3 citations · 14 across the 18 of their papers we have counts for
20 papers
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
Multimodal Co-Generation With Continuous Bitstream Diffusion
Georgios Batzolis, Georgios Kontos, Bruno Trentini +4
We show that continuous bitstream diffusion (CoBit), originally developed for language modeling, provides a unified framework for joint and conditional multimodal generation using…
Expanding Flow Maps
Sophia Tang, Pranam Chatterjee
Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet existing parameterizations are co…
SF-Cluster: Frustration-Guided MSA Subsampling for Alternative Protein Conformation Recovery
Hanqun Cao, Zijun Gao, Chunbin Gu +3
Deep-learning structure predictors are sensitive to their multiple sequence alignment (MSA) input, making MSA subsampling a practical route to recovering alternative conformations.…
A2D2: Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding
Sophia Tang, Yuchen Zhu, Molei Tao +1
Discrete diffusion models offer a simple and stable likelihood-based framework for sequence generation, recently extended to any-length settings via token insertion. Principled rew…
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6
Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In genera…
AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
Hanqun Cao, Zachary Quinn, Aastha Pal +4
Protein binder design has largely optimized for affinity alone, leaving conformational selectivity unaddressed: for allosteric targets such as kinases, nuclear receptors, and GPCRs…
mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
Sawan Patel, Sophia Tang, Yesol Kim +8
Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regions (UTRs), and their coupling…
GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design
Jingjie Zhang, Hanqun Cao, Haosen Shi +10
Cyclic peptides are attractive therapeutic modalities because their closed-ring topology can improve stability and target specificity. However, de novo cyclic peptide design remain…
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Hanqun Cao, Aastha Pal, Sophia Tang +4
Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This…
Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
Shrey Goel, Pranam Chatterjee
Generative modeling of peptide sequences requires navigating a discrete and highly constrained space in which many intermediate states are chemically implausible or unstable. Exist…
Entangled Schrödinger Bridge Matching
Sophia Tang, Yinuo Zhang, Pranam Chatterjee
Simulating trajectories of multi-particle systems on complex energy landscapes is a central task in molecular dynamics (MD) and drug discovery, but remains challenging at scale due…
AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance
Tong Chen, Yinuo Zhang, Pranam Chatterjee
Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely…
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Sophia Tang, Yuchen Zhu, Molei Tao +1
Reinforcement learning with stochastic optimal control offers a promising framework for diffusion fine-tuning, where a pre-trained diffusion model is optimized to generate paths th…
Branched Schrödinger Bridge Matching
Sophia Tang, Yinuo Zhang, Alexander Tong +1
Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schr…
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Tong Chen, Yinuo Zhang, Sophia Tang +1
Designing biological sequences that satisfy multiple, often conflicting, functional and biophysical criteria remains a central challenge in biomolecule engineering. While discrete…
Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
Sophia Tang, Yinuo Zhang, Alexander Tong +1
Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and pr…
Path Planning for Masked Diffusion Model Sampling
Fred Zhangzhi Peng, Zachary Bezemek, Sawan Patel +5
Any order generation of discrete data using masked diffusion models (MDMs) offers a compelling alternative to traditional autoregressive models, especially in domains that lack a n…
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng +9
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences…
Token-Level Guided Discrete Diffusion for Membrane Protein Design
Shrey Goel, Peregrine M. Schray, Yinuo Zhang +3
Reparameterized diffusion models (RDMs) have recently matched autoregressive methods in protein generation, motivating their use for challenging tasks such as designing membrane pr…