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.