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deepmind avatar

deepmind/alphafold

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Alphafold

AlphaFold is a deep learning biology framework and machine learning pipeline designed to predict the three-dimensional coordinates of proteins based on their amino acid sequences. It functions as a bioinformatics inference system for calculating protein folding patterns and estimating prediction confidence.

The system includes a protein multimer predictor for determining the 3D structures of protein complexes, supporting both homomers and heteromers. It utilizes specialized model presets to handle these complex structural predictions.

The framework covers biological database management for multiple sequence alignment and template searching, as well as structural confidence estimation to generate per-residue error metrics. It also provides capabilities for local geometry refinement to improve the accuracy of predicted structural coordinates.

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Features

  • Protein Structure Prediction - Uses deep learning to infer the 3D atomic coordinates of proteins from amino acid sequences.
  • Protein Representation Networks - Employs a specialized transformer network to iteratively refine protein sequence and pair representations.
  • Protein Folding Inference - Provides an inference pipeline for applying trained models to determine 3D protein structures.
  • Evolutionary Sequence Integration - Integrates multiple sequence alignments to extract co-evolutionary signals as primary input for structural prediction.
  • Deep Learning Biology Frameworks - Applies deep neural networks to solve core problems in protein folding and structural biology.
  • Protein Complex Prediction - Predicts the 3D arrangement of multiple interacting protein chains for both homomers and heteromers.
  • End-to-End Architectures - Utilizes a neural network architecture that maps amino acid sequences directly to 3D atomic positions.
  • Protein Residue Reliability Scoring - Predicts per-residue confidence scores to quantify the reliability of the resulting 3D protein structure.
  • Iterative Prediction Refiners - Passes predicted structures and representations back through the network multiple times to refine geometry.
  • Structural Confidence Analysis - Generates per-residue error metrics and confidence scores to assess the reliability of structural predictions.
  • Biology and Bioinformatics - Implements computational tools for managing genetic sequence data within a bioinformatics pipeline.
  • Genetic Database Management - Provides tools for downloading and configuring the large-scale genetic sequence databases required for alignment.
  • Molecular Dynamics Force Fields - Implements structural relaxation of predicted coordinates using a molecular dynamics force field.
  • Protein Reliability Analysis - Assesses the accuracy and confidence of predicted protein structural models using per-residue scoring.
  • Protein Structural Refinement - Applies computational procedures to optimize protein models by correcting stereochemical errors and clashes.
  • Computational Biology - System for predicting protein structures with high accuracy.
  • Neural Network Frameworks - Protein structure prediction inference pipeline.
14,687 stars·2,628 forks·Python·Apache-2.0·20 views

Star history

Star history chart for deepmind/alphafoldStar history chart for deepmind/alphafold

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 deepmind/alphafold do?

AlphaFold is a deep learning biology framework and machine learning pipeline designed to predict the three-dimensional coordinates of proteins based on their amino acid sequences. It functions as a bioinformatics inference system for calculating protein folding patterns and estimating prediction confidence.

What are the main features of deepmind/alphafold?

The main features of deepmind/alphafold are: Protein Structure Prediction, Protein Representation Networks, Protein Folding Inference, Evolutionary Sequence Integration, Deep Learning Biology Frameworks, Protein Complex Prediction, End-to-End Architectures, Protein Residue Reliability Scoring.

Which projects share features with deepmind/alphafold?

Projects with overlapping indexed features include: google-deepmind/alphafold — AlphaFold is a deep learning biology tool and structural bioinformatic pipeline designed to predict the… biopython/biopython — Biopython is a bioinformatics library for Python providing tools to parse, manipulate, and analyze biological… deepmind/deepmind-research — This project is an AI research implementation library and machine learning research repository. It provides a… facebookresearch/esm — This project is a collection of pretrained transformer protein language models designed for sequence embedding,… glouppe/info8010-deep-learning — This project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the… qwenlm/qwen3-omni — Qwen3-Omni is an omni-modal large language model designed to process and generate text, audio, images, and video…

Projects sharing features with Alphafold

These projects share indexed features with Alphafold. 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
  • 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
  • deepmind/deepmind-researchdeepmind avatar

    deepmind/deepmind-research

    15,024View on GitHub↗

    This project is an AI research implementation library and machine learning research repository. It provides a collection of reference code, illustrative implementations, and open-source research datasets used to verify hypotheses and build upon existing models in artificial intelligence. The repository focuses on scientific research reproduction by translating theoretical findings from published papers into executable code. It includes specialized scientific simulation environments designed to test the behavior of autonomous agents and models within controlled settings. The project covers AI

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  • facebookresearch/esmfacebookresearch avatar

    facebookresearch/esm

    4,138View on GitHub↗

    This project is a collection of pretrained transformer protein language models designed for sequence embedding, structure prediction, variant effect estimation, and generative inverse folding. It provides a framework for transforming amino acid sequences into high-dimensional numerical vectors and predicting biological properties through these embeddings. The system includes a generative model for inverse folding that designs amino acid sequences to fit a specific target protein backbone structure. It also features tools for protein variant analysis that use zero-shot prediction to estimate t

    Python
    View on GitHub↗4,138
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