2 citations · 2 across the 4 of their papers we have counts for
5 papers
Pharmacophore-based design by learning on voxel grids
Omar Mahmood, Pedro O. Pinheiro, Richard Bonneau +2
Ligand-based drug discovery (LBDD) relies on making use of known binders to a protein target to find structurally diverse molecules similarly likely to bind. This process typically…
Unified all-atom molecule generation with neural fields
Matthieu Kirchmeyer, Pedro O. Pinheiro, Emma Willett +7
Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBin…
Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignment
Ameya Daigavane, YuQing Xie, Bodhi P. Vani +3
Diffusion models are a popular class of generative models trained to reverse a noising process starting from a target data distribution. Training a diffusion model consists of lear…
Sampling Binary Data by Denoising through Score Functions
Francis Bach, Saeed Saremi
Gaussian smoothing combined with a probabilistic framework for denoising via the empirical Bayes formalism, i.e., the Tweedie-Miyasawa formula (TMF), are the two key ingredients in…
Score-based 3D molecule generation with neural fields
Matthieu Kirchmeyer, Pedro O. Pinheiro, Saeed Saremi
We introduce a new representation for 3D molecules based on their continuous atomic density fields. Using this representation, we propose a new model based on walk-jump sampling fo…