Deep learning employs deep neural net‑ works, which comprise several core architectures, such as convolutional neural networks (CNNs),10 recurrent neural networks (RNNs) or long short‑term memory (LSTM),11gen‑ erative adversarial networks (GANs),12 variational autoencoders (VAEs)13, and trans‑ former14models (Figure6.2). Each architecture is distinguished by its specic strengths, weaknesses, and applications. These models autonomously learn features directly from data, thereby eliminating the need for manual feature extraction. The training process, which optimizes weights through backpropagation and gradient descent, renes learn‑ ing over time. Although DL models reduce the necessity for manual feature engineering by learning complex data patterns, data pre‑processing is still crucial in the bioinfor‑ matics workow. This ensures data quality and compatibility, which are essential for the success of DL applications. This aspect becomes particularly critical in antibody design, where the sizes, amino acid compositions, and structural diversity of the functional sites, namely the complementarity‑determining regions (CDRs), vary across antibodies. Such variability complicates the direct comparison of features among antibodies.