ESM-2 outperforms all tested single-sequence protein language models across a range of structure prediction tasks. ESMFold harnesses the ESM-2 language model to generate accurate structure …

Mar 16, 2023 · We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to …

ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed …

We developed a new protein structure prediction approach named ESMFold. ESMFold uses the representations from a large language model (ESM2) to generate an accurate structure prediction …

ESM-2 models provide embeddings for amino acids that have led to state-of-the-art performance on downstream tasks such as structure and function prediction. ESM-2 has been trained at a number of …

ESM-2 (Evolutionary Scale Modeling) is a family of transformer-based protein language models developed by Meta AI’s Fundamental AI Research (FAIR) Protein Team.

Jan 13, 2025 · In this paper, we introduce the long versions of ESM2 architectures, which can process proteins with up to 2,048 amino acids without the need for additional preprocessing.

ESM-2 outperforms all tested single-sequence protein language models across a range of structure prediction tasks, and enables atomic resolution structure prediction.

ESM C is designed as a drop-in replacement for ESM2 and comes with major performance benefits. The 300M parameter ESM C delivers similar performance to ESM2 650M with dramatically reduced …

ESM-2 650M is a protein language model trained on evolutionary-scale sequence data, enabling generalization beyond natural protein families for de novo protein sequence generation and structure …