3 citations · 3 across the 3 of their papers we have counts for
6 papers · 1 filter
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
Zhonglin Cao, Mario Geiger, Allan dos Santos Costa +6
Fast and accurate generation of molecular conformers is desired for downstream computational chemistry and drug discovery tasks. Currently, training and sampling state-of-the-art d…
Proteina: Scaling Flow-based Protein Structure Generative Models
Tomas Geffner, Kieran Didi, Zuobai Zhang +8
Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop Proteina, a new large-scale…
BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery
Peter St. John, Dejun Lin, Polina Binder +89
Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increas…
EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini +7
Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of…
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
Ameya Daigavane, Song Kim, Mario Geiger +1
We present Symphony, an -equivariant autoregressive generative model for 3D molecular geometries that iteratively builds a molecule from molecular fragments. Existing autoreg…
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders
Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger +3
Three-dimensional native states of natural proteins display recurring and hierarchical patterns. Yet, traditional graph-based modeling of protein structures is often limited to ope…