We enhance antibody binding affinity through AI-guided mutation prediction and optimization. Enginoma combines deep learning CDR analysis with high-throughput screening to deliver antibodies with improved affinity.
Traditional affinity maturation requires screening thousands of variants through multiple rounds. Our AI platform dramatically accelerates this process by predicting beneficial CDR mutations before any wet lab work begins.
AI-guided CDR optimization reduces screening libraries significantly. Our platform delivers high-affinity antibodies efficiently from sequence to validated candidates.
Access affinity levels and specificities that natural antibodies have not achieved. Engineer antibodies for challenging targets with improved therapeutic potential.
Engineer for developability, expression yield, and stability. Our designs meet CMC requirements for therapeutic development, not just binding assays.
Fewer screening rounds mean faster development and lower costs. Our AI platform accelerates the antibody optimization workflow.
From sequence analysis to affinity validation, our platform covers the full antibody optimization workflow.
We analyze antibody structures using Enginoma Structure, homology modeling, and experimental data to identify CDR loops, paratope residues, and developability hotspots.
Our antibody-specific models trained on affinity datasets predict beneficial CDR mutations. We rank variants by predicted affinity, specificity, and developability.
We express variants in mammalian HEK293 or CHO cells and characterize binding affinity (KD), specificity, and developability using BLI, SPR, and ELISA.
Comprehensive antibody optimization for therapeutic applications
Increase binding affinity and therapeutic efficacy
We identify affinity-limiting residues in CDR loops and engineer mutations to improve binding. Paratope optimization and loop engineering deliver significant improvements in KD.
Engineer target specificity and cross-reactivity
Optimize antibody specificity for target antigens while minimizing cross-reactivity. We engineer antibodies for challenging targets including GPCRs, ion channels, and protein-protein interfaces.
Improve developability and CMC properties
Optimize for expression yield, stability, and reduced immunogenicity. Our designs meet CMC requirements with improved solubility, reduced aggregation, and enhanced manufacturability.
Optimize antibody format and valency
Engineer Fab, scFv, VHH, and bispecific formats for specific applications. Optimize valency, half-life, and tissue penetration for therapeutic efficacy.
Integrated computational optimization followed by experimental validation at each stage
We analyze your antibody sequence and structure, define target affinity goals, and establish screening assays and developability criteria.
Our AI models predict beneficial CDR mutations and generate focused libraries of 50-200 variants, ranked by predicted affinity improvement.
Variants are expressed in HEK293 or CHO cells and screened for affinity, specificity, and developability using BLI/SPR and ELISA.
Lead variants undergo detailed kinetic characterization (KD, kon, koff), developability assessment, and delivery of sequence data plus binding report.
Optimized antibodies are driving breakthroughs across therapeutic and diagnostic fields
Enhance efficacy of mAb therapeutics through improved target binding affinity and specificity, reducing required dosing and improving patient outcomes.
Optimize dual-targeting antibodies by improving affinity at both binding sites while maintaining favorable developability properties.
Improve ADC targeting efficiency and internalization rates through affinity optimization, enhancing payload delivery to target cells.
Optimize detection antibodies for improved sensitivity and specificity in diagnostic assays, IHC, flow cytometry, and point-of-care testing.
Enhance checkpoint inhibitor binding to PD-1, PD-L1, CTLA-4 and emerging targets for improved immune activation against tumors.
Develop high-affinity antibodies against low-abundance targets or challenging epitopes for rare disease and orphan drug applications.
Our pipeline builds on peer-reviewed methods published in leading journals
Raybould, M.I.J. et al. Five computational developability guidelines for therapeutic antibody design. PNAS 116, 4025-4030 (2019). https://doi.org/10.1073/pnas.1810576116
Computational guidelines for improving antibody developability.Olsen, T.H. et al. AbLang: An antibody language model. Bioinformatics Advances 2, vbac046 (2022). https://doi.org/10.1093/bioadv/vbac046
Language model for antibody sequence analysis and optimization.Mason, D.M. et al. Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning. Nature Biomedical Engineering 5, 600-612 (2021). https://doi.org/10.1038/s41551-021-00699-9
Deep learning for therapeutic antibody optimization in mammalian cells.Bennett, N.R. et al. Atomically accurate de novo design of single-domain antibodies. Nature 637, 339-347 (2025). https://doi.org/10.1038/s41586-025-09721-5
De novo antibody design with atomic accuracy.We optimize IgG, Fab, scFv, VHH (nanobodies), and bispecific formats. Our platform handles human, humanized, and murine antibody optimization for therapeutic and diagnostic applications.
We combine structure-guided design with AI mutation prediction. Our models predict beneficial CDR mutations, while our wet lab team validates variants through BLI/SPR to achieve improved binding affinity.
Yes. We optimize expression yield, solubility, stability, and immunogenicity. Our designs often achieve significant improvements in developability scores while maintaining or improving affinity.
Yes. We offer full antibody expression in mammalian cells, purification, and characterization. We can also assist with humanization, format conversion, and CMC development support.
Whether you need higher affinity, improved specificity, or better developability, our team can deliver optimized antibodies for your therapeutic or diagnostic application.
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