Papers with Code 아카이브

Protein Design

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하위 태스크: 0
평가표: 2
보존된 평가 기록: 10

Formally, given the design requirements of users, models are required to generate protein amino acid sequences that align with those requirements.

코드

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

https://forgemia.inra.fr/marianne.defresne/emmental-pll

공식 코드

Scalable Coupling of Deep Learning with Logical Reasoning

https://github.com/A4Bio/OpenCPD

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Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement

https://github.com/A4Bio/PiFold

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PiFold: Toward effective and efficient protein inverse folding

https://github.com/A4Bio/RDesign

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

https://github.com/AngxiaoYue/ReQFlow

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

https://github.com/Gleghorn-Lab/DSM

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Diffusion Sequence Models for Enhanced Protein Representation and Generation

https://github.com/IBM/fold2seq

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Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design

https://github.com/Mohammad-Khatami/grover-protein-desing

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Gate-based Quantum Computing for Protein Design

https://github.com/NVIDIA-Digital-Bio/proteina

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Proteina: Scaling Flow-based Protein Structure Generative Models

https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling

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A Text-guided Protein Design Framework

https://github.com/SuperSecretBioTech/evolutionary_monte_carlo_search

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Improving few-shot learning-based protein engineering with evolutionary sampling

https://github.com/ZCJ1111/BBO-sequence-design

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Protein Sequence Design with Batch Bayesian Optimisation

https://github.com/aayush-shah14/peptidegpt

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Peptide-GPT: Generative Design of Peptides using Generative Pre-trained Transformers and Bio-informatic Supervision

https://github.com/adaptive-intelligent-robotics/MOUR-QD

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Multi-Objective Quality-Diversity in Unstructured and Unbounded Spaces

https://github.com/aqlaboratory/genie

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

https://github.com/aqlaboratory/genie2

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Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2

https://github.com/aqlaboratory/openfold

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OpenProteinSet: Training data for structural biology at scale

https://github.com/bioinfomachinelearning/bio-diffusion

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Geometry-Complete Diffusion for 3D Molecule Generation and Optimization

https://github.com/bioinfomachinelearning/gcdm-sbdd

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Geometry-Complete Diffusion for 3D Molecule Generation and Optimization

https://github.com/bioinfomachinelearning/psbench

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

https://github.com/bitbol-lab/iterative_masking

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Generative power of a protein language model trained on multiple sequence alignments

https://github.com/blt2114/MotifBench

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

https://github.com/blt2114/twisted_diffusion_sampler

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Practical and Asymptotically Exact Conditional Sampling in Diffusion Models

https://github.com/chao1224/proteindt

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A Text-guided Protein Design Framework

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

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Generative De Novo Protein Design with Global Context

https://github.com/chenyuwang-monica/drakes

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Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design

https://github.com/divnori/rnaflow

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RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching

https://github.com/drorlab/gvp-pytorch

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Learning from Protein Structure with Geometric Vector Perceptrons

https://github.com/ecburx/proteval

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Building Confidence in Deep Generative Protein Design

https://github.com/emmijokinen/mgpfusion

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mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion

https://github.com/ericlbuehler/xlora

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X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Molecular Design

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

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Energy-based models for atomic-resolution protein conformations

https://github.com/flagshippioneering/pi-rldif

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Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding

https://github.com/gaozhangyang/AlphaDesign

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AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB

https://github.com/gfnorg/adversarialflownetworks

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Expected flow networks in stochastic environments and two-player zero-sum games

https://github.com/hanjq17/geotdm

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Geometric Trajectory Diffusion Models

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