Papers with Code 아카이브

Protein Structure Prediction

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하위 태스크: 2
평가표: 4
보존된 평가 기록: 20

Image credit: FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/3dem/model-angelo

공식 코드

A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps

https://github.com/A4Bio/RDesign

공식 코드

RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

https://github.com/Bhattacharya-Lab/PolyFold

공식 코드

PolyFold: an interactive visual simulator for distance-based protein folding

https://github.com/BioinfoMachineLearning/DIPS-Plus

공식 코드

DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction

https://github.com/CVC-Lab/SAC-for-H-Bond-Learning

공식 코드

Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach

https://github.com/FabianFuchsML/se3-transformer-public

공식 코드

Iterative SE(3)-Transformers

https://github.com/JinLi711/3DProteinPrediction

공식 코드

Universal Transforming Geometric Network

https://github.com/KennthShang/MTDP

공식 코드

Accurate and efficient protein embedding using multi-teacher distillation learning

https://github.com/PaddlePaddle/PaddleHelix

공식 코드

HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle

https://github.com/aangelopoulos/rcps

공식 코드

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/agemagician/Ankh

공식 코드

Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

https://github.com/alexduvalinho/geometric-gnns

공식 코드

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

https://github.com/anirudhgha/AMH

공식 코드

An Efficient MCMC Approach to Energy Function Optimization in Protein Structure Prediction

https://github.com/anton-bushuiev/ProteinTTT

공식 코드

Training on test proteins improves fitness, structure, and function prediction

https://github.com/aqlaboratory/genie

공식 코드

Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds

https://github.com/aqlaboratory/openfold

공식 코드

OpenProteinSet: Training data for structural biology at scale

https://github.com/bioinfomachinelearning/gcpnet

공식 코드

Geometry-Complete Perceptron Networks for 3D Molecular Graphs

https://github.com/bioinfomachinelearning/psbench

공식 코드

PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models

https://github.com/bjing2016/eigenfold

공식 코드

EigenFold: Generative Protein Structure Prediction with Diffusion Models

https://github.com/blt2114/MotifBench

공식 코드

MotifBench: A standardized protein design benchmark for motif-scaffolding problems

https://github.com/bozhenhhu/a-review-of-plms-and-methods-for-protein-structure-prediction

공식 코드

Protein Language Models and Structure Prediction: Connection and Progression

https://github.com/chengtan9907/gca-generative-protein-design

공식 코드

Generative De Novo Protein Design with Global Context

https://github.com/colbyford/pymolfold

공식 코드

PyMOLfold: Interactive Protein and Ligand Structure Prediction in PyMOL

https://github.com/compsoftmatterbiophysics-cityu-hk/applying-drl-to-hp-model-for-protein-structure-prediction

공식 코드

Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction

https://github.com/deepgraphlearning/diffpack

공식 코드

DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

https://github.com/deepgraphlearning/peer_benchmark

공식 코드

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

https://github.com/drorlab/DIPS

공식 코드

End-to-End Learning on 3D Protein Structure for Interface Prediction

https://github.com/facebookresearch/protein-ebm

공식 코드

Energy-based models for atomic-resolution protein conformations

https://github.com/genentech/cremp

공식 코드

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning

https://github.com/gouwenrui/cpe-pro-main

공식 코드

CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin Evaluation

https://github.com/hpcaitech/fastfold

공식 코드

FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

https://github.com/hyunp2/alphafold

공식 코드

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

https://github.com/idrori/cu-tsp

공식 코드

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

https://github.com/jasonkyuyim/se3_diffusion

공식 코드

SE(3) diffusion model with application to protein backbone generation

https://github.com/jiaqingxie/deepprotein

공식 코드

DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

https://github.com/johnyang101/reticular-sae

공식 코드

Towards Interpretable Protein Structure Prediction with Sparse Autoencoders

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