Bio Co-Scientist
Fast, precise structure prediction
that opens up new possibilities
Fast, precise structure prediction
that opens up new possibilities
With K-Fold, co-developed by KAIST and HITS, quickly predict binding structures and screen your antibody and binder candidates.
Fast, precise structure prediction that opens up new possibilities
With K-Fold, co-developed by KAIST and HITS, quickly predict binding structures and screen your antibody and binder candidates.
Bio Co-Scientist
Fast, precise structure prediction that opens up new possibilities
With K-Fold, co-developed by KAIST and HITS, quickly predict binding structures and screen your antibody and binder candidates.
Bio Co-Scientist
Small Molecule Discovery
K-Fold — the model that predicts structure
HyperLab predicts structures with K-Fold*, a next-generation bio foundation model co-developed by KAIST and HITS.
*K-Fold is a complex-folding model that predicts the 3D shapes biomolecules fold into. Working from sequence alone — no experimental structures like X-ray crystallography required — it predicts not just standalone protein structures but also complexes such as protein–small molecule and protein–nucleic acid, ready to use right away for binding-site analysis and binder or antibody design.
30s
Average inference time per complex
(on an L40S GPU)
MSA-free
Predicts challenging structures such as antibody–antigen complexes and TPDs with high accuracy, without relying on evolutionary information
Apo-to-holo
Precise prediction of challenging targets such as KRAS and Src based on unique principles
Broad research extensibility
Predicts binding structures based on the principles of physical and chemical interactions inherent to molecules. Demonstrates outstanding performance in designing new modalities such as antibodies, TPD, and synthetic binders, where natural evolutionary information is scarce.
Principle-based prediction precision
Goes beyond simple sequence pattern recognition to take into account physical and chemical interactions occurring inside and outside proteins, as well as dynamic structural changes during the binding process, and predicts precise complex structures.
High-speed co-folding in seconds
With high-speed prediction averaging 30 seconds per complex on an NVIDIA L40S GPU, you can quickly narrow down candidates.
Small Molecule Discovery
K-Fold — the model that predicts structure
HyperLab predicts structures with K-Fold*, a next-generation bio foundation model co-developed by KAIST and HITS.
*K-Fold is a complex-folding model that predicts the 3D shapes biomolecules fold into. Working from sequence alone — no experimental structures like X-ray crystallography required — it predicts not just standalone protein structures but also complexes such as protein–small molecule and protein–nucleic acid, ready to use right away for binding-site analysis and binder or antibody design.
30s
Average inference time per complex
(on an L40S GPU)
MSA-free
Predicts challenging structures such as antibody–antigen complexes and TPDs with high accuracy, without relying on evolutionary information
Apo-to-holo
Precise prediction of challenging targets such as KRAS and Src based on unique principles
Broad research extensibility
Predicts binding structures based on the principles of physical and chemical interactions inherent to molecules. Demonstrates outstanding performance in designing new modalities such as antibodies, TPD, and synthetic binders, where natural evolutionary information is scarce.
Principle-based prediction precision
Goes beyond simple sequence pattern recognition to take into account physical and chemical interactions occurring inside and outside proteins, as well as dynamic structural changes during the binding process, and predicts precise complex structures.
High-speed co-folding in seconds
With high-speed prediction averaging 30 seconds per complex on an NVIDIA L40S GPU, you can quickly narrow down candidates.
Small Molecule Discovery
K-Fold — the model that predicts structure
HyperLab predicts structures with K-Fold*, a next-generation bio foundation model co-developed by KAIST and HITS.
*K-Fold is a complex-folding model that predicts the 3D shapes biomolecules fold into. Working from sequence alone — no experimental structures like X-ray crystallography required — it predicts not just standalone protein structures but also complexes such as protein–small molecule and protein–nucleic acid, ready to use right away for binding-site analysis and binder or antibody design.
30s
Average inference time per complex
(on an L40S GPU)
MSA-free
Predicts challenging structures such as antibody–antigen complexes and TPDs with high accuracy, without relying on evolutionary information
Apo-to-holo
Precise prediction of challenging targets such as KRAS and Src based on unique principles
Broad research extensibility
Predicts binding structures based on the principles of physical and chemical interactions inherent to molecules. Demonstrates outstanding performance in designing new modalities such as antibodies, TPD, and synthetic binders, where natural evolutionary information is scarce.
Principle-based prediction precision
Goes beyond simple sequence pattern recognition to take into account physical and chemical interactions occurring inside and outside proteins, as well as dynamic structural changes during the binding process, and predicts precise complex structures.
