WE CLAIM:
1. A computer implemented method of designing cell nucleotide sequences, the computer
implemented method comprising:
receiving, by a sequence designing system, historical data related to results of one or more procedures related to analysis of cell nucleotide sequences from one or more databases;
executing, by the sequence designing system, an Artificial Intelligence (AI) based prediction model using vectorized data corresponding to the historical data;
predicting, by the sequence designing system, a plurality of cell nucleotide sequences having values of one or more cell characteristics within a predefined threshold values of the one or more cell characteristics for a target cell nucleotide sequence using the AI based prediction model;
identifying, by the sequence designing system, one or more feasible cell nucleotide sequences among the plurality of cell nucleotide sequences based on predefined reference information, wherein the one or more feasible cell nucleotide sequences is each assigned with a rank to generate a ranked list; and
generating, by the sequence designing system, an explanation for the ranked list of the one or more feasible cell nucleotide sequences, thereby designing the cell nucleotide sequences.
2. The computer implemented method as claimed in claim 1, wherein receiving the
historical data further comprises:
performing, by the sequence designing system, one or more pre-processing operations on the historical data for verifying correctness and completeness of the historical data based on the predefined reference information; and
arranging, by the sequence designing system, the historical data in a chronological order based on timestamps associated with the historical data.
3. The computer implemented method as claimed in claim 1, wherein predicting the
plurality of cell nucleotide sequences comprises:
determining, by the sequence designing system, an equilibrium dissociation constant (KD) related to binding affinity, value of biomarkers related to at least one of immune evasion and cell fitness using the AI based prediction model.

4. The computer implemented method as claimed in claim 1, wherein identifying the one
or more feasible cell nucleotide sequences comprises:
extracting, by the sequence designing system, a feasibility data corresponding to feasibility of each of the plurality of cell nucleotide sequences from the predefined reference information, based on an equilibrium dissociation constant (KD) and value of biomarkers related to at least one of immune evasion and cell fitness.
5. The computer implemented method as claimed in claim 1, wherein identifying the one
or more feasible cell nucleotide sequences further comprises:
selecting, by the sequence designing system, one or more of the plurality of cell nucleotide sequences within predefined threshold ranges of at least one of immune evasion and cell fitness; and
generating, by the sequence designing system, the ranked list of the one or more feasible cell nucleotide sequences based on an equilibrium dissociation constant (KD).
6. The computer implemented method as claimed in claim 1, wherein the historical data comprises at least one of one or more antigen nucleotide sequences, one or more cell nucleotide sequences, biomarkers data, function-specific biomarkers data, clinical trial progression data, clinical trial outcome data, in-vitro progression data, and in-vitro outcome data.
7. The computer implemented method as claimed in claim 1, wherein the one or more cell characteristics comprises at least one of binding affinity, immune evasion, and cell fitness.
8. The computer implemented method as claimed in claim 1, wherein the predefined reference information comprises at least one of a three-dimensional (3D) structure and binding database, a gene regulatory and epigenetic database, a target selectivity database, a signaling pathways database, an inflammatory and Endoplasmic Reticulum (ER) stress database, and a threshold range database.
9. A sequence designing system for designing cell nucleotide sequence, the sequence designing system comprising:

a processor; and
a memory, communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
receive historical data related to results of one or more procedures related to analysis of cell nucleotide sequences from one or more databases;
execute an Artificial Intelligence (AI) based prediction model using vectorized data corresponding to the historical data;
predict a plurality of cell nucleotide sequences having values of one or more cell characteristics within a predefined threshold values of the one or more cell characteristics for a target cell nucleotide sequence using the AI based prediction model;
identify one or more feasible cell nucleotide sequences among the plurality of cell nucleotide sequences based on predefined reference information, wherein the one or more feasible cell nucleotide sequences is each assigned with a rank to generate a ranked list; and
generate an explanation for the ranked list of the one or more feasible cell nucleotide sequences, thereby designing the cell nucleotide sequences.
10. The sequence designing system as claimed in claim 9, wherein after receiving the
historical data, the processor is further configured to:
perform one or more pre-processing operations on the historical data for verifying correctness and completeness of the historical data based on the predefined reference information; and
arrange the historical data in a chronological order based on timestamps associated with the historical data.
11. The sequence designing system as claimed in claim 9, wherein the processor predicts
the plurality of cell nucleotide sequences by:
determining an equilibrium dissociation constant (KD) related to binding affinity, value of biomarkers related to at least one of immune evasion and cell fitness using the AI based prediction model.
12. The sequence designing system as claimed in claim 9, wherein the processor identifies
the one or more feasible cell nucleotide sequences by:

extracting a feasibility data corresponding to feasibility of each of the plurality of cell nucleotide sequences from the predefined reference information, based on an equilibrium dissociation constant (KD) and value of biomarkers related to at least one of immune evasion and cell fitness.
13. The sequence designing system as claimed in claim 9, wherein after identifying the one
or more feasible cell nucleotide sequences, the processor is further configured to:
select one or more of the plurality of cell nucleotide sequences within predefined threshold ranges of at least one of immune evasion and cell fitness; and
generate the ranked list of the one or more feasible cell nucleotide sequences based on an equilibrium dissociation constant (KD).
14. The sequence designing system as claimed in claim 9, wherein the historical data comprises at least one of one or more antigen nucleotide sequences, one or more cell nucleotide sequences, biomarkers data, function-specific biomarkers data, clinical trial progression data, clinical trial outcome data, in-vitro progression data, and in-vitro outcome data.
15. The sequence designing system as claimed in claim 9, wherein the one or more cell characteristics comprises at least one of binding affinity, immune evasion, and cell fitness.
16. The sequence designing system as claimed in claim 9, wherein the predefined reference information comprises at least one of a three-dimensional (3D) structure and binding database, a gene regulatory and epigenetic database, a target selectivity database, a signaling pathways database, an inflammatory and Endoplasmic Reticulum (ER) stress database, and a threshold range database.