Editing Antibody Design by AI (section)

== Genetic algorithms for antibody sequence optimization ==

As an alternative approach to our Ens-Grad methodology, we also applied a non-gradientbased optimization procedure to propose high affinity CDR-H3 sequences. We designed two genetic algorithms (GA-KNN and GA-CNN) that seek to explore sequences under the guidance of a fitness function that estimates the enrichment of a proposed sequence. The two genetic algorithms differed only in their fitness functions that were used to estimate the predicted enrichment of a CDR-H3 sequence.

Each genetic algorithm selected random sequences to start. In each generation, the genetic algorithm mutated parental sequences 600 times and performed a crossover on a pair of parents 600 times to produce a total of 1200 new children. In practice, we observe that random crossovers and mutations are quite unlikely to produce sequences that resemble valid antibody sequences in the seed pool. To enhance the sampling efficiency of the genetic algorithm, random crossover is then performed between each child sequence generated and its 10 nearest neighbor sequences in 1000 randomly sampled sequences from the seed pool. This effectively “projects” back the sampled child sequence back to the space of allowable sequences. After each child sequence is processed in this way, 3000 survivors from the 4200 sequences were probabilistically chosen based on fitness. Two fitness functions were evaluated. For GA-KNN we used the average scores of the ten nearest neighbors from 1000 examples in the training set. For GA-CNN, we used the prediction from a single convolutional neural network that was trained on the training set and consists of a convolutional layer of 64 filters with size 5 and stride 1, a max-pooling layer of size 2, a fully-connected layer of 16 neurons, and a Dropout layer with a dropout rate of 0.3 . The genetic algorithms ran for 30 generations. The final pool of generated sequences consists of all the sequences the algorithm sampled.