9 papers
Few-step Cofolding with All-Atom Flow Maps
Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7
All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating struc…
Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps
Peter Holderrieth, Douglas Chen, Luca Eyring +7
Flow and diffusion models produce high-quality samples, but adapting them to user preferences or constraints post-training remains costly and brittle, a challenge commonly called r…
Discrete Flow Maps
Peter Potaptchik, Jason Yim, Adhi Saravanan +3
The sequential nature of autoregressive next-token prediction imposes a fundamental speed limit on large language models. While continuous flow models offer a path to parallel gene…
FragmentFlow: Scalable Transition State Generation for Large Molecules
Ron Shprints, Peter Holderrieth, Juno Nam +2
Transition states (TSs) are central to understanding and quantitatively predicting chemical reactivity and reaction mechanisms. Although traditional TS generation methods are compu…
GLASS Flows: Transition Sampling for Alignment of Flow and Diffusion Models
Peter Holderrieth, Uriel Singer, Tommi Jaakkola +3
The performance of flow matching and diffusion models can be greatly improved at inference time using reward alignment algorithms, yet efficiency remains a major limitation. While…
LEAPS: A discrete neural sampler via locally equivariant networks
Peter Holderrieth, Michael S. Albergo, Tommi Jaakkola
We propose "LEAPS", an algorithm to sample from discrete distributions known up to normalization by learning a rate matrix of a continuous-time Markov chain (CTMC). LEAPS can be se…