File size: 1,333 Bytes
6d3b444 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
from modules.extract_signature import extract_signature
from modules.fuse_perspectives import fuse_perspectives
from modules.generate_binders import generate_binders
from modules.run_simulations import run_simulations
from modules.validate_ethics import validate_ethics
from modules.personalize_binders import personalize_binders
from modules.exporter import export_designs
def codette_pipeline(target_input):
# Stage 1: Extract Signature
sig = extract_signature(target_input)
# Stage 2: Perspective Fusion
context = fuse_perspectives(sig)
# Stage 3: Candidate Generation
candidates = generate_binders(context)
# Stage 4: Simulations
scored = run_simulations(candidates)
# Stage 5: Ethics Filter
ethics_checked = validate_ethics(scored)
# Stage 6: Personalization
personalized = personalize_binders(ethics_checked, patient_data={
"immune_profile": ["A*24:02", "B*27:05"],
"metabolic_rate": 1.2,
"prior_exposure": ["SARS-CoV-2", "Influenza-B"],
"ancestry_profile": ["Native", "Irish"]
})
# Stage 7: Export
result = export_designs(personalized)
return result
if __name__ == "__main__":
# Example input
test_seq = "MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFD"
output = codette_pipeline(test_seq)
print(output)
|