Graves Jordan, Byerly Jacob, Priego Eduardo, Makkapati Naren, Parish S Vince, Medellin Brenda, Berrondo Monica
Macromoltek, Inc, 2500 W William Cannon Dr, Suite 204, Austin, Austin, TX 78745, USA.
Antibodies (Basel). 2020 Apr 28;9(2):12. doi: 10.3390/antib9020012.
Driven by its successes across domains such as computer vision and natural language processing, deep learning has recently entered the field of biology by aiding in cellular image classification, finding genomic connections, and advancing drug discovery. In drug discovery and protein engineering, a major goal is to design a molecule that will perform a useful function as a therapeutic drug. Typically, the focus has been on small molecules, but new approaches have been developed to apply these same principles of deep learning to biologics, such as antibodies. Here we give a brief background of deep learning as it applies to antibody drug development, and an in-depth explanation of several deep learning algorithms that have been proposed to solve aspects of both protein design in general, and antibody design in particular.
受计算机视觉和自然语言处理等领域成功案例的推动，深度学习最近通过辅助细胞图像分类、发现基因组联系以及推进药物研发等方式进入了生物学领域。在药物研发和蛋白质工程中，一个主要目标是设计出一种能作为治疗药物发挥有用功能的分子。通常，重点一直放在小分子上，但已经开发出了新方法，将深度学习的这些相同原理应用于生物制品，比如抗体。在此，我们简要介绍深度学习在抗体药物开发中的应用背景，并深入解释为解决一般蛋白质设计尤其是抗体设计方面的问题而提出的几种深度学习算法。
