AI-Integrated Antibody Optimization via AImab ™
Tired of a manual trial and error approach to optimizing affinities on your antibody? We introduce the world’s most advanced platform to accelerate your R&D by combining the best of AI with wet lab validation to generate the highest or desired affinities that you need so that you can have the highest success in the clinic.
Our AI platform integrates protein language models, structure-aware mutation scoring, and experimental validation workflows to accelerate antibody affinity optimization while reducing reliance on large-scale library screening.
Dramatically Accelerate your R&D
1
Speed
Don't have enough time to wait for the highest quality data? Unlike current approaches that are unable to evaluate large sets of data at a time, we run massively parallel campaigns using AI combined with wet lab validation to dramatically save time for you.
2
Precision Engineering
Wondering whether you screened all the clones and epitopes and perhaps missed the best one? Our comprehensive approach allows us to survey the largest set of possible candidates to provide the ones of interest. We are able to explore the largest number of binding sites to optimize your mab.
Success Rate
Why wonder whether your campaign will work or not. Our track record of working with leading academic institutions, pharmaceutical and biotech companies is unrivaled time and time again.
3
Limitations of Existing Affinity Maturation
Most antibody optimization programs don't fail because of bad science. They fail because the workflow itself creates ceilings — on throughput, on diversity, and on speed. We have solved all of that with our platform.
Throughput Is the Bottleneck
Manual clone picking limits your team to screening hundreds of variants. Rare high-affinity binders — the ones that change a program — live in the millions you never reach.
Enrichment Signals Mislead
Noisy selection data without an AI scoring layer consistently steers teams toward low-probability candidates. You iterate, but toward the wrong targets.
Rounds of Panning Eat Timelines
Three panning rounds compounds weeks into months. By the time results arrive, programs accept "good enough" — not because it is, but because time ran out.
Validated Output Package
Ranked Variant Portfolio
- Top 10 validated antibody
variants.
Sequence annotations.
Mutation mapping relative
to parental clone.
Clonotype grouping.
Binding Kinetics Data BLI
- kd, kon, koff measurements.
Full binding curves.
Replicate consistency.
Instrument calibration metadata.
Computational Scoring
- iHTS™ score distribution.
Multi-objective ranking criteria.
Affinity vs stability trade-off analysis.
Candidate prioritization rationale.
Data Package
Raw and processed sequence files.
Experimental protocols.
Analysis scripts (if applicable).
Summary technical report.
Platform Architecture
A comprehensive approach to generating the affinities that you need
Computational Data Layer
AI Modeling Engine
Experimental Validation
Variant Library Generation
( iHTS™ Platform)
Automated construction and high-throughput screening of large antibody variant libraries around a reference sequence.
AI Scoring Layer
( iHTS™ Score)
A pre-trained antibody language model integrates sequence patterns and selection dynamics into a unified ranking score correlated with real binding outcomes.
Experimental Validation
Top-ranked variants are expressed and validated via BLI, delivering binding curves, KD values, and clonotype grouping for downstream advancement.
Computational Prioritization
Multi-objective scoring identifies high-probability candidates while preserving epitope consistency and sequence diversity.
A closed-loop workflow connecting automated screening, AI-based prioritization, and experimental confirmation—enabling scalable and predictable antibody affinity optimization.
In less then
8 weeks
Millions of Variants Screened
Al Ranking ( iHTS™ Score)
Computational
Prioritisation
Experimental
Validation
10
01
02
03
04
05
>10 sequence variants explored (Automated iHTS™ library generation and selection)
Sequence patterns and selection dynamics integrated into unified predictive scoring.
Multi-objective filtering for affinity, stability, epitope preservation, and developability. up to 10 candidates Shortlisted, 10,000 IP distinct diverse sequences generated to create 10 optimal antibodies with characteristics desired
(BLI) Expression, biophysical characterization, KD measurement.
Delivered Portfolio: 10 validated candidates ≥2
10
10
From High Dimensional Sequence space to validated Affinity Gains
Built by Scientists & Validated in the Lab.
Fidelis Bio was founded on a conviction that antibody optimization shouldn't be a manual, low-throughput guessing game. We built a platform that combines AI with real experimental validation — because computational predictions alone are not enough.