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AI-powered software

As the perfect partner for high-throughput single-cell sorters, AbFinder      leverages next-generation sequencing (NGS) and AI-driven models to optimize antibody discovery. Supports Any Single B-Cell Sorter or Method.

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What is AbFinder   ?
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AbFinder     is a user-friendly analysis tool specifically designed for single-cell BCR sequencing data. It simplifies the process of interpreting and understanding the complex data generated from single-cell BCR sequencing experiments, helping lab scientists accelerate their research and make informed decisions.

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  • Accelerating Antibody Candidate Selection with AI

The AbFinder     platform integrates advanced bioinformatics and AI-driven tools to accelerate NGS sequence analysis and streamline rational selection of antibody candidates.

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  • Tailored for Lab Scientists

Available in Cloud (SaaS) and On-Premises deployments, AbFinder    enables scientists to rapidly access, analyze, and interpret antibody sequences with minimal effort.

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  • From Repertoire to Hit List

AbFinder’s AI-powered workflows enable automated identification of candidate antibody sequences from large-scale NGS repertoires, assess liabilities, and predict developability potential.

  • AI-Powered Insights

The system continuously learns from large datasets, improving selection accuracy over time.

  • Faster Data Processing

Algorithms optimize data interpretation, significantly reducing manual effort.

Sequence analysis

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01

Data Quality Control

  • Fast processing: process 10X single-cell barcoded sequences for IgG and FASTQ data from Illumina for nanobody (VHH) .

  • Filter abnormal sequences, including: frameshift mutations, stop codons, N-containing reads, CDR3 missing initial cysteine,  incomplete sequences.

  • Perform VH-VL pairing with removal of orphan chains and correction of  redundant sequences.

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Visualization

  • Visualize and explore information about the BCR repertoire with intuitive charts for V gene usage, CDR length distribution, Clonotype size etc. Graphically presented paired antibodies from full repertoire and from selected panel with phylogram.

02

Annotation

  • Numbering schemes and annotation using IMGT database. 

  • Annotate CDRs, V-gene, J gene of VH, VL respectively.

  • Present features of VH V gene somatic hyper mutation (SHM), CDR3 length etc.

  • Support diverse germline database, including mouse, rabbit,  human.

  • Identify liability in VH CDRs

04

Cluster & Compare

  • Group antibodies based on their sequence similarity,  CDRs, clonotypes, and  structural features etc.

  • AI-driven hit expansion from clusters where known binders present to expand and diversify the candidate pool.

Docking analysis

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01     AI-enhanced 3D Structure Prediction

  • Uses homology modeling and AI methods to predict 3D antibody structures with minimal error margins.

02     Structure Optimization

  •  Optimizes key regions of antibody structures for higher accuracy and stability.

  • Applicable to proteins capable of forming a 3D structure, typically >50 amino acids.

03     Antigen* Antibody Docking Simulation

  • Simulates binding interactions between antibodies and antigens.

  • Predicts binding affinity, providing a crucial basis for antibody optimization.

  • * Antigen  refer to protein capable forming 3D structure, typically >50 AA.

Features

Comprehensive BCR Analysis

  • Identify clonal expansion and somatic hypermutation.

  • Quantify repertoire diversity.

Annotation

  • Automatically annotate CDRs, V-genes, and J-genes of VH and VL.

  • Detect liabilities in CDR regions.

  • Supports diverse germline databases including human, mouse, and rabbit.

Security & Accessibility

  • Protect sensitive data with enterprise-grade encryption.

  • Available in Cloud (SaaS) and On-Premises.

Visualization & Comparison

  • Explore and compare BCR datasets.

  • Graphically analyze V-gene usage, CDR length distribution, and clonotype diversity.

  • Leverage phylogenetic analysis to group similar antibodies.

Automated Workflow & Usability

  • Automates repetitive tasks with a user-friendly interface.

  • AI-powered hit selection ensures optimal candidate identification.

Applications in AI-Powered Antibody Discovery

Through accurate analysis and annotation, key features, liabilities, and phylogenetic relationships are presented to assist researches in efficiently selecting the optimal and most diverse panel of hits for downstream expression and characterization.

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