Even though experimentally generated data on binding becomes more high‑through‑ put, it still falls short of covering all the possible antibody‑antigen interaction pairs. To overcome this hurdle, researchers tested this concept in silico using simulated Ab–Ag binding data (Akbar etal. 2022). This allowed them to also perform a theoretical study of a general “binding objective function” and address the problem of “learnability” of the Ab–Ag recognition. Akbar and colleagues trained the LSTM‑recurrent neural net‑ work (RNN) on CDR‑H3 that were apriori computationally associated with develop‑ ability data (Robert etal. 2021). The simulations included a wide range of physiological antibody‑binding parameters and enabled the 3D structure computation of synthetic antibody‑antigen binding. The network generated sequences that exceeded the develop‑ ability parameters of sequences on which the model was trained. RNN‑LSTM models were trained separately on binders as positive data and non‑binders as negative data, suggesting that the design of native‑like CDR‑H3 sequences is possible without negative examples. Also, accuracy is likely to be further improved with more training examples. This fact could potentially reduce the cost of generating training datasets, given that the HER2generation rate of the RNN was remarkably high despite only being trained on positive data. In addition, by delivering binders against this target for training, the network could generate specic binders against HER2. This demonstrated that antibody properties are, in principle, trainable on multiple modalities– binding and develop‑ ability. The ability of in silico generative frameworks to generate antigen‑specic and developable immune receptor sequences on a large scale and on demand is a critical feature for the rapid synthesis of antibodies.