https://github.com/AlgoMole/GeoBFN
공식 코드Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Papers with Code 아카이브
3D Molecule Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AngxiaoYue/ReQFlow
공식 코드ReQFlow: Rectified Quaternion Flow for Efficient and High-Quality Protein Backbone Generation
https://github.com/LEOXC1571/GFMDiff
공식 코드Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation
https://github.com/acharkq/next-mol
공식 코드NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation
https://github.com/atomicarchitects/symphony
공식 코드Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
https://github.com/bioinfomachinelearning/bio-diffusion
공식 코드Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/bioinfomachinelearning/gcdm-sbdd
공식 코드Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/dunni3/flowmol
공식 코드Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
https://github.com/fengshikun/SLDM
공식 코드Straight-Line Diffusion Model for Efficient 3D Molecular Generation
https://github.com/franco-solis/graphvf-code
공식 코드GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
https://github.com/genentech/voxmol
공식 코드https://github.com/gracezhao1997/EEGSDE
공식 코드https://github.com/graph-0/jodo
공식 코드Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
https://github.com/harryjo97/drum
공식 코드https://github.com/harryjo97/grum
공식 코드https://github.com/isayevlab/geom-drugs-3dgen-evaluation
공식 코드GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation
https://github.com/khodabandeh-ali/bokdiff
공식 코드BoKDiff: Best-of-K Diffusion Alignment for Target-Specific 3D Molecule Generation
https://github.com/lee-jwon/bind
공식 코드https://github.com/minkaixu/geoldm
공식 코드Geometric Latent Diffusion Models for 3D Molecule Generation
https://github.com/pengxingang/moldiff
공식 코드MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
https://github.com/physilearn/d3mes
공식 코드D3MES: Diffusion Transformer with multihead equivariant self-attention for 3D molecule generation
https://github.com/prescient-design/funcmol
공식 코드https://github.com/skeletondyh/radm
공식 코드Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment
https://github.com/zaixizhang/flag
공식 코드Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
https://github.com/ML-GSAI/EGSDE
https://github.com/bioinfomachinelearning/gcpnet
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/graph-0/cdgs
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
https://github.com/olsdavis/fisher-flow
Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
https://github.com/rlacombe/accgeoldm
Geometric Latent Diffusion Models for 3D Molecule Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.