High-speed co-folding in seconds
With high-speed prediction averaging 30 seconds per complex on an NVIDIA L40S GPU, you can quickly narrow down candidates.
Antibody design
1
Pinpoint the target antigen's binding site
Find the antigen structure through research chat and identify where binding can occur.
2
Pull in a suitable antibody sequence
Retrieve the sequence that gives your design a starting point.
3
Design the CDR backbone with RFdiffusion
Build the backbone of the CDR loops to fit the binding site.
4
Design the sequence with Protein/Ligand MPNN
Generate the amino acid sequence to fill the designed backbone.
5
Predict the antigen–antibody complex with K-Fold
Predict how your designed antibody binds the target antigen, then review it in the 3D Viewer.
6
Screen candidates with ipTM confidence scores
Use antigen–antibody binding confidence as a metric to surface your most promising antibody candidates.
Protein binder design
1
Pinpoint the target protein's binding site
Find the target structure through research chat and identify where the binder should attach.
2
Design the backbone with RFdiffusion
Build the binder's backbone to fit the binding site.
3
Design the sequence with ProteinMPNN
Generate the amino acid sequence for the designed backbone.
4
Predict the target–binder complex with K-Fold
Predict how your designed binder binds the target, then review the complex in the 3D Viewer.
5
Screen candidates by confidence score
Evaluate the target–binder interface with ipTM to surface your most promising candidates.
Use Case in HyperLab
Use cases
Explore real sessions run by researchers, from knowledge graph exploration to structure prediction, omics analysis, and data analysis.
Explore real sessions run by researchers, from knowledge graph exploration to structure prediction, omics analysis, and data analysis.
KRAS G12D minibinder design
KRAS G12D is a key driver mutation in pancreatic and colorectal cancers.
Learn more
Vepdegestrant PROTAC ternary complex modeling
A step-by-step study of vepdegestrant (ARV-471), the first FDA-approved heterobifunctional PROTAC, spanning literature and clinical research through ternary-complex structure prediction.
Learn more
EGFR 3-phenylpyrazole SAR study
SAR of 3-phenylpyrazole EGFR inhibitors, from ChEMBL analogues to predicted binding poses and Bayesian activity modeling.
Learn more
How does it differ from AlphaFold3?
The approach and principles are different.
While existing co-folding models rely heavily on evolutionary statistics from multiple sequence alignment (MSA), K-Fold predicts binding structures based on the physical and structural features of the molecules themselves, without MSA.
In particular, unlike existing approaches that learn only the final static structure, it learns structural changes that occur during the binding process. As a result, it demonstrates robust generalization performance for next-generation drug modalities such as antibodies and TPD, where natural evolutionary information is scarce, and for protein targets such as kinases and GPCRs, where structural changes are important.
In addition, with no MSA search bottleneck and the use of optimized computational kernels, ultrafast prediction averaging 30 seconds per complex is possible. (on an L40S GPU)
Can the accuracy be trusted?
Reliability is verified through sophisticated benchmark evaluations and multifaceted confidence metrics.
K-Fold shows top-level accuracy not only on representative complex structure benchmarks, but also in predictions of antigen–antibody complexes, TPD ternary complexes, and structural changes in kinases and GPCRs.
In addition, it provides confidence metrics for complexes and binding interfaces, such as pLDDT, PAE, pTM, and ipTM, along with prediction results, allowing researchers to critically validate the prediction results.
Do I need other tools to design with K-Fold?
No. Strategy research, sequence retrieval, backbone and sequence design, and structure prediction all run in one place, end to end — no preparing sequences by hand, no moving results between tools.
Get the HyperLab
your research needs.
From Bio Co-Scientist, which researches with you in conversation,
to Small Molecule Discovery for molecular design and prediction,
choose the Lab space that fits your research.
—
AI drug discovery Platform
Bio Co-Scientist
HITS Inc.
CEO : Woo Youn Kim
Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu, Seoul, Republic of Korea
Tel : +82-2-6953-0317
E-mail :
Company registration number : 260-88-01818
Mail order sales registration number : 2026-서울강남-06240
© HITS Inc. All rights reserved.
FAQ
Can I run analyses without any coding experience?
Yes — no coding required.
You don’t need to know Linux, R, or Python.
Simply upload your data through the web interface and request analysis strategies via AI chat.
From execution to interpretation, the entire process is handled automatically.
