22 citations · 43 across the 6 of their papers we have counts for
7 papers
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…
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…
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…
Non-Negative Matrix Factorizations for Multiplex Network Analysis
Vladimir Gligorijevic, Yannis Panagakis, Stefanos Zafeiriou
Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is comm…