What happened
Recently, it has become feasible to generate antibodies in silico using AI-based approaches such as deep learning, natural language processing, and diffusion models. This opens the door to computational antibody design as a complement to laboratory-based methods (animal immunization, hybridoma technology, and molecular display) for biologic drug discovery. However, when proposing human antibody sequences, or libraries thereof, it remains essential to determine whether they can be expressed, purified, and biophysically characterized using standardized laboratory assays typically applied early in discovery campaigns. In this work, we devised a Turing imitation game inspired experiment to compare two deep learning methods, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), for generating experimentally viable de novo antibody sequences. Under the sampling protocols tested here, a Wasserstein GAN with gradient penalty produced sequences that were experimentally validated at a markedly higher rate (92/93; 99%) than those from a VAE (2/40; 5%). Importantly, the two sets of experimental candidates were not sampled identically: GAN sequences were drawn unconditionally from the trained generator, whereas VAE sequences were generated from latent seeds corresponding to marketed antibodies not seen during training. This methodological asymmetry, rather than model architecture alone, largely accounts for the contrasting outcomes, and when the VAE was instead seeded from its training distribution its in-silico developability profiles were comparable to the GAN's. Our findings highlight both the promise of computationally driven discovery of antibody-based biotherapeutics and the decisive role of sampling strategy in determining experimental success.