Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
verdicts
UNVERDICTED 6representative citing papers
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
DiLaDiff augments masked diffusion LMs with latent space modeling and consistency distillation to improve token correlation capture and inference speed.
Feynman-Kac steering of RFdiffusion with ProteinMPNN-based guiding potentials improves predicted interface energetics and raises binder designability by 89.5%.
O3 uses surrogate latent spaces extracted from generative models to perform sample-efficient black-box optimization over their outputs, outperforming direct sampling and original-latent optimization on image and protein tasks.
DiffuMeta uses diffusion transformers and algebraic language representations to generate diverse 3D shell metamaterials with targeted stress-strain responses under large deformations including buckling and contact.
citing papers explorer
-
Generative Pseudo-Force Fields for Molecular Generation
Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
-
RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
-
DiLaDiff: Distilled Latent-Augmented Diffusion for Language Modeling
DiLaDiff augments masked diffusion LMs with latent space modeling and consistency distillation to improve token correlation capture and inference speed.
-
Controllable protein design with particle-based Feynman-Kac steering
Feynman-Kac steering of RFdiffusion with ProteinMPNN-based guiding potentials improves predicted interface energetics and raises binder designability by 89.5%.
-
Sample-Efficient Optimisation over the Outputs of Generative Models
O3 uses surrogate latent spaces extracted from generative models to perform sample-efficient black-box optimization over their outputs, outperforming direct sampling and original-latent optimization on image and protein tasks.
-
Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers
DiffuMeta uses diffusion transformers and algebraic language representations to generate diverse 3D shell metamaterials with targeted stress-strain responses under large deformations including buckling and contact.