Protein Structure Prediction

Use AI to predict protein 3D structures, accelerating drug discovery and protein design.

Business impact

• Time to market — Speeds up drug development by shortening protein structure prediction timelines
• R&D efficiency — Enhances research productivity by automating complex protein modeling tasks
• Drug discovery success rate — Improves candidate selection accuracy through better structural insights
• Product Quality — Enables design of more stable and functional proteins for biotech applications
• Cost reduction — Lowers experimental screening expenses by computationally narrowing candidates

Data requirements

• Protein sequence databases (Text) — Provide amino acid sequences as input for structure prediction models
• 3D protein structure repositories (Image) — Offer reference structures for training and validating AI models
• Binding affinity datasets (Numeric) — Enable prediction of protein-ligand interactions to assess drug potency
• Genetic and environmental data (Structured) — Support discovery of stable proteins from diverse biological contexts
• Experimental assay results (Numeric) — Validate AI predictions and refine model accuracy iteratively

AI methods and techniques

• Predictive AI — Used to forecast protein 3D conformations from amino acid sequences
• Generative AI — Generates novel protein designs with desired structural properties
• Agentic AI — Automates iterative design and validation cycles in protein engineering

AI models and model families

AlphaFold, AlphaFold 2, RoseTTAFold, Boltz-1x model, GPT-4o

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Durham University, Isomorphic Labs, Polytechnique F Lausanne EPFL, SandboxAQ, University Washington.