Biomedical Co-Scientist
Biomedical Co-Scientist
In HyperLab's Biomedical Co-Scientist Lab space,
research, design, and structure validation happen in a single flow.
Start your research with one line of chat.
01
Turn your idea
into design results
Generate multiple candidates from a single research idea, compare them side by side, and steer toward the direction you want
— cutting research time at every step.
02
Hundreds of structure predictions, faster
K-Fold runs hundreds of structure predictions at once and returns results up to 30× faster than AlphaFold3
— saving you time and cost.
03
Search, predict, and analyze — all in one place
Run your entire workflow through conversation: from literature search to structure prediction to interpreting results, all in one uninterrupted flow.
K-Fold Model
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.
Broad research applications
K-Fold's prediction algorithm draws on a protein's own physical and chemical properties instead of relying on evolutionary information — so it works even where that data is scarce, as with synthetic binders or molecular glues.
Proven accuracy
In predicting the 3D structures of biomolecular complexes
— proteins, small molecules, and nucleic acids
— K-Fold reaches accuracy close to AlphaFold3.
Faster results
Up to ~30× faster than AlphaFold3, so you can narrow down candidates quickly.
30×
Faster than AlphaFold3
AF3-level
Complex-structure accuracy
No MSA
Predicts from sequence alone
Workflow
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.
Every step happens in one place
— no shuffling sequences or results between tools.
Experience protein and antibody design research without the friction.
FAQ
We’ve got the answers
How is K-Fold different from AlphaFold3?
The approach.
AlphaFold3-style models lean heavily on evolutionary information (MSA), while K-Fold predicts binding structures from a protein's own features — no MSA needed.
And unlike models that learn only the final, post-binding structure, K-Fold is trained on the structural changes that happen before and after binding, which makes its predictions more accurate.
That's why it generalizes well to tricky cases like synthetic binders and molecular glues, where evolutionary data is scarce — at up to ~30× the speed of AlphaFold3.
Can I trust the accuracy?
Yes.
In a government evaluation, the 2B-class K-Fold model scored close to AlphaFold3 on 3D structure prediction for molecular complexes.
It also reports pLDDT, PAE, pTM, and ipTM, so you can critically validate every prediction.
What molecules and complexes can it predict?
Protein–protein and protein–small molecule complexes, nucleic acids (DNA/RNA), and
ions — and it predicts complexes that combine all of these in a single run.
What are K-Fold's input and output formats?
Inputs: protein, DNA, and RNA sequences, small-molecule SMILES strings, and ion specifications.
Outputs: CIF and PDB formats together, returning 5 conformers per structure prediction by default.
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.
Is it fast enough for real research?
Up to 30× faster than AlphaFold3. That's fast enough to power virtual screening workflows in the lab and in industry — running repeated predictions to narrow down candidates quickly.
Is my uploaded research data used to train the AI?
No. Your data and analysis results are never used for model training.
How is my data protected?
Production and development servers are physically separated. Access to production servers is secured with OTP and a VPC-based private network, with no external access.
Ready to revolutionize
Your drug discovery process?
Sign up with your email and start using HyperLab right now
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
© HITS Inc. All rights reserved.
1. Turn your idea into design results
Generate multiple candidates from a single research idea, compare them side by side, and steer toward the direction you want — cutting research time at every step.
2. Hundreds of structure predictions, faster
K-Fold runs hundreds of structure predictions at once and returns results up to 30× faster than AlphaFold — saving you time and cost.
3. Search, predict, and analyze
— all in one place
Run your entire workflow through conversation: from literature search to structure prediction to interpreting results, all in one uninterrupted flow.
Biomedical Co-Scientist
Biomedical Co-Scientist
In HyperLab's
Biomedical Co-Scientist Lab space,
research, design, and structure validation happen in a single flow.
Start your research with one line of chat.
Biomedical Co-Scientist
Biomedical Co-Scientist
In HyperLab's
Biomedical Co-Scientist Lab space,
research, design, and structure validation happen in a single flow.
Start your research with one line of chat.
K-Fold Model
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.
1. Broad research applications
K-Fold's prediction algorithm draws on a protein's own physical and chemical properties instead of relying on evolutionary information — so it works even where that data is scarce, as with synthetic binders or molecular glues.
2. Proven accuracy
In predicting the 3D structures of biomolecular complexes
— proteins, small molecules, and nucleic acids
— K-Fold reaches accuracy close to AlphaFold3.
3. Faster results
Up to ~30× faster than AlphaFold3, so you can narrow down candidates quickly.
K-Fold
Workflow
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.
Every step happens in one place
— no shuffling sequences or results between tools.
Experience protein and antibody design research without the friction.
Every step happens in one place
— no shuffling sequences or results between tools.
Experience protein and antibody design research without the friction.
We’ve got the answers
FAQ
How is K-Fold different from AlphaFold3?
The approach.
AlphaFold3-style models lean heavily on evolutionary information (MSA), while K-Fold predicts binding structures from a protein's own features — no MSA needed.
