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ForceGen: End-to-end de novo protein generation based on nonlinear
  mechanical unfolding responses using a protein language diffusion model

ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model

16 October 2023
Bo Ni
David L. Kaplan
Markus J. Buehler
    DiffM
ArXivPDFHTML

Papers citing "ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a protein language diffusion model"

5 / 5 papers shown
Title
Sifting through the Noise: A Survey of Diffusion Probabilistic Models
  and Their Applications to Biomolecules
Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules
Trevor Norton
Debswapna Bhattacharya
MedIm
DiffM
50
2
0
31 May 2024
ProtAgents: Protein discovery via large language model multi-agent
  collaborations combining physics and machine learning
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Alireza Ghafarollahi
Markus J. Buehler
LLMAG
AI4CE
13
23
0
27 Jan 2024
MechAgents: Large language model multi-agent collaborations can solve
  mechanics problems, generate new data, and integrate knowledge
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Bo Ni
Markus J. Buehler
AI4CE
LLMAG
15
39
0
14 Nov 2023
Diffusion-LM Improves Controllable Text Generation
Diffusion-LM Improves Controllable Text Generation
Xiang Lisa Li
John Thickstun
Ishaan Gulrajani
Percy Liang
Tatsunori B. Hashimoto
AI4CE
173
777
0
27 May 2022
Protein Structure and Sequence Generation with Equivariant Denoising
  Diffusion Probabilistic Models
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models
N. Anand
Tudor Achim
DiffM
187
173
0
26 May 2022
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