# US 2026/0100243 A1 · System and method for generating sequences for therapeutic proteins > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100243 A1 - Document ID: US20260100243A1 - Status: Published application - Kind: A1 - Application number: 19/417,417 - Filed: 2025-12-12 - Published: 2026-04-09 - Earliest claimed: 2024-09-05 - Assignees: LG Management Development Institute Co., Ltd. - Applicants: LG Management Development Institute Co., Ltd. - Inventors: Kiyoung KIM; Soorin YIM; Doyeong HWANG - Web page: https://patentgrep.com/patent/US20260100243A1 ## CPC - G16B 15/00: ICT specially adapted for analysing two-dimensional [2D] or three-dimensional [3D] molecular structures, e.g. structural or functional relations or structure alignment - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/088: Non-supervised learning, e.g. competitive learning - G16B 15/30: Drug targeting using structural data; Docking or binding prediction - G16B 20/50: Mutagenesis - G16B 30/10: Sequence alignment; Homology search - G16B 30/20: Sequence assembly - G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding - G16B 40/20: Supervised data analysis - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system and method for generating protein amino acid sequences having a user-desired property are provided. Using a noise-based diffusion model, the system and method can generate amino acid sequences of proteins that have excellent disease treatment effects and are safe for use as therapeutic agents in a human body. The system can function by obtaining reference protein sequence information, generating noise-added protein sequence information, iteratively generating noise-removed protein sequence information and partially noise-added protein sequence information, and generating noise-removed output protein sequence information. Noise may be added to protein sequence information using a Gaussian or other known noise model. Noise may be removed from protein sequence information using an artificial neural network model trained by a method of minimizing a loss function. By incorporating sequence guidance and structure guidance derived from known proteins, users can generate improved candidate protein drugs for testing. ## Claims ### Claim 1 A protein sequence generation system for new therapeutic protein drug development, the system comprising: equipment for synthesizing a protein; a memory configured to store one or more instructions; and at least one processor configured to execute the one or more instructions stored in the memory, wherein operations performed by the one or more instructions comprise: a step of obtaining reference protein sequence information; a step of generating noise-added protein sequence information by repeatedly adding noise to the reference protein sequence information; and a step of generating noise-removed output protein sequence information from the noise-added protein sequence information, wherein the steps of the generating comprise: a step of generating the noise-removed protein sequence information by removing noise from input protein sequence information according to one or more protein structure guidances specified by a user; and a step of repeatedly performing all or part of a step of generating the noise-added protein sequence information by adding noise to the noise-removed protein sequence information according to one or more protein sequence guidances specified by the user, wherein all or part of the steps of the generating are repeatedly performed, wherein the noise-removed output protein sequence information resulting from a final iteration of the generating steps is used with the equipment to synthesize a candidate protein, and wherein, in relation to the reference protein, the candidate protein exhibits improved properties corresponding to the protein structure guidances. ### Claim 2 (depends on 1) The system according to claim 1, wherein the system generates a candidate protein having a property of binding to a target protein and a protein motif that are specified by the user. ### Claim 3 (depends on 2) The system according to claim 2, wherein the target protein is one or more proteins selected from among proteins associated with one or more of onset, treatment, prevention, and amelioration of a human disease. ### Claim 4 (depends on 3) The system according to claim 3, wherein the system generates protein sequence information and a candidate protein corresponding to all or a part of an antibody or binding fragment thereof. ### Claim 5 (depends on 4) The system according to claim 4, wherein the system generates protein sequence information including amino acid sequence information corresponding to a complementary binding region of the antibody or binding fragment thereof. ### Claim 6 (depends on 2) The system according to claim 2, wherein the structure guidance is one or more selected from a group consisting of binding affinity to the target protein, immunogenicity to B cells, and off-target binding affinity. ### Claim 7 (depends on 2) The system according to claim 2, wherein the sequence guidance is one or more selected from a group consisting of immunogenicity to B cells and immunogenicity to helper T cells. ### Claim 8 A protein sequence generation method performed by at least one processor, the method comprising: a step of obtaining reference protein sequence information; a step of generating noise-added protein sequence information by repeatedly adding noise to the reference protein sequence information; and a step of generating noise-removed output protein sequence information from the noise-added protein sequence information, wherein the steps of the generating comprise: a step of generating the noise-removed protein sequence information by removing noise from input protein sequence information according to one or more protein structure guidance specified by a user; and a step of repeatedly performing all or part of a step of generating the noise-added protein sequence information by adding noise to the noise-removed protein sequence information according to one or more protein sequence guidances specified by the user, and wherein all or part of the steps of the generating are repeatedly performed; and a step of using the noise-removed output protein sequence information resulting from a final iteration of the generating steps to synthesize a candidate protein, wherein, in relation to the reference protein, the candidate protein exhibits improved properties corresponding to the protein structure guidances. ### Claim 9 (depends on 8) The method according to claim 8, wherein the method generates a candidate protein having a property of binding to a target protein specified by the user. ### Claim 10 (depends on 9) The method according to claim 9, wherein the target protein is one or more proteins selected from among proteins associated with one or more of onset, treatment, prevention, and amelioration of a human disease. ### Claim 11 (depends on 10) The method according to claim 10, wherein the method generates protein sequence information and a candidate protein corresponding to all or a part of an antibody or binding fragment thereof. ### Claim 12 (depends on 11) The method according to claim 11, wherein the method generates protein sequence information including amino acid sequence information corresponding to a complementary binding region of the antibody or binding fragment thereof. ### Claim 13 (depends on 9) The method according to claim 9, wherein the structure guidance is one or more selected from a group consisting of binding affinity to the target protein, immunogenicity to B cells, and off-target binding affinity. ### Claim 14 (depends on 9) The method according to claim 9, wherein the sequence guidance is one or more selected from a group consisting of immunogenicity to B cells and immunogenicity to helper T cells. ### Claim 15 (depends on 8) A program stored in a computer-readable recording medium to execute the method according to claim 8 on a computer. ### Claim 16 (depends on 8) The method according to claim 8, wherein the steps of generating noise-added protein sequence information are carried out using a Gaussian noise model. ### Claim 17 (depends on 8) The method according to claim 8, wherein the step(s) of generating noise-removed protein sequence information are carried out using an artificial neural network model trained by a method of minimizing a loss function.