How is this different from ChatGPT or Gemini?
Will my data be used to train AI models?
Who owns the figures generated on this platform?
How is customer data protected?
Get the HyperLab
your research needs.
From Bio Co-Scientist, which researches with you in conversation, to Small Molecule Discovery for molecular design and prediction, choose the Lab space that fits your research.
HITS Inc.
CEO : Woo Youn Kim
Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu, Seoul, Republic of Korea
Tel : +82-2-6953-0317
E-mail :
Company registration number : 260-88-01818
Mail order sales registration number : 2026-서울강남-06240
© HITS Inc. All rights reserved.
Get the HyperLab
your research needs.
From Bio Co-Scientist, which researches with you in conversation, to Small Molecule Discovery for molecular design and prediction, choose the Lab space that fits your research.
HITS Inc.
CEO : Woo Youn Kim
Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu, Seoul, Republic of Korea
Tel : +82-2-6953-0317
E-mail :
Company registration number : 260-88-01818
Mail order sales registration number : 2026-서울강남-06240
© HITS Inc. All rights reserved.
Get the HyperLab
your research needs.
From Bio Co-Scientist, which researches with you in conversation,
to Small Molecule Discovery for molecular design and prediction,
choose the Lab space that fits your research.
HITS Inc.
CEO : Woo Youn Kim
Address : 8F, 28, Teheran-ro 4-gil, Gangnam-gu, Seoul, Republic of Korea
Tel : +82-2-6953-0317
E-mail :
Company registration number : 260-88-01818
Mail order sales registration number : 2026-서울강남-06240
© HITS Inc. All rights reserved.
Can I run analyses without any coding experience?
Yes — no coding required.
You don’t need to know Linux, R, or Python.
Simply upload your data through the web interface and request analysis strategies via AI chat.
From execution to interpretation, the entire process is handled automatically.
How is this different from ChatGPT or Gemini?
Will my data be used to train AI models?
Who owns the figures generated on this platform?
How is customer data protected?
Antibody design
Antibody design
1
Pinpoint the target antigen's binding site
Find the antigen structure through research chat and identify where binding can occur.
2
Pull in a suitable antibody sequence
Retrieve the sequence that gives your design a starting point.
3
Design the CDR backbone with RFdiffusion
Build the backbone of the CDR loops to fit the binding site.
4
Design the sequence with ProteinMPNN
Generate the amino acid sequence to fill the designed backbone.
5
Predict the antigen–antibody complex with K-Fold
Predict how your designed antibody binds the target antigen, then review it in the 3D Viewer.
6
Screen candidates with ipTM confidence scores
Use antigen–antibody binding confidence as a metric to surface your most promising antibody candidates.
Protein binder design
1
Pinpoint the target protein's binding site
Find the target structure through research chat and identify where the binder should attach.
2
Design the backbone with RFdiffusion
Build the binder's backbone to fit the binding site.
3
Design the sequence with ProteinMPNN
Generate the amino acid sequence for the designed backbone.
4
Predict the target–binder complex with K-Fold
Predict how your designed binder binds the target, then review the complex in the 3D Viewer.
5
Screen candidates by confidence score
Evaluate the target–binder interface with ipTM to surface your most promising candidates.
Your entire design workflow,
in one flow
Your entire design workflow,
in one flow
Antibody design
1
Pinpoint the target antigen's binding site
Find the antigen structure through research chat and identify where binding can occur.
2
Pull in a suitable antibody sequence
Retrieve the sequence that gives your design a starting point.
3
Design the CDR backbone with RFdiffusion
Build the backbone of the CDR loops to fit the binding site.
4
Design the sequence with ProteinMPNN
Generate the amino acid sequence to fill the designed backbone.
5
Predict the antigen–antibody complex with K-Fold
Predict how your designed antibody binds the target antigen, then review it in the 3D Viewer.
6
Screen candidates with ipTM confidence scores
Use antigen–antibody binding confidence as a metric to surface your most promising antibody candidates.
Protein binder design
1
Pinpoint the target protein's binding site
Find the target structure through research chat and identify where the binder should attach.
2
Design the backbone with RFdiffusion
Build the binder's backbone to fit the binding site.
3
Design the sequence with ProteinMPNN
Generate the amino acid sequence for the designed backbone.
4
Predict the target–binder complex with K-Fold
Predict how your designed binder binds the target, then review the complex in the 3D Viewer.
5
Screen candidates by confidence score
Evaluate the target–binder interface with ipTM to surface your most promising candidates.