https://github.com/bioinfomachinelearning/gcpnet
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
Papers with Code 아카이브
Protein Design
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/braingpt-lovelab/backwards
https://github.com/chao1224/chatdrug
https://github.com/drorlab/gvp
Learning from Protein Structure with Geometric Vector Perceptrons
https://github.com/imoonlab/hyper-yolo
https://github.com/jacquesboitreaud/optimol
https://github.com/lyu6poshao/prollama
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
https://github.com/matsagad/mres-project
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
https://github.com/oatml-markslab/proteingym
RITA: a Study on Scaling Up Generative Protein Sequence Models
https://github.com/oatml-markslab/tranception
RITA: a Study on Scaling Up Generative Protein Sequence Models
https://github.com/open-finance-lab/finlora
https://github.com/pku-yuangroup/prollama
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
https://github.com/salesforce/jaxformer
ProGen2: Exploring the Boundaries of Protein Language Models
https://github.com/wengong-jin/refinegnn
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design
https://github.com/yangletliu/finlora
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.