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AI-enabled precision medicine for data-driven healthcare decisions.

Frontier generative AI for programming biology across sequence, structure, and function.

EvolutionaryScale's ESM3 represents the 2026 gold standard for generative biological modeling, evolving beyond simple protein folding to a complete multimodal 'biological programming' environment. Founded by the original Meta FAIR ESM team, the platform centers on the ESM3 98B parameter model, which was trained on 2.78 billion proteins and 771 trillion tokens. Unlike previous models that only predicted structure from sequence, ESM3 is natively multimodal, allowing researchers to prompt the model with partial structures, functional motifs, or sequence constraints simultaneously. By 2026, it has become the primary enterprise alternative to AlphaFold3, offering a unique capability to generate entirely novel, functional proteins (de novo design) that do not exist in nature, such as synthetic fluorescent proteins. The architecture utilizes a sophisticated geometric transformer that tokenizes 3D coordinates alongside amino acids, enabling atom-level precision. This 'programmable biology' approach allows for the rapid engineering of enzymes, antibodies, and therapeutic proteins with specific binding affinities and thermal stabilities, significantly compressing the R&D cycle for pharmaceutical and industrial biotechnology applications.
EvolutionaryScale's ESM3 represents the 2026 gold standard for generative biological modeling, evolving beyond simple protein folding to a complete multimodal 'biological programming' environment.
Explore all tools that specialize in tertiary structure prediction. This domain focus ensures EvolutionaryScale (ESM3) delivers optimized results for this specific requirement.
Simultaneously reason across sequence, structure, and function by masking and filling across all three modalities in a single forward pass.
Uses a VQ-VAE to compress 3D atomic coordinates into discrete tokens that the transformer can process alongside text-like amino acid sequences.
Uses the model's internal log-likelihood to predict how mutations affect protein stability and function without experimental labels.
Capability to generate side-chain orientations and backbone coordinates with sub-angstrom precision.
Can take a partial structure and 'infill' the missing sequence, or take a sequence and 'infill' the structural coordinates.
Integration of functional reasoning where the model can explain biological constraints (experimental 2026 feature).
Weights biased by evolutionary conserved patterns derived from billions of years of biological data.
Obtain API credentials via EvolutionaryScale Forge or AWS Marketplace.
Install the 'esm' Python library via pip (pip install esm).
Authenticate using the 'esm.sdk.client' with your provided API key.
Load the desired model size (ESM3-Open-1.4B for local dev or ESM3-98B for enterprise).
Prepare input data as an 'ESMProtein' object containing sequence or structural templates.
Define generation constraints (e.g., fixing specific active site residues).
Run the 'generate' function to sample new sequences or structures.
Perform folding validation using the integrated 'ESMFold' or ESM3 structure tracks.
Analyze output confidence metrics (pLDDT and PAE scores).
Export structural candidates to PDB format for downstream molecular dynamics simulation.
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Verified feedback from other users.
"Highly praised for its generative capabilities compared to AlphaFold's purely predictive nature. Researchers value the open-weight options for smaller models."
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