457 citations · 514 across the 10 of their papers we have counts for
11 papers
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…
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
Frédéric A. Dreyer, Jan Ludwiczak, Karolis Martinkus +8
We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell re…
Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
Angelica Chen, Samuel D. Stanton, Frances Ding +6
Although large language models (LLMs) have shown promise in biomolecule optimization problems, they incur heavy computational costs and struggle to satisfy precise constraints. On…
OpenProteinSet: Training data for structural biology at scale
Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan +7
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein struc…
AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies
Karolis Martinkus, Jan Ludwiczak, Kyunghyun Cho +8
We introduce AbDiffuser, an equivariant and physics-informed diffusion model for the joint generation of antibody 3D structures and sequences. AbDiffuser is built on top of a new r…
Generalization within in silico screening
Andreas Loukas, Pan Kessel, Vladimir Gligorijevic +1
In silico screening uses predictive models to select a batch of compounds with favorable properties from a library for experimental validation. Unlike conventional learning paradig…
Protein Discovery with Discrete Walk-Jump Sampling
Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny +10
We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Mar…
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Nataša Tagasovska, Nathan C. Frey, Andreas Loukas +9
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences. However, these problems often require sampling ne…
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Ji Won Park, Samuel Stanton, Saeed Saremi +6
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences. Whereas it is poss…
Multi-segment preserving sampling for deep manifold sampler
Daniel Berenberg, Jae Hyeon Lee, Simon Kelow +6
Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility.…
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark +144
Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations i…