AlphaFold

AlphaFold is a groundbreaking AI system developed by Google DeepMind to accurately predict the three-dimensional structures of proteins from their amino acid sequences. This technology solved a decades-old biological grand challenge and provides millions of structural predictions freely to the global scientific community. Through the AlphaFold Protein Structure Database, maintained jointly with EMBL-EBI, researchers worldwide gain instant access to these predictions.

The system utilizes deep learning and neural network architectures trained on known protein structures from the Protein Data Bank (PDB). AlphaFold incorporates evolutionary relationships and physical spatial geometry to output highly accurate 3D models. Beyond individual proteins, advanced iterations like AlphaFold 3 can also model complex interactions involving DNA, RNA, ligands, and ions.

Its applications span from fundamental academic biology to targeted drug discovery. Scientists leverage AlphaFold to comprehend cellular machinery, decipher disease mechanisms, and design novel enzymes for environmental sustainability. The ability to deploy the model locally or run it via cloud platforms makes it a versatile core tool for academic institutes and commercial enterprises alike.

• 3D Structure Prediction — Generates highly accurate three-dimensional molecular models directly from primary amino acid sequences near atomic accuracy.
• AlphaFold Database — Provides open access to hundreds of millions of predicted protein structures across key model organisms and pathogens.
• Complex & Interaction Modeling — Enables the prediction of molecular interactions between proteins, nucleic acids, ions, and small molecules.

Designed for biochemists, molecular biologists, bioinformaticians, and pharmaceutical researchers across academia and industry. It is ideal for research teams analyzing protein function or engaging in modern drug design.

A biochemist is studying an uncharacterized enzyme from an extremophilic bacterium to understand its thermal stability. Instead of spending months on complex X-ray crystallography experiments, the researcher submits the amino acid sequence to AlphaFold or searches the database. Within a short time, a precise 3D structural model with confidence metrics is generated, allowing the scientist to design targeted laboratory mutations immediately.

• Designed for biochemists, molecular biologists, bioinformaticians, and pharmaceutical researchers across academia and industry. It is ideal for research teams analyzing protein function or engaging in modern drug design.
• A biochemist is studying an uncharacterized enzyme from an extremophilic bacterium to understand its thermal stability.
• 3D Structure Prediction: Generates highly accurate three-dimensional molecular models directly from primary amino acid sequences near atomic accuracy.
• AlphaFold Database: Provides open access to hundreds of millions of predicted protein structures across key model organisms and pathogens.
• Complex & Interaction Modeling: Enables the prediction of molecular interactions between proteins, nucleic acids, ions, and small molecules.

Do not rely solely on AlphaFold when immediate real-time computations are required without compute infrastructure, or where regulatory standards strictly require physical experimental validation. It is also less suitable for intrinsically disordered protein regions.

• Cradle — Paid · HQ: Amsterdam, Netherlands · GDPR / EU
• ESMFold — Open Source · HQ: Menlo Park, United States · GDPR / EU
• Owkin — Paid · HQ: Paris, France · Unclear
• RoseTTAFold — Open Source · HQ: Seattle, United States · US Cloud
• Schrödinger Maestro — Paid · HQ: New York, United States · Unclear