US 12,451,216 B2
Recursive transformers for AI-based protein-protein interaction and drug design
Stephen Gbejule Odaibo, Sugar Land, TX (US)
Assigned to Deep EigenMatics LLC, Sugar Land, TX (US)
Filed by Stephen Gbejule Odaibo, Sugar Land, TX (US)
Filed on Apr. 10, 2025, as Appl. No. 19/175,905.
Prior Publication US 2025/0239330 A1, Jul. 24, 2025
Int. Cl. G16B 40/20 (2019.01); G06N 3/0455 (2023.01); G16B 15/30 (2019.01); G16B 40/30 (2019.01)
CPC G16B 40/20 (2019.02) [G06N 3/0455 (2023.01); G16B 15/30 (2019.02); G16B 40/30 (2019.02)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
a) receiving, at a processor, representations of a plurality of protein-protein complexes;
b) using the representations of the plurality of protein-protein complexes to train a neural network to obtain a representation of a protein-protein complex, given a representation of a constituent target complex of that protein-protein complex:
ii) wherein the constituent target complex is a protein or protein-protein complex,
iii) wherein the neural network is configured to proceed recursively such that:
(1) for each iteration of the recursion, the neural network is configured to generate and output a representation of a candidate protein, if any, in complex with the constituent target complex,
(2) for each iteration of the recursion, a representation of the complex of the generated candidate protein (the output of the iteration) and the constituent target complex (the input of the iteration) is passed back into the neural network as input for the next iteration of the recursion;
c) using the trained neural network to obtain a representation of a candidate protein-protein complex, given a representation of a constituent target complex;
d) synthesizing a constituent protein of the candidate protein-protein complex.