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.
Watson, David Juergens, Nathaniel R
9 Pith papers cite this work, alongside 1,961 external citations. Polarity classification is still indexing.
9
Pith papers citing it
1,961
external citations · OpenAlex
representative 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.
Finite-particle approximation of the Doob h-function causes reward hacking via two failure modes in reward-guided diffusion; a damping schedule corrects within-mode bias in Gaussian settings.
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.
A ~3.3M-parameter dense PairMixer inverse folder trails LigandMPNN on interface recovery while propagating ligand signal to distal residues where local graphs lose it.
UCD adjusts diffusion-based 3D molecular graph generation to handle epistemic uncertainty, improving sample quality and reaching new benchmark performance.
citing papers explorer
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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.
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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.
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Are we really tilting? The mechanics of reward guidance in flow and diffusion models
Finite-particle approximation of the Doob h-function causes reward hacking via two failure modes in reward-guided diffusion; a damping schedule corrects within-mode bias in Gaussian settings.
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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.
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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%.
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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.
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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.
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UMA-Inverse: Ligand-Conditioned Protein Inverse Folding with a Distogram-Supervised Dense Pair Encoder
A ~3.3M-parameter dense PairMixer inverse folder trails LigandMPNN on interface recovery while propagating ligand signal to distal residues where local graphs lose it.
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Uncertainty-Calibrated Diffusion for Reliable 3D Molecular Graph Generation
UCD adjusts diffusion-based 3D molecular graph generation to handle epistemic uncertainty, improving sample quality and reaching new benchmark performance.