133 citations · 148 across the 6 of their papers we have counts for
6 papers
SynLaD: Latent Diffusion for Generating Synthesizable Molecules Conditioned on 3D Pharmacophore Profiles
Miruna Cretu, John Bradshaw, Patricia Suriana +6
We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to m…
NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries
Ewa M. Nowara, Pedro O. Pinheiro, Sai Pooja Mahajan +4
We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scie…
Structure-based drug design by denoising voxel grids
Pedro O. Pinheiro, Arian Jamasb, Omar Mahmood +2
We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures. Our approach represents molecules as 3D atomic density grids and leverage…
3D molecule generation by denoising voxel grids
Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz +6
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from…
A COLD Approach to Generating Optimal Samples
Omar Mahmood, José Miguel Hernández-Lobato
Optimising discrete data for a desired characteristic using gradient-based methods involves projecting the data into a continuous latent space and carrying out optimisation in this…
Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang +1
A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the inter…