We design and engineer therapeutic antibodies with enhanced affinity, specificity, and developability. Enginoma combines AI-guided CDR optimization with structure-based design to deliver clinical-ready antibody candidates.
Traditional hybridoma discovery and lead optimization is time-consuming and resource-intensive. Our AI platform accelerates therapeutic antibody development by computationally predicting optimal sequences and structural configurations before experimental validation.
AI-guided CDR design and grafting accelerates lead identification. Our computational pipeline screens thousands of variants virtually to identify the most promising candidates for experimental validation.
Predict and engineer solutions for aggregation, viscosity, expression yield, and immunogenicity early in development. Multi-parameter optimization reduces clinical failure risk and accelerates timelines.
Enginoma Structure and molecular dynamics simulations guide epitope targeting and paratope optimization. Achieve precise control over binding kinetics and selectivity for challenging targets.
Design bispecific antibodies with optimal heavy-light chain pairing and develop antibody-drug conjugates with controlled drug-to-antibody ratios and stable linkers for targeted cancer therapy.
From antigen analysis to lead characterization, our platform covers the full therapeutic antibody development workflow.
Our protein language models trained on antibody-antigen complex structures predict optimal CDR sequences for enhanced affinity and specificity. We generate and rank thousands of CDR variants computationally.
Enginoma Structure predictions combined with molecular dynamics simulations guide residue-level engineering. We optimize binding interfaces, engineer conformational stability, and predict structural changes from mutations.
Multi-parameter AI models predict and engineer developability characteristics including aggregation propensity, expression yield, viscosity, and immunogenic potential to reduce clinical failure risk.
Comprehensive therapeutic antibody engineering across multiple formats and applications
Enhance binding strength and kinetics
AI-guided directed evolution simulates affinity maturation to achieve sub-nanomolar KD. We identify and combine beneficial mutations across CDR regions to optimize on-rate (kon) and off-rate (koff) independently.
Reduce immunogenicity while retaining affinity
AI-guided framework grafting identifies critical framework residues that preserve structural integrity and binding affinity. Humanization reduces HACA responses while maintaining binding affinity.
Dual-targeting therapeutic formats
Design bispecific formats including T-cell engagers (BiTEs), dual-variable domain (DVD) antibodies, and asymmetric Fc-engineered formats. We optimize heavy-light chain assembly and linker design for stability.
Targeted cancer therapeutics
Design antibody-drug conjugates with optimized drug-to-antibody ratio (DAR), stable linkers, and potent payloa selection. Our platform predicts conjugation site accessibility and linker stability in circulation.
Integrated computational design followed by experimental validation at each stage
We analyze your target antigen structure, define functional requirements (affinity, specificity, format), and establish screening assays and developability criteria.
Our AI models generate and rank CDR variant libraries based on predicted binding affinity, structural stability, and developability scores.
Lead variants are expressed in mammalian cells and screened for binding kinetics, expression yield, and developability characteristics.
Lead candidates undergo full characterization including SPR/BLI kinetics, thermal stability, aggregation analysis, and scale-up assessment.
Therapeutic antibodies engineered for oncology, immunology, and infectious disease applications
Therapeutic antibodies targeting tumor antigens, immune checkpoint inhibitors (PD-1/PD-L1, CTLA-4), and bispecific T-cell engagers for hematological and solid tumor indications.
Antibodies targeting cytokines (IL-17, IL-6, TNF-alpha), cell surface receptors, and immune modulators for autoimmune and inflammatory diseases.
Neutralizing antibodies targeting viral surface proteins (SARS-CoV-2, influenza), bacterial toxins, and emerging pathogens for prophylactic and therapeutic applications.
Antibody-drug conjugates with cytotoxic payloa including MMAE, MMAF, DM1, calicheamicin, and novel topoisomerase I inhibitors for targeted cancer therapy.
Biobetter and biosimilar antibody engineering to improve efficacy, safety, or manufacturing compared to reference products while maintaining regulatory equivalence.
High-affinity antibodies for diagnostic assay development including ELISA, IHC, flow cytometry, and rapid point-of-care testing platforms.
Our pipeline builds on peer-reviewed methods published in leading journals
Shanker, V.R. et al. Unsupervised evolution of protein and antibody complexes with a structure-informed language model. Science 385, 46-53 (2024). https://doi.org/10.1126/science.adk8946
AI-driven de novo design of bispecific antibody targeting arms.Ramon, A. et al. Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV. Nature Machine Intelligence 6, 74-91 (2024). https://doi.org/10.1038/s42256-023-00778-3
Vision transformer model for predicting antibody developability characteristics.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.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.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 work with full-length IgG antibodies (human, murine, chimeric, humanized), Fab fragments, scFv, bispecific antibodies (T-cell engagers, dual-targeting), and antibody-drug conjugates (ADCs). The Enginoma platform supports human, murine, chimeric, and humanized sequences.
We use protein language models and structure prediction tools to design CDR mutations predicted to enhance binding affinity. Our computational pipeline screens thousands of variants virtually before wet lab validation to achieve improved affinity.
Yes. We perform AI-guided humanization by identifying framework residues critical for structural stability and grafting CDRs onto human germline frameworks while preserving binding affinity.
We predict and engineer solutions for aggregation propensity, high viscosity, poor expression, unstable disulfide bonds, and immunogenic epitopes. Our multi-parameter optimization balances affinity, specificity, expression yield, and solution behavior.
Yes. We offer full antibody expression in CHO cells or HEK293, purification, and comprehensive characterization including binding kinetics (SPR/BLI), thermal stability (DSF), and aggregation analysis.
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