And unlike models that learn only the final, post-binding structure, K-Fold is trained on the structural changes that happen before and after binding, which makes its predictions more accurate.
That's why it generalizes well to tricky cases like synthetic binders and molecular glues, where evolutionary data is scarce — at up to ~30× the speed of AlphaFold3.
Can I trust the accuracy?
Yes.
In a government evaluation, the 2B-class K-Fold model scored close to AlphaFold3 on 3D structure prediction for molecular complexes.
It also reports pLDDT, PAE, pTM, and ipTM, so you can critically validate every prediction.
What molecules and complexes can it predict?
Protein–protein and protein–small molecule complexes, nucleic acids (DNA/RNA), and
ions — and it predicts complexes that combine all of these in a single run.
What are K-Fold's input and output formats?
Inputs: protein, DNA, and RNA sequences, small-molecule SMILES strings, and ion specifications.
Outputs: CIF and PDB formats together, returning 5 conformers per structure prediction by default.
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.
Is it fast enough for real research?
Up to 30× faster than AlphaFold3. That's fast enough to power virtual screening workflows in the lab and in industry — running repeated predictions to narrow down candidates quickly.
Is my uploaded research data used to train the AI?
No. Your data and analysis results are never used for model training.
How is my data protected?
Production and development servers are physically separated. Access to production servers is secured with OTP and a VPC-based private network, with no external access.
Ready to
revolutionize your
drug discovery process?
Sign up with your email and start using HyperLab right now
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
HITS Inc.
CEO : Woo Youn Kim
© HITS Inc. All rights reserved.
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
HITS Inc.
CEO : Woo Youn Kim
© HITS Inc. All rights reserved.
Ready to revolutionize
Your drug discovery process?
Sign up with your email and start using HyperLab right now
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
© HITS Inc. All rights reserved.
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
© HITS Inc. All rights reserved.
Biomedical Co-Scientist
Biomedical Co-Scientist
In HyperLab's Biomedical Co-Scientist Lab space,
research, design, and structure validation happen in a single flow.
Start your research with one line of chat.
1. Turn your idea into design results
Generate multiple candidates from a single research idea, compare them side by side, and steer toward the direction you want — cutting research time at every step.
2. Hundreds of structure predictions, faster
K-Fold runs hundreds of structure predictions at once and returns results up to 30× faster than AlphaFold — saving you time and cost.
3. Search, predict, and analyze — all in one place
Run your entire workflow through conversation: from literature search to structure prediction to interpreting results, all in one uninterrupted flow.
K-Fold — the model that predicts structure
K-Fold Model
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.
1. Broad research applications
K-Fold's prediction algorithm draws on a protein's own physical and chemical properties instead of relying on evolutionary information — so it works even where that data is scarce, as with synthetic binders or molecular glues.
2. Proven accuracy
In predicting the 3D structures of biomolecular complexes
— proteins, small molecules, and nucleic acids
— K-Fold reaches accuracy close to AlphaFold3.
3. Faster results
Up to ~30× faster than AlphaFold3, so you can narrow down candidates quickly.
K-Fold
Your entire design workflow, in one flow
Workflow
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.
Every step happens in one place
— no shuffling sequences or results between tools.
Experience protein and antibody design research without the friction.
Every step happens in one place
— no shuffling sequences or results between tools.
Experience protein and antibody design research without the friction.
How is K-Fold different from AlphaFold3?
The approach.
AlphaFold3-style models lean heavily on evolutionary information (MSA), while K-Fold predicts binding structures from a protein's own features — no MSA needed.
And unlike models that learn only the final, post-binding structure, K-Fold is trained on the structural changes that happen before and after binding, which makes its predictions more accurate.
That's why it generalizes well to tricky cases like synthetic binders and molecular glues, where evolutionary data is scarce — at up to ~30× the speed of AlphaFold3.
Can I trust the accuracy?
Yes.
In a government evaluation, the 2B-class K-Fold model scored close to AlphaFold3 on 3D structure prediction for molecular complexes.
It also reports pLDDT, PAE, pTM, and ipTM, so you can critically validate every prediction.
What molecules and complexes can it predict?
Protein–protein and protein–small molecule complexes, nucleic acids (DNA/RNA), and
ions — and it predicts complexes that combine all of these in a single run.
What are K-Fold's input and output formats?
Inputs: protein, DNA, and RNA sequences, small-molecule SMILES strings, and ion specifications.
Outputs: CIF and PDB formats together, returning 5 conformers per structure prediction by default.
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.
Is it fast enough for real research?
Up to 30× faster than AlphaFold3. That's fast enough to power virtual screening workflows in the lab and in industry — running repeated predictions to narrow down candidates quickly.
Is my uploaded research data used to train the AI?
No. Your data and analysis results are never used for model training.
How is my data protected?
Production and development servers are physically separated. Access to production servers is secured with OTP and a VPC-based private network, with no external access.
We’ve got the answers
FAQ
30×
Faster than AlphaFold3
AF3-level
Complex-structure accuracy
No MSA
Predicts from sequence alone