From the 1 of 6 linked papers with an AI index.
6 papers
A fast summation method for the DFT-D3 dispersion correction
Victoria Valeeva, Cheuk Hin Ho, Mario Geiger +4
The paper introduces FourierD3, a low‑rank decomposition technique that restores separability in the DFT‑D3 dispersion correction, enabling fast particle‑mesh evaluation in O(N log…
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…
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…
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
Jesun Firoz, Franco Pellegrini, Mario Geiger +17
Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists…
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…
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…