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Vladimir Gligorijevic

5 accepted papers

2025

Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks

Angelica Chen, Samuel Don Stanton, Frances Ding, Robert G Alberstein, Andrew Martin Watkins, Richard Bonneau +3
ICML 2025poster

Although large language models (LLMs) have shown promise in biomolecule optimization problems, they incur heavy computational costs and struggle to satisfy precise constraints. On the other hand, specialized solvers like LaMBO-2 offer efficiency and fine-grained control but require more domain exper…

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2025

Unified all-atom molecule generation with neural fields

Matthieu Kirchmeyer, Pedro O. Pinheiro, Emma Willett, Karolis Martinkus, Joseph Kleinhenz, Emily K. Makowski +4
NeurIPS 2025poster

Generative models for structure-based drug design are often limited to a specific modality, restricting their broader applicability. To address this challenge, we introduce FuncBind, a framework based on computer vision to generate target-conditioned, all-atom molecules across atomic systems. FuncBi…

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2024

Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient

Natasa Tagasovska, Vladimir Gligorijevic, Kyunghyun Cho, Andreas Loukas
NeurIPS 2024poster

Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design use a generative model and a surrogate model (discriminator), requiring large datasets. However, real-world scientific ap…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Protein Discovery with Discrete Walk-Jump Sampling

Nathan C. Frey, Dan Berenberg, Karina Zadorozhny, Joseph Kleinhenz, Julien Lafrance-Vanasse, Isidro Hotzel +7
ICLR 2024oral

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 Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising. Our $\textit{Discre…

Cited by 42Source ↗PDF ↗code ↗Scholar ↗
2023

AbDiffuser: full-atom generation of in-vitro functioning antibodies

Karolis Martinkus, Jan Ludwiczak, WEI-CHING LIANG, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal +5
NeurIPS 2023spotlight

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 representation of protein structure, relies on a novel architecture for aligned proteins, and utilizes strong diffusion p…

Cited by 52Source ↗PDF ↗Scholar ↗