Antibody design plays a pivotal role in advancing therapeutics. Although deep
learning has made rapid progress in this field, existing methods make limited
use of general protein knowledge and assume a graphical model (GM) that
violates empirical findings on proteins. To address these limitations, we
present Anfinsen Goes Neural (AGN), a graphical model that uses a pre-trained
protein language model (pLM) and encodes a seminal finding on proteins called
Anfinsen's dogma. Our framework follows a two-step process of sequence
generation with pLM and structure prediction with graph neural network (GNN).
Experiments show that our approach outperforms state-of-the-art results on
benchmark experiments. We also address a critical limitation of
non-autoregressive models -- namely, that they tend to generate unrealistic
sequences with overly repeating tokens. To resolve this, we introduce a
composition-based regularization term to the cross-entropy objective that
allows an efficient trade-off between high performance and low token
repetition. We demonstrate that our approach establishes a Pareto frontier over
the current state-of-the-art. Our code is available at
this https URL

抗体设计，深度学习，蛋白质知识，Anfinsen Goes Neural, 现有技术