A method for generating a library of candidate antibodies with key properties of interest, the method comprising: generating, by a computing system including one or more computing devices having one or more processors and memory and using a first generating component of a generative adversarial network , a plurality of first amino acid sequences, individual first amino acid sequences of the plurality of first amino acid sequences corresponding to antibody light chains ; generating, by the computing system and using a second generating component of the generative adversarial network , a plurality of second amino acid sequences, individual second amino acid sequences of the plurality of second amino acid sequences corresponding to antibody heavy chains ; producing a third amino acid sequence by combining, by the computing system and using the generative adversarial network , a first amino acid sequence of the plurality of first amino acid sequences with a second amino acid sequence of the plurality of second amino acid sequences, the third amino acid sequence corresponding to an antibody including a light chain corresponding to the first amino acid sequence and a heavy chain corresponding to the second amino acid sequence; analyzing, by the computing system and using the generative adversarial network , the third amino acid sequence with respect to an additional plurality of amino acid sequences , the additional plurality of amino acid sequences being included in training data for the generative adversarial network ; generating, by the computing system, an output indicating a measure of similarity between the third amino acid sequence and at least a portion of the additional plurality of amino acid sequences ; and when the measure of similarity exceeds a threshold, including the third amino acid sequence in a library of candidate antibodies, at least a portion of the library of candidate antibodies being intended for expression in vivo. generating, by a computing system including one or more computing devices having one or more processors and memory and using a first generating component of a generative adversarial network , a plurality of first amino acid sequences, individual first amino acid sequences of the plurality of first amino acid sequences corresponding to antibody light chains ; generating, by the computing system and using a second generating component of the generative adversarial network , a plurality of second amino acid sequences, individual second amino acid sequences of the plurality of second amino acid sequences corresponding to antibody heavy chains ; producing a third amino acid sequence by combining, by the computing system and using the generative adversarial network , a first amino acid sequence of the plurality of first amino acid sequences with a second amino acid sequence of the plurality of second amino acid sequences, the third amino acid sequence corresponding to an antibody including a light chain corresponding to the first amino acid sequence and a heavy chain corresponding to the second amino acid sequence; analyzing, by the computing system and using the generative adversarial network , the third amino acid sequence with respect to an additional plurality of amino acid sequences , the additional plurality of amino acid sequences being included in training data for the generative adversarial network ; generating, by the computing system, an output indicating a measure of similarity between the third amino acid sequence and at least a portion of the additional plurality of amino acid sequences ; and when the measure of similarity exceeds a threshold, including the third amino acid sequence in a library of candidate antibodies, at least a portion of the library of candidate antibodies being intended for expression in vivo. <EOS>