3 citations · 9 across the 6 of their papers we have counts for
6 papers
OMTRA: A Multi-Task Generative Model for Structure-Based Drug Design
Ian Dunn, Liv Toft, Tyler Katz +4
Structure-based drug design (SBDD) focuses on designing small-molecule ligands that bind to specific protein pockets. Computational methods are integral in modern SBDD workflows an…
FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation
Ian Dunn, David R. Koes
A generative model capable of sampling realistic molecules with desired properties could accelerate chemical discovery across a wide range of applications. Toward this goal, signif…
GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generation
Filipp Nikitin, Ian Dunn, David Ryan Koes +1
Deep generative models have shown significant promise in generating valid 3D molecular structures, with the GEOM-Drugs dataset serving as a key benchmark. However, current evaluati…
Exploring Discrete Flow Matching for 3D De Novo Molecule Generation
Ian Dunn, David R. Koes
Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Flow matching is a recently proposed generative modeling framewo…
Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
Ian Dunn, David Ryan Koes
Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Diffusion models currently achieve state of the art performance…
Accelerating Inference in Molecular Diffusion Models with Latent Representations of Protein Structure
Ian Dunn, David Ryan Koes
Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D…