| CPC G06F 30/27 (2020.01) [G06N 3/0455 (2023.01); G16B 15/30 (2019.02); G16B 40/20 (2019.02)] | 15 Claims |
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1. A method, comprising:
a) receiving, at a processor, representations of a plurality of target protein-ligand complexes;
b) training a neural network to classify the plurality of target proteins:
i) wherein the neural network is equipped with a discriminative feature localization mechanism,
iii) wherein the classification is done according to a specified ligand effect category,
iv) wherein the ligand effect category is represented by a specified partitioning of the plurality of representations of the target protein-ligand complexes,
v) wherein each partition in the partitioning represents an output class of the neural network,
vi) wherein the neural network is configured to accept the target protein's sequence and structure representation as input, and return the associated ligand's effect classification as output,
vii) wherein the neural network output also includes a discriminative feature localization map;
c) receiving, at a processor, a set of initial values of a plurality of structure parameters specifying the target protein's conformational structure;
d) using, via the processor, the trained neural network to perform inference on the initial values of the protein's conformational structure representation:
i) wherein the neural network outputs both the ligand effect classification and the discriminative feature map,
ii) wherein the discriminative feature localization map specifies values of a localized subset of the structure parameters of the target protein;
e) receiving, at a processor, a local structure update method, which is a set of instructions to update the values of the localized subset of structure parameters specified by the discriminative feature map:
i) wherein the local structure update method consists of a plurality of iterative steps, and some termination criteria,
ii) wherein the output of each iterative update step—an updated conformational structure representation—is evaluated by the neural network, yielding an updated classification score and an updated discriminative feature map,
iii) wherein:
(1) if termination criteria are not yet met, then the updated conformational structure representation and the updated discriminative feature map are both re-entered as input into the local update method, else
(2) if termination criteria are met, then the local structure update iteration terminates, and the updated conformational structure representation and the updated discriminative feature map are both returned as output;
f) selecting from the representations of a plurality of target protein-ligand complexes, a subset of complexes with a specific ligand effect category;
g) using the selected specific subset of complexes to train an expert neural network:
i) wherein the expert neural network is configured to accept the target protein's sequence and structure representation as input, and return an associated candidate ligand's sequence as output,
ii) wherein the expertise of the neural network is the specific ligand effect category of its training dataset,
iii) wherein at inference, the local structure update method is first used to update the input structure representation of the target protein towards the expertise category of the expert neural network,
iv) wherein at inference, the expert neural network's action is on the updated structure representation of the target protein returned by the local structure update method;
h) using the trained expert neural network to generate a candidate peptide ligand's sequence as output, given a target protein's sequence and structure representation as input,
i) synthesizing the peptide ligand, and
j) testing the biological activity of the synthesized peptide ligand in vitro and in vivo.
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