3 citations · 14 across the 18 of their papers we have counts for
11 papers · 1 filter
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…