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AB-ARS

Antibody-Automatic Report System

Antibody-
Automatic Report System

Design optimal antibody candidates in 2 weeks. Ab-ARS® uses 3D structural analysis
and AI algorism to simultaneously optimize binding affinity and developability.

AB-ARS® — The Intelligent Frontier of Antibody Engineering

AB-ARS® — The Intelligent Frontier
of Antibody Engineering

Conventional antibody discovery faces structural challenges in early-stage development,
where affinity, stability, and developability must be considered simultaneously,
especially for challenging targets with low predictability.
AB-ARS® addresses these limitations through AI-driven design.
By precisely analyzing target–antibody interactions,
the platform identifies development-ready antibody candidates from the outset,
reducing unnecessary iterations and improving efficiency toward clinical advancement.

AB-ARS®: Navigating the Complex Challenges of Antibody Discovery

AI and Structure-Based Design — Accelerating the First Gate of Antibody Drug Discovery

AB-ARS® identifies high-potential antibody candidates through AI-powered structural analysis and predictive modeling. The platform automatically designs and optimizes CDR regions, leveraging a database of 1.8M+ peptide-peptide interactions and 5,000+ simulation datasets.

AB-ARS® robust computational infrastructure ensures smooth workflows even as variant numbers and design complexity increase. AB-ARS® supports multiple antibody formats—IgG, VHH (Nanobody), and ScFv—making it readily applicable across diverse antibody drug development pipelines.

AI-Powered Epitope Discovery & CDR Targeting

3D structure-based AI analysis predicts antigen binding sites and identifies high-affinity CDR regions.

AI-Powered Epitope Discovery & CDR Targeting

3D structure-based AI analysis predicts antigen binding sites and identifies high-affinity CDR regions.

AI-Powered Epitope Discovery & CDR Targeting

3D structure-based AI analysis predicts antigen binding sites and identifies high-affinity CDR regions.

Large-Scale Data Base-Driven CDR Library Design

Optimized variant libraries are generated based on a peptide–peptide interaction database, reflecting CDR loop conformation, polarity, and charge distribution.

Large-Scale Data Base-Driven CDR Library Design

Optimized variant libraries are generated based on a peptide–peptide interaction database, reflecting CDR loop conformation, polarity, and charge distribution.

Large-Scale Data Base-Driven CDR Library Design

Optimized variant libraries are generated based on a peptide–peptide interaction database, reflecting CDR loop conformation, polarity, and charge distribution.

3D Docking–Based Binding Affinity Prediction

Generated antibody candidates undergo docking analysis with antigens, precisely evaluating binding affinity and stability through structure-based metrics including CNN scores, RMSD, and MD simulations.

3D Docking–Based Binding Affinity Prediction

Flexible Antibody Format & Developability Filterin

Applicable to multiple antibody formats including IgG, VHH (Nanobody), and scFv. Candidate molecules are selected based on developability indicators such as CDR stability, isoelectric point, and solubility.

Flexible Antibody Format & Developability Filterin

3D Docking–Based Binding Affinity Prediction

Flexible Antibody Format & Developability Filterin

AB-ARS® Process

AB-ARS® Process

Epitope Discovery

Identifying antigen binding sites through 3D structure-based prediction

Ab ARS precisely maps weakly binding epitopes by analyzing the 3D structure of the antigen protein.

Epitope Discovery

Identifying antigen binding sites through 3D structure-based prediction

Ab ARS precisely maps weakly binding epitopes by analyzing the 3D structure of the antigen protein.

Epitope Discovery

Identifying antigen binding sites through 3D structure-based prediction

Ab ARS precisely maps weakly binding epitopes by analyzing the 3D structure of the antigen protein.

CDR Generation

Creating optimal CDR variant libraries from 1.8M+ peptide-peptide interaction database

Multiple antibody variants with high potential for antigen interaction are designed by comprehensively considering properties such as CDR loop conformation, hydrophobicity, and charge distribution. A peptide–peptide interaction database is leveraged to ensure both diversity and predictive accuracy in antibody design.

