Papers with Code 아카이브
Protein Design
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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://github.com/ibm/afdistill
공식 코드https://github.com/instadeepai/metalic
공식 코드Metalic: Meta-Learning In-Context with Protein Language Models
https://github.com/instadeepai/protein-structure-tokenizer
공식 코드https://github.com/johnyang101/pmpnndiff
공식 코드Fast non-autoregressive inverse folding with discrete diffusion
https://github.com/ketatam/diffdock-pp
공식 코드DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
https://github.com/lamm-mit/protagents
공식 코드https://github.com/leojklarner/context-guided-diffusion
공식 코드Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design
https://github.com/lightonai/rita
공식 코드RITA: a Study on Scaling Up Generative Protein Sequence Models
https://github.com/linzy19/taxdiff
공식 코드TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
https://github.com/mgreenig/loopgen
공식 코드Score-Based Generative Models for Designing Binding Peptide Backbones
https://github.com/michael-s-yao/gabo
공식 코드Generative Adversarial Model-Based Optimization via Source Critic Regularization
https://github.com/michaelchen-lab/caft-llm
공식 코드Improving large language models with concept-aware fine-tuning
https://github.com/microsoft/frame-flow
공식 코드https://github.com/microsoft/protein-frame-flow
공식 코드https://github.com/mindspore-ai/mindscience/tree/master/MindSPONGE/applications/MEGAProtein
공식 코드https://github.com/ngruver/nos
공식 코드https://github.com/nju-websoft/CtrlProt
공식 코드Controllable Protein Sequence Generation with LLM Preference Optimization
https://github.com/oxcsml/constrained-diffusion
공식 코드https://github.com/prescient-design/lobster
공식 코드https://github.com/sabagh1994/pgvae
공식 코드Robust Model-Based Optimization for Challenging Fitness Landscapes
https://github.com/salesforce/progen
공식 코드ProGen2: Exploring the Boundaries of Protein Language Models
https://github.com/samsinai/VAE_protein_function
공식 코드https://github.com/violet-sto/bridge-if
공식 코드Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
https://github.com/wang-cr/pdb-struct
공식 코드PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
https://github.com/weixiaoqimath/persistent_sheaf_Laplacians
공식 코드https://github.com/yinjunbo/cfpgen
공식 코드CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
https://github.com/zacharyhorvitz/fk-diffusion-steering
공식 코드A General Framework for Inference-time Scaling and Steering of Diffusion Models
https://github.com/zjunlp/mol-instructions
공식 코드Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
https://github.com/2023-MindSpore-1/ms-code-141
https://github.com/COMP6248-Reproducability-Challenge/Geometric-Vector-Perceptron
Learning from Protein Structure with Geometric Vector Perceptrons
https://github.com/Leeroo-AI/mergoo
https://github.com/MindCode-4/code-13/tree/main/RITA
RITA: a Study on Scaling Up Generative Protein Sequence Models
https://github.com/ahaldane/MSA_VAE
https://github.com/aim-uofa/vfn
De novo protein design using geometric vector field networks
https://github.com/binbinjiang/cvt-slr
Progressive Multi-Modality Learning for Inverse Protein Folding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.