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Latent Labs It was founded by members of the DeepMind AlphaFold team. AI Biotechnology PlatformIt utilizes flagship AI tools. Latent-XIt completely revolutionizes the design process of protein binders and molecular drugs, supports custom protein targets and binding sites, and generates and screens high-quality new molecular structures with one click.The platform boasts significant advantages such as experimental verification, free access, and extremely high speed and efficiency.It is widely applicable to biopharmaceutical companies, research institutions, and AI drug development developers.Latent Labs is leading a new era of AI-driven molecular biology and protein drug discovery.
Latent Labs（Official websiteAs a pioneering platform for AI content detection and molecular design, it was created by top teams including the co-developers of AlphaFold 2 and scientists from DeepMind, propelling drug design and biological research into a new era.
Latent Labs' main functions
In 2025, Latent Labs will launch its flagship platform. Latent-XThe goal is to make organisms "programmable," significantly improving the efficiency of molecular design in fields such as protein drugs, enzyme optimization, and genetic engineering.
1. AI-designed precise protein binding agents
With Latent-X at its core, the platform uses powerful AI technology to directly synthesize protein-binding agents at the atomic level.Supports uploading protein targets and custom epitopes.It can output molecular structures with one click and filter them by index scoring.
| Function | Latent Labs Description | Related Links |
|---|---|---|
| Supported design types | Metacyclic peptides, Mini-Binders | Latent-X Introduction |
| Binding affinity performance | Metacyclic peptides: mono/micromolar; Mini-Binder: picomolar | Experiment Report |
| User operation threshold | No programming required, full web-based workflow | Platform entrance |
| Structural Prediction and Visualization | It features built-in overlay, scoring, and image display. | Demo video |
2. Extremely high hit rate and experimental verification
- Metacyclic peptide design three target hit ratesGundam 91-100%
- Mini-Binder five-target hit rate10-64%
- The strongest binding affinity is superior to similar AI tools (such as RFdiffusion and AlphaProteo).
3. High-speed and efficient online design experience
Leveraging cutting-edge AI models and high-performance cloud computing,The generation efficiency is 10 times higher than that of traditional models.Molecular generation and structure determination are completed within seconds.
Latent Labs Pricing & Solutions
Platform LaunchRegister and use immediatelyA free trial layer lowers the barrier to AI drug design.
| Scheme type | illustrate | cost | Applicable users |
|---|---|---|---|
| Free Plan | Register and use immediately; daily credit limit refreshed automatically. | free | All users, startups, academic institutions |
| Enterprise/Business Edition | Higher computing power and usage, dedicated services | Contact the authorities for discussion | Pharmaceutical R&D teams and users with large demands |
| API Access Plan | The plan includes future support for automated integration. | Not yet open (subject to commercial agreement). | Developers, highly automated scenarios |
Please refer to the Latent Labs pricing FAQ for details.
How to use Latent Labs
- Go to platform to register/login
- Upload protein target structure data
- Specify the epitope/hotspot.
- Choose either macrocyclic peptide or mini-binder type.
- One-click generation of dozens to hundreds of binder molecules
- Automatic sorting and scoring, selecting high-scoring solutions.
- Export sequences and 3D structures (non-exclusive license)
The platform greatly simplifies molecular design and lowers the barrier to AI protein design.
| step | illustrate | Support Page |
|---|---|---|
| Platform Registration | Free registration, one-click login | Entrance |
| Data Upload | Supports multiple protein structure files | Official website guide |
| Target and hot zone definition | Freely defined binding sites | Built-in guide |
| Results intelligent sorting/filtering | Optimization based on experimental scoring | Results Page |
| Structure visualization and download | 3D visualization, supports export | Platform Interface |
Latent Labs' target audience
- Innovative biopharmaceutical companiesShorten the new drug development cycle and reduce risks
- Biotechnology startup teamNo need to build your own AI team to use cutting-edge tools
- Universities and research institutionsLow barriers to entry promote protein research and experimental validation
- AI/Pharmaceutical Industry DevelopersIn the future, APIs can be integrated into automated processes.
The platform's adaptability and future API support are particularly important for the integration and innovation of numerous disciplines.
The team and technology behind Latent Labs
The team members come from DeepMind AlphaFold 1/2/3, Microsoft, Stability AI, and others. CEO Simon Kohl said, "We are making biological research as engineerable as semiconductors."“
| Position | Representatives | Background & Experience |
|---|---|---|
| Founder & CEO | Simon Kohl | AlphaFold2 creators and former DeepMind team |
| Technical backbone | Alex Bridgland | AlphaFold full-generation product development |
| Biological Algorithm Engineering | Dave Yuan | Zymergen, Mammoth Biosciences |
| Business/Platform Manager | Martha Carruthers | Pear Therapeutics |
See moreTeam Page。
Platform's unique advantages and future development
Latent Labs It sets new benchmarks in experimental validation, model innovation, rapid AI generation, and free access. In the future, it will support more molecular models such as nanobody and antibody-based drugs, contributing to drug diversity.
| category | Key features |
|---|---|
| Academic/Experimental Adaptability | All AI designs have been experimentally validated and demonstrate superior user-friendliness compared to traditional designs. |
| Interface and experience | No local deployment required, zero-code web operation |
| Data security/compliance | GDPR standards ensure strict data confidentiality. |
| Future expansion | Will support antibody and API automation |
Frequently Asked Questions
1. How does the platform ensure the validity of the generated structure?
All AI design structures use the same approach as those in the paper.in silico scoring systemBased on multi-target experimental verification, it is recommended that "PASS" subjects be used for experiments. See details.Technical Report。
2. How are user data and privacy protected?
User-generated protein sequences and structuresNon-exclusive ownership by usersThe data is strictly protected in accordance with GDPR, used only for platform statistics, and will never be leaked. See details.Privacy Policy。
3. What new features will emerge in the future?
The model will continue to iterate and will support nanobody, antibody, and API integration in the future, facilitating automated drug development.
As a key engine driving the future of programmable biology, Latent Labs injects unprecedented efficiency and innovation into protein design and drug discovery, and is open to the scientific and industrial communities. The era of "AI-driven biology" has arrived.
data statistics
Data evaluation
aboutLatent LabsSpecial Announcement
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