CDR Generation

Creating optimal CDR variant libraries from 1.8M+ peptide-peptide interaction database

CDR Generation

Creating optimal CDR variant libraries from 1.8M+ peptide-peptide interaction database

Antigen-Antibody Binding Simulation

Precise affinity prediction through 3D docking analysis and CNN-based evaluation

The generated antibody candidates undergo 3D docking with the antigen, and various structural indicators, including binding energy, CNN score, and root mean square deviation (RMSD), are analyzed. Finally, molecular dynamics (MD) simulations are performed to assess binding stability.

Antigen-Antibody Binding Simulation

Precise affinity prediction through 3D docking analysis and CNN-based evaluation

Antigen-Antibody Binding Simulation

Precise affinity prediction through 3D docking analysis and CNN-based evaluation

Developability Assessment

Filtering final candidates based on developability assessment

For binding-validated candidates, Ab ARS evaluate additional developability and safety including CDR structural stability, isoelectric point, solubility, and charge distribution to select optimal antibody candidates.

Developability Assessment

Filtering final candidates based on developability assessment

Developability Assessment

Filtering final candidates based on developability assessment

Core Value of AB-ARS®

Core Value
of AB-ARS®

90%

Revolutionary Speed

Reduce antibody candidate discovery from years to within 3 weeks.

90%

Revolutionary Speed

Reduce antibody candidate discovery from years to within 3 weeks.

60%

R&D Cost Reduction

Reduce early development costs by minimizing experimental iterations through in silico prediction.

60%

R&D Cost Reduction

Reduce early development costs by minimizing experimental iterations through in silico prediction.

50%

Lower Clinical Failure Risk

Early validation of antibody efficacy and stability through 3D structure-based docking and CNN analysis minimizes downstream failure potential.

50%

Lower Clinical Failure Risk

Early validation of antibody efficacy and stability through 3D structure-based docking and CNN analysis minimizes downstream failure potential.

80%

Increased Antibody Format Coverage

Applicable to all major antibody modalities including IgG, VHH (Nanobody), and scFv, ensuring high platform flexibility and scalability.

80%

Increased Antibody Format Coverage

Applicable to all major antibody modalities including IgG, VHH (Nanobody), and scFv, ensuring high platform flexibility and scalability.

90%

Revolutionary Speed

Reduce antibody candidate discovery from years to within 3 weeks.

90%

Revolutionary Speed

Reduce antibody candidate discovery from years to within 3 weeks.

60%

R&D Cost Reduction

Reduce early development costs by minimizing experimental iterations through in silico prediction.

60%

R&D Cost Reduction

Reduce early development costs by minimizing experimental iterations through in silico prediction.

50%

Lower Clinical Failure Risk

Early validation of antibody efficacy and stability through 3D structure-based docking and CNN analysis minimizes downstream failure potential.

50%

Lower Clinical Failure Risk

Early validation of antibody efficacy and stability through 3D structure-based docking and CNN analysis minimizes downstream failure potential.

80%

Increased Antibody Format Coverage

Applicable to all major antibody modalities including IgG, VHH (Nanobody), and scFv, ensuring high platform flexibility and scalability.

80%

Increased Antibody Format Coverage

Applicable to all major antibody modalities including IgG, VHH (Nanobody), and scFv, ensuring high platform flexibility and scalability.

These data are based on projects and PoC analysis data from Syntekabio between 2021-2025.

Research Highlights

The result of PD-L1-Antibody Design

Using AB-ARS®, we designed novel antibody candidates that specifically bind to PD-L1. These candidates demonstrated binding affinity equivalent to or superior to that of the existing treatment (atezolizumab), demonstrating the potential of AB-ARS®.

Confirmed specific binding to PD-L1

Target Engagement

Confirmed specific binding to PD-L1

Target Engagement

Confirmed specific binding to PD-L1

Target Engagement

Identified distinct CDR3 interaction patterns

Novel Mechanism

Identified distinct CDR3 interaction patterns

Novel Mechanism

Identified distinct CDR3 interaction patterns

Novel Mechanism

Binding affinity comparable or exceeding commercial antibodies

Competitive Performance

Binding affinity comparable or exceeding commercial antibodies

Competitive Performance

Experimentally validated residue-level predictions

Molecular Precision

Experimentally validated residue-level predictions

Molecular Precision

Binding affinity comparable or exceeding commercial antibodies

Competitive Performance

Binding affinity comparable or exceeding commercial antibodies

Competitive Performance

Experimentally validated amino acid predictions

Molecular Precision

Experimentally validated amino acid predictions

Molecular Precision

Ab-ARS®

AB-ARS® antibody candidate (left) vs. Atezolizumab (right)

Comparison of PD-L1 Binding Pose

AB-ARS® candidate #1

Ab-ARS®
candidate #1

Atezolizumab

Known to bind PD-L1

EC50

AB-ARS® Candidate vs. Atezolizumab

These results are based on in vitro studies.

