2.6k citations · 3.6k across the 16 of their papers we have counts for
16 papers
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…
Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT
Andrew T. McNutt, Abhinav K. Adduri, Caleb N. Ellington +4
Virtual screening of small molecules against protein targets can accelerate drug discovery and development by predicting drug-target interactions (DTIs). However, structure-based m…
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…
PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking
Michael Brocidiacono, Konstantin I. Popov, David Ryan Koes +1
Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scori…