US 12,488,859 B2
Peptide based vaccine generation system with dual projection generative adversarial networks
Renqiang Min, Princeton, NJ (US); Hans Peter Graf, South Amboy, NJ (US); and Ligong Han, Edison, NJ (US)
Assigned to NEC Corporation, Tokyo (JP)
Filed by NEC Laboratories America, Inc., Princeton, NJ (US)
Filed on Apr. 1, 2022, as Appl. No. 17/711,310.
Claims priority of provisional application 63/170,712, filed on Apr. 5, 2021.
Prior Publication US 2022/0328127 A1, Oct. 13, 2022
Int. Cl. G16B 15/30 (2019.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G16B 40/20 (2019.01)
CPC G16B 15/30 (2019.02) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); G16B 40/20 (2019.02)] 19 Claims
 
1. A computer-implemented method for generating new binding peptides to Major Histocompatibility Complex (MHC) proteins, comprising:
training, by a processor device, a Generative Adversarial Network (GAN) having a generator and a discriminator only on a set of binding peptide sequences given training data comprising the set of binding peptide sequences and a set of non-binding peptide sequences, wherein a GAN training objective comprises the discriminator being iteratively updated to distinguish generated peptide sequences from sampled binding peptide sequences as fake or real and the generator being iteratively updated to fool the discriminator;
generating new peptide sequences with user-specified binding properties to create a vaccine based on the trained GAN; and
administering the vaccine,
wherein said training comprises learning two projection vectors for a binding class by optimizing a GAN training objective that combines two cross-entropy losses, a first of the two cross-entropy losses discriminating binding peptide sequences in the training data from non-binding peptide sequences in the training data, and a second of the two cross-entropy losses discriminating generated binding peptide sequences from non-binding peptide sequences in the training data.