Business Model

Pre-validated Package Model

AI Driven Asset Program

Pre-validated antibody candidates

The Asset Program provides prepackaged, high-quality antibody candidates selected through large-scale AI analysis. These candidates can be applied to research through on-site evaluation and are provided in a standardized data format for global pharmaceutical companies.

Canditate: PD-L1 (CD274), PD-1 (PDCD1), CTLA-4, etc.

Generate 50,000+ variants per target → HTS validation → Final candidate selection

Includes target-specific ELISA and expression data

Standardized data packages

Learn More About Asset List

Pre-validated Package Model

AI Driven Asset Program

Pre-validated antibody candidates

Canditate: PD-L1 (CD274), PD-1 (PDCD1), CTLA-4, etc.

Generate 50,000+ variants per target → HTS validation → Final candidate selection

Includes target-specific ELISA and expression data

Standardized data packages

Learn More About Asset List

Pre-validated Package Model

AI Driven Asset Program

Pre-validated antibody candidates

Canditate: PD-L1 (CD274), PD-1 (PDCD1), CTLA-4, etc.

Generate 50,000+ variants per target → HTS validation → Final candidate selection

Includes target-specific ELISA and expression data

Standardized data packages

Learn More About Asset List

Annual Subscription Model

PaaS

(Platform as a Service)

Dedicated AI Platform for Antibody Design and Optimization

Exclusive, time-bound access to an AI-driven antibody design platform, integrating antigen–antibody interaction analysis, large-scale computation, and dedicated scientific expertise—operated as an extension of in-house R&D.

Unified subscription for platform, compute, and antibody expertise

HPC-scale computation with dedicated antibody discovery scientists

Pre-defined annual scope for antibody candidate discovery and optimization

Consult on PaaS Model

Annual Subscription Model

PaaS

(Platform as a Service)

Dedicated AI Platform for Antibody Design and Optimization

Unified subscription for platform, compute, and antibody expertise

HPC-scale computation with dedicated antibody discovery scientists

Pre-defined annual scope for antibody candidate discovery and optimization

Consult on PaaS Model

Annual Subscription Model

PaaS

(Platform as a Service)

Dedicated AI Platform for Antibody Design and Optimization

Unified subscription for platform, compute, and antibody expertise

HPC-scale computation with dedicated antibody discovery scientists

Pre-defined annual scope for antibody candidate discovery and optimization

Consult on PaaS Model

고객 맞춤형 발굴형 모델

FFS

(Fee For Service)

타깃 기반 예측 분석으로 최적 항체후보 도출

고객의 타깃 정보와 요구 사항에 맞춰 맞춤형 항체후보를 발굴합니다. AI 기반 분석의 정밀도와 유연한 협업 구조를 바탕으로 R&D 목적과 범위에 따라 다양한 형태로 확장이 가능합니다.

신규 후보 생성 + 고객 논의를 통한 Top 100~200 항체후보 선별

맞춤형 ELISA 검증 및 발현 포함

단계별 과금 구조 기반 협업 유연성 확보

타깃 문의하기

Customized Discovery Model

FFS

(Fee For Service)

Optimal candidates through target-based predictive analysis

AB-ARS® discover customized antibody candidates to protein target and requirements. Based on AI-powered analysis precision and flexible collaboration structures, projects can expand in various forms according to R&D objectives and scope.

Antibody candidate design and stepwise selection tailored to client objectives

Includes target-specific ELISA and expression data

Ensuring collaboration flexibility with a milestone-based fee structure

Customized Discovery Model

FFS

(Fee For Service)

Target-driven predictive analysis for optimal antibody discovery

We discover customized antibody candidates based on the client’s target and requirements. Precise AI-driven analysis and a flexible collaboration model allow scalable engagement across different R&D goals and scopes.

New candidate generation and selection of Top 100–200 antibodies with client input

Includes customized ELISA validation and expression

Flexible collaboration with milestone-based pricing