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google-deepmind avatar

google-deepmind/alphafold3

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Alphafold3

AlphaFold3 is a biomolecular structure prediction model and bioinformatics structural analysis tool. It uses a deep learning system to predict the three-dimensional shapes of proteins, DNA, RNA, and ligands.

The system functions as a diffusion-based protein folding model that predicts the spatial coordinates of biomolecular atoms and interactions. It utilizes a GPU-accelerated inference pipeline to process genetic sequences and structural templates for molecular modeling.

The project covers structural bioinformatics analysis and protein interaction modeling to determine the physical arrangement of atoms within complex biomolecular assemblies. It incorporates a computational biology workflow that manages data pipelines and model inference.

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Features

  • Structure Prediction Models - Predicts the 3D shapes of biomolecular interactions using a deep learning inference pipeline.
  • Diffusion-Based 3D Generators - Uses a denoising diffusion process to generate 3D atomic coordinates and structured molecular shapes.
  • GPU-Accelerated Inference - Utilizes GPU acceleration to compute structural predictions from complex biomolecular input data.
  • Diffusion Models - Generative model using a diffusion architecture to predict spatial coordinates of biomolecular atoms.
  • Biology and Bioinformatics - Provides a computational biology workflow combining data pipelines and GPU models to analyze genetic sequences.
  • Protein Interaction Modeling - Simulates the binding of proteins with other molecules or ligands to support drug discovery.
  • Bioinformatics Toolkits - Provides a toolkit for computing atomic arrangements and folding patterns of macromolecular complexes.
  • Structural Bioinformatics Analysis - Uses deep learning to determine the physical arrangement of atoms within complex biomolecular assemblies.
  • Attention-Based Pair Representations - Implements a transformer-based architecture to update spatial relationships between residue pairs in a 2D grid.
  • Coordinate-Free Representations - Predicts molecular geometry using local rotation and translation frames to ensure rotational invariance.
  • Data Pipelines - Manages workflows that chain genetic searches and template processing to prepare data for structural prediction.
  • Multi-Track Tokenization - Represents diverse biomolecular entities as distinct token streams to handle different chemical identities.
  • Template-Based Structure Retrieval - Executes genetic search and sequence alignment on CPUs to find known structures for use as spatial anchors.
7,613 stars·1,113 forks·Python·other·20 views

Star history

Star history chart for google-deepmind/alphafold3Star history chart for google-deepmind/alphafold3

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does google-deepmind/alphafold3 do?

AlphaFold3 is a biomolecular structure prediction model and bioinformatics structural analysis tool. It uses a deep learning system to predict the three-dimensional shapes of proteins, DNA, RNA, and ligands.

What are the main features of google-deepmind/alphafold3?

The main features of google-deepmind/alphafold3 are: Structure Prediction Models, Diffusion-Based 3D Generators, GPU-Accelerated Inference, Diffusion Models, Biology and Bioinformatics, Protein Interaction Modeling, Bioinformatics Toolkits, Structural Bioinformatics Analysis.

Which projects share features with google-deepmind/alphafold3?

Projects with overlapping indexed features include: google-deepmind/alphafold — AlphaFold is a deep learning biology tool and structural bioinformatic pipeline designed to predict the… jwohlwend/boltz — Boltz is a deep learning molecular modeler and biomolecular structure prediction system. It uses neural network… k-dense-ai/claude-scientific-skills — This project is a scientific agent framework and workflow orchestrator designed to extend large language models with… biopython/biopython — Biopython is a bioinformatics library for Python providing tools to parse, manipulate, and analyze biological… bentoml/bentoml — BentoML is a machine learning model serving framework and GPU-accelerated inference server designed to package,… deepseek-ai/flashmla — FlashMLA is an LLM attention kernel library and inference acceleration library providing a collection of…

Projects sharing features with Alphafold3

These projects share indexed features with Alphafold3. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • google-deepmind/alphafoldgoogle-deepmind avatar

    google-deepmind/alphafold

    14,681View on GitHub↗

    AlphaFold is a deep learning biology tool and structural bioinformatic pipeline designed to predict the three-dimensional shapes of proteins from their amino acid sequences. It functions as a machine learning system capable of generating 3D molecular models for both monomeric proteins and multimeric protein complexes, including homomers and heteromers. The system incorporates evolutionary information through multiple sequence alignment to identify physical proximity between residues. It utilizes a neural network architecture featuring spatial attention mechanisms and iterative refinement to d

    Python
    View on GitHub↗14,681
  • jwohlwend/boltzjwohlwend avatar

    jwohlwend/boltz

    4,038View on GitHub↗

    Boltz is a deep learning molecular modeler and biomolecular structure prediction system. It uses neural network architectures to simulate the physical folding and docking of biomolecules, specifically predicting the three-dimensional shapes of protein and ligand complexes. The project functions as a protein-ligand complex predictor and binding affinity predictor, estimating the strength and probability of molecular interactions between ligands and targets. These capabilities are applied to computer aided drug design, including ligand binding affinity prediction and protein-ligand interaction

    Python
    View on GitHub↗4,038
  • k-dense-ai/claude-scientific-skillsK-Dense-AI avatar

    K-Dense-AI/claude-scientific-skills

    8,907View on GitHub↗

    This project is a scientific agent framework and workflow orchestrator designed to extend large language models with specialized tools for genomic, chemical, and biological research. It provides a system for planning research hypotheses and executing automated workflows by integrating scientific databases with dynamic code execution. The framework includes a cheminformatics modeling suite for predicting molecular bioactivity and performing virtual screening, alongside a bioinformatics analysis toolkit for processing genomic sequences and single-cell data. It also features an academic document

    Pythonai-scientistbioinformaticschemoinformatics
    View on GitHub↗8,907
  • biopython/biopythonbiopython avatar

    biopython/biopython

    5,078View on GitHub↗

    Biopython is a bioinformatics library for Python providing tools to parse, manipulate, and analyze biological sequences, molecular structures, and phylogenetic trees. It serves as a biological sequence parser for genomic and proteomic data across multiple industry-standard file formats and acts as an interface for querying biological data and citations from NCBI Entrez repositories. The project distinguishes itself through specialized toolkits for protein structure analysis and phylogenetic tree construction. It includes a protein structure analyzer for processing PDB and mmCIF files to calcu

    Pythonbioinformaticsbiopythondna
    View on GitHub↗5,078
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