proteins

The paper introduces Large Discovery Model (LDM), a recurrent architecture that couples generative models with Bayesian non-parametric surrogates to guide uncertainty-aware search in scientific domains like molecules and proteins, achieving significant performance gains over existing methods.

SciReasoner is a multimodal scientific foundation model that enables interpretable structural reasoning across proteins, molecules, and crystals, achieving state-of-the-art performance on 67 out of 86 benchmarks.

This paper introduces a categorical framework for distinguishing genuine scientific discovery from mere retrieval or search in self-improving AI agents, using category theory to formalize regime transitions. The authors demonstrate the framework with a protein mechanics example where an agent's accuracy drops as it tackles harder problems, but its theory compresses more data, indicating real discovery.

Proposes learning the unmasking order in masked diffusion models using a lightweight policy network, with a weighted loss that outperforms heuristics on combinatorial tasks and protein design.

Neuronal proteins from the brain drain to the dura, skull, and nose, whereas injected CSF-tracer accumulates in neck lymph nodes. The study highlights that the act of injection may perturb the system under investigation.

ESMFold2 is an open-source AI model for protein structure prediction that achieves state-of-the-art performance on protein interactions and antibodies, with a massive structure database (ESM Atlas).

A newly released AI tool has generated an atlas of over one billion predicted protein structures and sequences.