Papers with Code 아카이브

Protein Folding

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

https://github.com/AngxiaoYue/ReQFlow

공식 코드

ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation

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

공식 코드

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

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/bonaventuredossou/ece526_course_project

공식 코드

A Study of Acquisition Functions for Medical Imaging Deep Active Learning

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/brookehus/sCSC

공식 코드

Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity

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/eagalpern/exon-foldon

공식 코드

Reassessing the Exon-Foldon correspondence using Frustration Analysis

https://github.com/gaozhangyang/AlphaDesign

공식 코드

AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB

https://github.com/hincz-lab/ml-nonequilibrium

공식 코드

Machine learning in and out of equilibrium

https://github.com/hphuongdhsp/Q-Newton-method

공식 코드

A fast and simple modification of Newton's method helping to avoid saddle points

https://github.com/ibm/afdistill

공식 코드

AlphaFold Distillation for Protein Design

https://github.com/kakaobrain/solvent

공식 코드

Solvent: A Framework for Protein Folding

https://github.com/learningmatter-mit/geodesic-interpolation-cv

공식 코드

Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

https://github.com/lupoglaz/TorchProteinLibrary

공식 코드

TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure

https://github.com/magiccircuit/msa-augmentor

공식 코드

Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation

https://github.com/mkhodak/relax

공식 코드

Rethinking Neural Operations for Diverse Tasks

https://github.com/msmbuilder/vde

공식 코드

Variational Encoding of Complex Dynamics

https://github.com/mvandermerwe/BP-GPU-Message-Scheduling

공식 코드

Message Scheduling for Performant, Many-Core Belief Propagation

https://github.com/nick11roberts/XD

공식 코드

Rethinking Neural Operations for Diverse Tasks

https://github.com/peizhenbai/MapDiff

공식 코드

Mask prior-guided denoising diffusion improves inverse protein folding

https://github.com/phermosilla/IEConv_proteins

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Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures

https://github.com/plainerman/variational-doob

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Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

https://github.com/ppxasjsm/Geometric-constraints-protein-folding

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Geometric constraints in protein folding

https://github.com/quanrj/clusteringprl

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Clustering for Protein Representation Learning

https://github.com/roberCO/QFold

공식 코드

QFold: Quantum Walks and Deep Learning to Solve Protein Folding

https://github.com/sjtu-hpc/parafold

공식 코드

ParaFold: Paralleling AlphaFold for Large-Scale Predictions

https://github.com/sjunhongshen/dash

공식 코드

Efficient Architecture Search for Diverse Tasks

https://github.com/supercomputing-system-ai-lab/megafold

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MegaFold: System-Level Optimizations for Accelerating Protein Structure Prediction Models

https://github.com/tencent-ailab/DrugOOD

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DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations

https://github.com/tijana-zrnic/pac-labels

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Probably Approximately Correct Labels

https://github.com/tongjiyiming/TGGAN

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TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models

https://github.com/torchmd/torchmd

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TorchMD: A deep learning framework for molecular simulations

https://github.com/tsynbio/TourSynbio

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TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering

https://github.com/vanbanTruong/D2G2

공식 코드

Disentangled Dynamic Graph Deep Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.