# Biomedical research

ESM++ is a faithful implementation of ESMC, supporting batch processing and compatible with the standard Huggingface interface without requiring the ESM Python package. The small version corresponds to the 300-million-parameter version of ESMC.

ProGen2-xlarge is a protein generation model based on the foundational model proposed by Nijkamp et al., with modifications to its configuration and forward propagation.

ProGen2-base is a protein generation model based on the research by Nijkamp et al., supporting protein sequence generation and prediction.

ESM-2 is a next-generation protein model trained with masked language modeling objectives, suitable for fine-tuning on various downstream tasks with protein sequences as input.

ESM-2 is a state-of-the-art protein model trained on masked language modeling objectives, suitable for fine-tuning on various protein sequence input tasks.

ESM-2 is a cutting-edge protein model trained on masked language modeling objectives, suitable for various protein sequence analysis tasks

ESM-2 is a next-generation protein model trained with masked language modeling objectives, suitable for fine-tuning on various protein sequence tasks.

This model is a fine-tuned version based on Rostlab/prot_bert, designed for tasks involving the relationship between protein sequences and SMILES molecular representations.

ESM-1b is a large-scale protein language model based on the Transformer architecture, developed by Facebook AI Research (FAIR).