2 citations · 3 across the 14 of their papers we have counts for
5 papers · 2 filters
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Giangiacomo Mercatali, Yogesh Verma, Andre Freitas +1
We introduce a novel score-based diffusion framework named Twigs that incorporates multiple co-evolving flows for enriching conditional generation tasks. Specifically, a central or…
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?
Rafał Karczewski, Samuel Kaski, Markus Heinonen +1
Several generative models with elaborate training and sampling procedures have been proposed to accelerate structure-based drug design (SBDD); however, their empirical performance…
Topological Neural Networks go Persistent, Equivariant, and Continuous
Yogesh Verma, Amauri H Souza, Vikas Garg
Topological Neural Networks (TNNs) incorporate higher-order relational information beyond pairwise interactions, enabling richer representations than Graph Neural Networks (GNNs).…
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
Najwa Laabid, Severi Rissanen, Markus Heinonen +2
Graph diffusion models, dominant in graph generative modeling, remain underexplored for graph-to-graph translation tasks like chemical reaction prediction. We demonstrate that stan…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…