alphafold | Open source code for AlphaFold | Machine Learning library

 by   deepmind Python Version: 2.0.0 License: Apache-2.0

kandi X-RAY | alphafold Summary

kandi X-RAY | alphafold Summary

alphafold is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. alphafold has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install alphafold' or download it from GitHub, PyPI.

This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. For simplicity, we refer to this model as AlphaFold throughout the rest of this document. We also provide an implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update code. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper and, if applicable, the AlphaFold-Multimer paper. Please also refer to the Supplementary Information for a detailed description of the method. You can use a slightly simplified version of AlphaFold with this Colab notebook or community-supported versions (see below).

            kandi-support Support

              alphafold has a medium active ecosystem.
              It has 10416 star(s) with 1851 fork(s). There are 202 watchers for this library.
              It had no major release in the last 12 months.
              There are 131 open issues and 554 have been closed. On average issues are closed in 19 days. There are 15 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of alphafold is 2.0.0

            kandi-Quality Quality

              alphafold has 0 bugs and 0 code smells.

            kandi-Security Security

              alphafold has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              alphafold code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              alphafold is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              alphafold releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 13404 lines of code, 714 functions and 81 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed alphafold and discovered the below as its top functions. This is intended to give you an instant insight into alphafold implemented functionality, and help decide if they suit your requirements.
            • Convert atoms to Torsion angles .
            • Computes the difference between two residues .
            • Makes atom14 positions .
            • Entry point for the tool .
            • Extract template features from a PDB object .
            • Predict a structure .
            • Parse a mmCIF file .
            • Align a PDB template sequence to a query sequence .
            • Wraps a function applying shard_size .
            • Process a single hit sequence .
            Get all kandi verified functions for this library.

            alphafold Key Features

            No Key Features are available at this moment for alphafold.

            alphafold Examples and Code Snippets

            The 3DFI pipeline process in detail
            Perldot img1Lines of Code : 265dot img1License : Permissive (MIT)
            copy iconCopy
            ## Creating a working directory for 3DFI:
            export RESULTS=~/Results_3DFI
            export FSAOUT=$RESULTS/FASTA
            mkdir -p $RESULTS
            ## Running on provided examples:
               -f $TDFI_HOME/Examples/FASTA/*.fasta \
               -o $FSAOUT
            ## Creat  
            Getting started
            Perldot img2Lines of Code : 142dot img2License : Permissive (MIT)
            copy iconCopy
            sudo dnf install aria2
            LICENSE=XXXXX ## replace XXXXX by modeller license
            sudo env KEY_MODELLER=$LICENSE rpm -Uvh $MODELLER
            export CCP4=/opt/xtal/CCP4/ccp4-7.1/bin/
            export PATH=$PATH:$CCP4
            sudo dnf install ucsf-  
            COSMIS,Using the COSMIS framework,Run COSMIS
            Pythondot img3Lines of Code : 45dot img3License : Strong Copyleft (GPL-3.0)
            copy iconCopy
                "ensembl_cds": "/path/to/Homo_sapiens.GRCh38.cds.all.fa.gz",
                "uniprot_pep": "/path/to/UP000005640_9606.fasta.gz",
                "gnomad_variants": "/path/to/gnomad_filtered/gnomad_variant_counts_hg38.json",
                "uniprot_to_enst": "/path/to/uniprot_to  

            Community Discussions


            Bash repeating my directory and can not call the file
            Asked 2021-Nov-18 at 20:51

            have to use a .sh script to unpack and prep some databases. The code is the following:



            Answered 2021-Nov-18 at 20:51

            To answer your first question: why is it repeating? Because you are repeating it in your code:



            failed to alloc X bytes unified memory; result: CUDA_ERROR_OUT_OF_MEMORY: out of memory
            Asked 2021-Sep-01 at 12:43

            I am trying to run a tensorflow project and I am encountering memory problems on the university HPC cluster. I have to run a prediction job for hundreds of inputs, with differing lengths. We have GPU nodes with different amounts of vmem, so I am trying to set up the scripts in a way that will not crash in any combination of GPU node - input length.

            After searching the net for solutions, I played around with TF_FORCE_UNIFIED_MEMORY, XLA_PYTHON_CLIENT_MEM_FRACTION, XLA_PYTHON_CLIENT_PREALLOCATE, and TF_FORCE_GPU_ALLOW_GROWTH, and also with tensorflow's set_memory_growth. As I understood, with unified memory, I should be able to use more memory than a GPU has in itself.

            This was my final solution (only relevant parts)



            Answered 2021-Aug-29 at 18:26

            Probably this answer will be useful for you. This nvidia_smi python module have some useful tools like checking the gpu total memory. Here I reproduce the code of the answer I mentioned earlier.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install alphafold

            The following steps are required in order to run AlphaFold:. If you wish to run AlphaFold using Singularity (a common containerization platform on HPC systems) we recommend using some of the third party Singularity setups as linked in or
            Install Docker. Install NVIDIA Container Toolkit for GPU support. Setup running Docker as a non-root user.
            Download genetic databases (see below).
            Download model parameters (see below).
            Check that AlphaFold will be able to use a GPU by running: docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi The output of this command should show a list of your GPUs. If it doesn't, check if you followed all steps correctly when setting up the NVIDIA Container Toolkit or take a look at the following NVIDIA Docker issue.


            Colab notebooks provided by the community (please note that these notebooks may vary from our full AlphaFold system and we did not validate their accuracy):.
            Find more information at:

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          • PyPI

            pip install alphafold

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            gh repo clone deepmind/alphafold

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