antibodies for SARS-COV.

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