101 citations · 216 across the 10 of their papers we have counts for
10 papers
Topotein: Topological Deep Learning for Protein Representation Learning
Zhiyu Wang, Arian Jamasb, Mustafa Hajij +3
Protein representation learning (PRL) is crucial for understanding structure-function relationships, yet current sequence- and graph-based methods fail to capture the hierarchical…
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design
Nataša Tagasovska, Ji Won Park, Matthieu Kirchmeyer +8
Machine learning (ML) has demonstrated significant promise in accelerating drug design. Active ML-guided optimization of therapeutic molecules typically relies on a surrogate model…
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…
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead +7
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for dat…
Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
Charles Harris, Kieran Didi, Arian R. Jamasb +4
Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years.…
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Chaitanya K. Joshi, Arian R. Jamasb, Ramon Viñas +5
Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D confo…
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss +24
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantag…
Structure-based Drug Design with Equivariant Diffusion Models
Arne Schneuing, Charles Harris, Yuanqi Du +10
Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Generative SBDD methods lev…
Protein Representation Learning by Geometric Structure Pretraining
Zuobai Zhang, Minghao Xu, Arian Jamasb +4
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain prote…
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes
Ryan-Rhys Griffiths, Jake L. Greenfield, Aditya R. Thawani +7
Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively add…