Absci Investor Presentation Deck
model
from absci import de_novo_model
de_novo_model.load_latest()
antigen model.load_pdb("7olz.pdb",
chain="A")
antibodies = model.predict(antigen, N=300000)
DRUG
CREATION
absci.
from absci_library import codon_optimizer
library
CORPORATE PRESENTATION
SEPTEMBER 2023
ABSCI CORPORATION 2023 ALL RIGHTS RESERVED
= codon_optimizer.reverse_translate(library)
library.to_csv("covid-antibody-designs.csv")
from absci import lead_opt_model
lead_optimizer = lead_opt_model.load_latest()
library.naturalness =
lead_optimizer.naturalness(library)
library.to_wet_lab(assay="ACE")
lead_optimizer.optimize(library).to_wet_lab(as
say="SPR")
from absci import genetic_algorithm; parameters=["maximizelbinding_affinity:pH=7.5", "minimizelbinding_affinity: pH-6.0",
"maximize l human_naturalness"]; library = genetic_algorithm.multiparametric_optimization (library, parameters, evolutions=100);
library.to_wet_lab(assays=["ACE", "SPR", "Bioassays"])View entire presentation