112 citations · 552 across the 46 of their papers we have counts for
14 papers · 1 filter
Exploring Synthesizable Chemical Space with Iterative Pathway Refinements
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. Existing solutions for this problem often struggle to effec…
One-step Diffusion Models with -Divergence Distribution Matching
Yilun Xu, Weili Nie, Arash Vahdat
Sampling from diffusion models involves a slow iterative process that hinders their practical deployment, especially for interactive applications. To accelerate generation speed, r…
GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +6
Drug discovery is a complex process that involves multiple stages and tasks. However, existing molecular generative models can only tackle some of these tasks. We present Generalis…
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragme…
Multi-student Diffusion Distillation for Better One-step Generators
Yanke Song, Jonathan Lorraine, Weili Nie +2
Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student…
Truncated Consistency Models
Sangyun Lee, Yilun Xu, Tomas Geffner +4
Consistency models have recently been introduced to accelerate sampling from diffusion models by directly predicting the solution (i.e., data) of the probability flow ODE (PF ODE)…
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Shengchao Liu, Chengpeng Wang, Jiarui Lu +5
Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained…
Augmented Bridge Matching
Valentin De Bortoli, Guan-Horng Liu, Tianrong Chen +2
Flow and bridge matching are a novel class of processes which encompass diffusion models. One of the main aspect of their increased flexibility is that these models can interpolate…
A Text-guided Protein Design Framework
Shengchao Liu, Yanjing Li, Zhuoxinran Li +10
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format…
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Shengchao Liu, Weili Nie, Chengpeng Wang +6
There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize the chemical structures of molecules but…
Fast Sampling of Diffusion Models via Operator Learning
Hongkai Zheng, Weili Nie, Arash Vahdat +2
Diffusion models have found widespread adoption in various areas. However, their sampling process is slow because it requires hundreds to thousands of network evaluations to emulat…
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin +6
Diffusion models have been recently employed to improve certified robustness through the process of denoising. However, the theoretical understanding of why diffusion models are ab…
Diffusion Models for Adversarial Purification
Weili Nie, Brandon Guo, Yujia Huang +3
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model. These methods do not make assumptions on the form of a…
An Improved Semi-Supervised VAE for Learning Disentangled Representations
Weili Nie, Zichao Wang, Ankit B. Patel +1
Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning. In this work, we focus on semi-supervised disentanglement lear…